Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Computational profiling of the gut–brain axis: microflora dysbiosis insights to neurological disorders

Computational profiling of the gut–brain axis: microflora dysbiosis insights to neurological... Abstract Almost 2500 years after Hippocrates’ observations on health and its direct association to the gastrointestinal tract, a paradigm shift has recently occurred, making the gut and its symbionts (bacteria, fungi, archaea and viruses) a point of convergence for studies. It is nowadays well established that the gut microflora’s compositional diversity regulates via its genes (the microbiome) the host’s health and provides preliminary insights into disease progression and regulation. The microbiome’s involvement is evident in immunological and physiological studies that link changes in its biodiversity to its contributions to the host’s phenotype but also in neurological investigations, substantiating the aptly named gut–brain axis. The definitive mechanisms of this last bidirectional interaction will be our main focus because it presents researchers with a new conundrum. In this review, we prospect current literature for computational analysis methodologies that accommodate the need for better understanding of the microbiome–gut–brain interactions and neurological disorder onset and progression, through cross-disciplinary systems biology applications. We will present bioinformatics tools used in exploring these synergies that help build and interpret microbial 16S ribosomal RNA data sets, produced by shotgun and high-throughput sequencing of healthy and neurological disorder samples stored in biological databases. These approaches provide alternative means for researchers to form hypotheses to their inquests faster, cheaper and swith precision. The goal of these studies relies on the integration of combined metagenomics and metabolomics assessments. An accurate characterization of the microbiome and its functionality can support new diagnostic, prognostic and therapeutic strategies for neurological disorders, customized for each individual host. gut–brain axis, microflora, microbiome, neurological disorders, precision medicine, computational metagenomics The host and its microflora: an interesting symbiosis The philosophical expression ‘no man is an island’ takes a whole new meaning, when one considers the fact that from the time of birth, each of us coexists with an assortment of bacteria, fungi, archaea and viruses. These ∼1014 microorganisms constitute the human microflora [1] (also known as microbiota) colonizing the skin, mouth, lungs, reproductive and gastrointestinal (GI) tract of everyone, creating a mutualistic biological interaction, a symbiosis. Especially the gut, with its physiology and large surface, acts as the perfect host environment for the microflora’s development, exhibiting the greatest diversity and abundance of bacterial populations. The composition of the human microflora, although evolving through the early stages of life and being perturbed by habitat, lifestyle, medication and health, is unique in each individual, creating a form of personal ‘fingerprint’ [2]. This evolution includes interactions between the members of the microflora fighting for ‘dominance’ among themselves. There are of course similarities across the field with bacterial phyla like Bacteroidetes, Firmicutes and Actinobacteria being present in every host [3], but the difference lies in the abundance of their subpopulations. Interestingly enough, in 2009, Turnbaugh et al. [4] observed that even though the microflora composition may vary between individuals, its core function remains the same in similar pathophysiological conditions. In recent years, the combined genetic composition of the microflora, called the microbiome, has been implicated directly with numerous aspects of human health in ways that previously were, and in many cases still remain, unknown [5]. The beneficial role of the host–microflora relationship is dependent on a semi-stable homeostasis which, when disturbed, leads to dysbiosis [6], a status inducing or signifying pathological conditions. Under homeostasis, the functional role [7] of the microbiome includes defense versus pathogens and inflammation via its interactions with the mucosa, vitamin synthesis, energy production, metabolism alteration, dietary modifications like turning fibers into short-chain fatty acids (SCFAs) while contributing to neurodevelopment [8], adult brain function [9] and longevity [10, 11]. During dysbiosis on the other hand, certain microbial populations become differentially abundant driving their metabolic contributions to follow accordingly, strongly affecting the host epigenome [12–14]. The gut microflora actively attributes to the development and maintenance of the gut immune system [15, 16], the permeability of the blood brain barrier (BBB) [17] and its imbalance has already been linked to various pathological conditions like inflammatory bowel diseases (IBDs) [18], cardiovascular conditions [19], atherosclerosis [20], diabetes [21], cancer [22], metabolic syndrome [23], human immunodeficiency virus (HIV) [24], chronic kidney disease [25], antiphospholipid syndrome [26] and most importantly for the premise of this review various neurological [27] and neuropsychiatric [28] conditions. The gut–brain–microbiome axis It has been known for a while now that the enteric nervous system acts as a kind of second ‘brain’ [29, 30] providing a bridge between the gut, the mucosal immune system, the neuroendocrine system, the autonomic nervous system, the vagus nerve and by extension the brain [31]. Previous hypotheses pointed at the brain as the instigator of this relationship trying to ‘control’ the gut, but later studies pointed at a bidirectional relationship. These observations provided the basis for the investigations of the gut–brain axis on a more advanced level revealing four distinct signaling pathways composed of neural, immunological, endocrinological and microbial communications [32]. With the newfound knowledge of the microflora’s implication in human health, the axis expanded to include the microbiome among its components forming what can be found in literature as the microbiome–gut–brain axis [33, 34]. Microbial metabolites interact with the host environment, controlling immune responses via the mucosa, reaching the brain via the bloodstream and modulating neural responses. It is clear that there is a whole ecosystem that affects the homeostasis and pathological conditions alike, via known and unknown mechanisms [35]. For example, the microbiome’s contribution to the metabolism of tryptophan, an essential amino acid for the synthesis of serotonin in the central nervous system (CNS), leads to its absorption by the gut and the crossing of the BBB [36]. The SCFAs, which are immunoregulating metabolites of gut microflora, influence microglia homeostasis and shape brain development [37]. Nitric oxide inhibition via microbial metabolites contributes to microglia maturation [14]. Recently, Bellono et al. [38] have shown that enterochromaffin cells express chemosensors that regulate serotonin-sensitive nerve fibers and establish a direct communication between the gut ecosystem and the nervous system. Current knowledge has linked the gut–brain axis to variable systematic pathological conditions like obesity [39–41], irritable bowel syndrome [42–44], upper GI disorders [45] like gastroparesis, dyspepsia and anorexia, infant colic [46] but mainly to neurological conditions affecting mental state and development, memory and behavior [47]. Clinical and preclinical studies have delved into characterizing the gut microflora dysbiosis in neurological conditions, pointing at differentially abundant microbial genera. From the early stages of life through adolescence, the gut microflora appears to influence not only normal neurological development but also the onset and/or the progression of pathological conditions like autism, schizophrenia, psychosis and bipolar disorder in both animal models and patients [48–50]. Autism spectrum disorders (ASDs), which are characterized by pathological neurodevelopment, have been linked to altered microbiome states in recent studies [51–56]. Increases of the population of bacteria of the genus Lactobacillus have been identified in patients exhibiting first episodes of psychosis and correlated positively with symptom severity (whereas Lachnospiraceae and Ruminococcaceae correlated negatively) in a study by Schwarz et al. [57]. These kinds of differences in microbial composition could possibly provide future strategies in the development of diagnostic tools for various disorders. A longitudinal study performed by Evans et al. [58] highlighted the population loss of Faecalibacterium as important in bipolar disorder, after excluding covariant factors. In 2013 Nieto et al. [59], using oral antibiotics in mice altered the gut microbial composition leading to an increase of brain-derived neurotrophic factor’s expression in the hippocampus that is implicated in cognitive impairment, morphological and functional synaptic pathology and contribution to N-methyl-D-aspartate receptor dysfunction. This dysfunction has been associated with schizophrenia. The gut–brain axis continues to shape our neurological and mental health beyond adolescence. Stress [60], insomnia [61], depression [62], anxiety [63] and even fear-related signaling [64], although not fatal in most cases, directly affect the quality of life of millions daily, regardless of age. As an example, Zheng et al. [65] in a 2016 paper, presented a four-part study, which at first tested germ-free mice and observed a reduction of depression-like symptoms prompting a microbiota–gut–brain axis involvement in depression. They, then, continued the experiment on patients exhibiting major depression disorder (MDD) versus healthy controls to find significant differences in the abundance of the bacterial phyla Firmicutes, Actinobacteria and Bacteroidetes. The third step was fecal microflora transplantation from both MDD and healthy controls to the germ-free mice, which concluded that the mice recipients of the ‘MDD microflora’ after 2 weeks showed increased depression-like and anxiety-like symptomology. Finally, by applying functional shotgun metagenomics, they investigated the metabolic effects of microbiota on ‘MDD microflora’ mice and identified several dysregulated metabolic pathways, especially those involved with carbohydrate metabolism and its function in depression. When it comes to quality of life and in some cases even mortality, strokes and progressive neurodegenerative diseases show dramatic percentages in the ageing population [36]. The microbiota–gut–brain axis has been implicated in the outcome of ischemic brain injury [66] and also in amyotrophic lateral sclerosis [67], multiple sclerosis [68], Parkinson’s [69] and Alzheimer’s disease (AD) [70]. A few months before this review, Bonfili et al. [71] using 3xTg-AD mouse models (transgenic mice with three mutations associated with familial AD) investigated the role of microflora regulation via administration of SLAB51 probiotics (a mixture of lactic acid bacteria and bifidobacteria) in the etiopathology of AD. Their experiments provided insights in regulating amyloid load, counteracting cognitive decline and brain damage, increasing gut hormone concentrations and regulating proteosomal and autophagic pathways. They calculated statistically significant microflora compositional and functional changes between wild-type and AD models, after probiotic treatment, specifically attributed to the increase in Bifidobacterium spp., the reduction in Campylobacterales and their role in inflammation via the regulation of pro-inflammatory cytokines. As evident from the above examples, preclinical and clinical studies can be enhanced significantly by bioinformatics approaches, enriching our apprehension of the microbiome’s involvement. The findings of such approaches provide a unique perspective to the composition and functional role of the microbiome, allowing researchers to theorize on dysbiosis as a cause or an effect of specific conditions and at the same time investigating the effects of intervention to the microflora (Figure 1). The next chapters of this review highlight these technology-based methodologies and provide the outline of how the insilico process formulates in microbiome studies. Figure 1 View largeDownload slide A graphical abstract of this review highlighting the gut–brain axis communication pathways, the host mechanisms the microflora regulates and some of its major perturbagens. It also presents a basic pipeline of computational analysis found in contemporary microbiome publications. Figure 1 View largeDownload slide A graphical abstract of this review highlighting the gut–brain axis communication pathways, the host mechanisms the microflora regulates and some of its major perturbagens. It also presents a basic pipeline of computational analysis found in contemporary microbiome publications. Computational metagenomic approaches In the field of metagenomics research, some fundamental questions often arise: How do we know so much about the microbiome and how did we get there so fast after decades of speculation? How exactly do we know what the microbiome is composed of? Can we identify interactions between populations of the microflora? How did we associate specific members of the microflora and their metabolic products with a diverse spectrum of health conditions? The response lies in the technological advantage, gene and next-generation sequencing (NGS) [72, 73] has provided for the uncultured microflora and the fast strides of Bioinformatics. Before delving into the functional role of microbial populations in the pathophysiology of disorders, we must be able to identify them with high sensitivity and specificity. NGS has provided to a large extend these capabilities by introducing shotgun along the 16S ribosomal RNA (rRNA) sequencing [74, 75]. The 16S rRNA gene is considered to be the de facto housekeeping gene of bacterial and archaeal populations. At this point, the first concession of studying the microbiome is introduced in the form of focusing on the bacteriome’s (bacterial microbiome) implications and often foregoing the mycobiome’s (fungal microbiome) [76, 77] and virome’s [78, 79] (viral microbiome), which both have been associated with pathological conditions but are still largely understudied. This concession is largely based on the richness (quantification of how many distinct species) and abundance (quantification of how many members the species have) of bacterial populations over those of the fungi and viruses but also on their ease of detection and better understanding of their biological processes. Metagenomics [80, 81] is the term introduced to specify the study of the metagenome, which is the combined DNA composition of environmental samples. In the case of human microflora, in fecal and histological biopsy samples, it refers to the identification and quantification of the genetic contributions of microbial subpopulations. [82]. Shotgun metagenomics, although more expensive, provide a higher resolution and accuracy of the results but those become more complex because they include all the microorganisms of a sample [83], including host DNA. 16S rRNA metagenomics, on the other hand, are more accessible and faster to achieve in a laboratory setting when the focus of the study is the bacteria and archaea in multiple control and patient samples. Both approaches use a practice that introduces an amount of variance between different studies, the utilization of NGS library construction for RNA or DNA [84]. Additionally, the 16S rRNA standard operating procedure requires another step in the library building with a fair amount of uncertainty, the amplification of hypervariable regions of the 16S rRNA gene via multiplex polymerase chain reaction (PCR) primers [85]. In both cases, whether it is the sequencing of a whole sample or of the 16S rRNA amplicons, we end up with small reads (25–500 base pairs) allowing for microorganisms who are unknown or in small abundances to be detected. These reads require extensive bioinformatics preprocessing with specialized tools for read trimming, merging, assembly, scaffolding and mapping [86]. Table 1 provides an overview of preprocessing tools and supplies information on their ability to perform: Table 1 Applications for the preprocessing of microbial sequence reads Tool Trimming, merging, scaffolding, assembly Quality contol Denoising Chimera detection Reference Abyss 2.0 ✓ [88] Bambus 2 ✓ [89] BBAP ✓ [90] CATCh ✓ [91] ChimeraSlayer ✓ [92] dupRadar ✓ [93] EP_metagenomic ✓ [94] IDBA-UD ✓ [95] IM-TORNADO ✓ ✓ ✓ ✓ [96] InteMAP ✓ [97] IPED ✓ [98] MAP ✓ [99] MeFiT ✓ [100] MEGAHIT ✓ [101] MESER ✓ [102] MetAMOS ✓ ✓ ✓ ✓ [103] metaSPAdes ✓ [104] MetaVelvet ✓ [105] mothur ✓ ✓ ✓ ✓ [106] NoDe ✓ [107] OCToPUS ✓ ✓ ✓ ✓ [108] Orione ✓ ✓ ✓ ✓ [109] PRICE ✓ [110] QIIME ✓ ✓ ✓ ✓ [111] Qualimap2 ✓ [112] Ray Meta ✓ [113] ROP ✓ [114] Sequins ✓ [115] sleuth ✓ [116] Snowball ✓ ✓ [117] Trimmomatic ✓ ✓ [118] UCHIME ✓ [119] VSEARCH ✓ [120] Xander ✓ [121] Tool Trimming, merging, scaffolding, assembly Quality contol Denoising Chimera detection Reference Abyss 2.0 ✓ [88] Bambus 2 ✓ [89] BBAP ✓ [90] CATCh ✓ [91] ChimeraSlayer ✓ [92] dupRadar ✓ [93] EP_metagenomic ✓ [94] IDBA-UD ✓ [95] IM-TORNADO ✓ ✓ ✓ ✓ [96] InteMAP ✓ [97] IPED ✓ [98] MAP ✓ [99] MeFiT ✓ [100] MEGAHIT ✓ [101] MESER ✓ [102] MetAMOS ✓ ✓ ✓ ✓ [103] metaSPAdes ✓ [104] MetaVelvet ✓ [105] mothur ✓ ✓ ✓ ✓ [106] NoDe ✓ [107] OCToPUS ✓ ✓ ✓ ✓ [108] Orione ✓ ✓ ✓ ✓ [109] PRICE ✓ [110] QIIME ✓ ✓ ✓ ✓ [111] Qualimap2 ✓ [112] Ray Meta ✓ [113] ROP ✓ [114] Sequins ✓ [115] sleuth ✓ [116] Snowball ✓ ✓ [117] Trimmomatic ✓ ✓ [118] UCHIME ✓ [119] VSEARCH ✓ [120] Xander ✓ [121] Note: These steps precede the microbial characterization (binning/OTU picking). View Large Table 1 Applications for the preprocessing of microbial sequence reads Tool Trimming, merging, scaffolding, assembly Quality contol Denoising Chimera detection Reference Abyss 2.0 ✓ [88] Bambus 2 ✓ [89] BBAP ✓ [90] CATCh ✓ [91] ChimeraSlayer ✓ [92] dupRadar ✓ [93] EP_metagenomic ✓ [94] IDBA-UD ✓ [95] IM-TORNADO ✓ ✓ ✓ ✓ [96] InteMAP ✓ [97] IPED ✓ [98] MAP ✓ [99] MeFiT ✓ [100] MEGAHIT ✓ [101] MESER ✓ [102] MetAMOS ✓ ✓ ✓ ✓ [103] metaSPAdes ✓ [104] MetaVelvet ✓ [105] mothur ✓ ✓ ✓ ✓ [106] NoDe ✓ [107] OCToPUS ✓ ✓ ✓ ✓ [108] Orione ✓ ✓ ✓ ✓ [109] PRICE ✓ [110] QIIME ✓ ✓ ✓ ✓ [111] Qualimap2 ✓ [112] Ray Meta ✓ [113] ROP ✓ [114] Sequins ✓ [115] sleuth ✓ [116] Snowball ✓ ✓ [117] Trimmomatic ✓ ✓ [118] UCHIME ✓ [119] VSEARCH ✓ [120] Xander ✓ [121] Tool Trimming, merging, scaffolding, assembly Quality contol Denoising Chimera detection Reference Abyss 2.0 ✓ [88] Bambus 2 ✓ [89] BBAP ✓ [90] CATCh ✓ [91] ChimeraSlayer ✓ [92] dupRadar ✓ [93] EP_metagenomic ✓ [94] IDBA-UD ✓ [95] IM-TORNADO ✓ ✓ ✓ ✓ [96] InteMAP ✓ [97] IPED ✓ [98] MAP ✓ [99] MeFiT ✓ [100] MEGAHIT ✓ [101] MESER ✓ [102] MetAMOS ✓ ✓ ✓ ✓ [103] metaSPAdes ✓ [104] MetaVelvet ✓ [105] mothur ✓ ✓ ✓ ✓ [106] NoDe ✓ [107] OCToPUS ✓ ✓ ✓ ✓ [108] Orione ✓ ✓ ✓ ✓ [109] PRICE ✓ [110] QIIME ✓ ✓ ✓ ✓ [111] Qualimap2 ✓ [112] Ray Meta ✓ [113] ROP ✓ [114] Sequins ✓ [115] sleuth ✓ [116] Snowball ✓ ✓ [117] Trimmomatic ✓ ✓ [118] UCHIME ✓ [119] VSEARCH ✓ [120] Xander ✓ [121] Note: These steps precede the microbial characterization (binning/OTU picking). View Large Read preprocessing Quality control, to ensure error reads, artifacts and bias are detected and corrected Denoising, to remove the noise often introduced by DNA/RNA preparation and PCR Chimera detection, to identify and remove chimeras, which are artificial recombinants formed during the PCR amplification stage [87] It is obvious that there is no clear winner on sequencing methodologies but rather a better suited for the job in front of us. The products of the sequencing process, regardless of the technology used, are distinct sequences of the microflora members of the samples reported in fasta or fastq files and a mapping file containing all the necessary metadata for the samples. These files will be the input of the next steps for the identification of the species the sequences belong to and assigning them taxonomies. Operational taxonomic unit (OTU) is a term introduced to describe clusters of similar sequences, which might represent a species. Although not necessarily flawless, this approach typically uses a 97% similarity of sequences for the clustering and leads to the selection of 1 sequence per OTU to represent the taxa it belongs to via phylogenetic alignment. Various bioinformatics approaches and algorithms exist for this process, which also known as binning, either in workflows or in individual implementations of homology- and prediction- based methods both for shotgun and 16S rRNA metagenomics. Most of these algorithms rely primarily on two specific practices and hybrid implementations of them: denovo and closed reference OTU picking for 16S rRNA data or homology-independent/dependent binning for shotgun data accordingly. Denovo OTU picking is largely based on prediction-based implementations like Infernal [122], UPARSE [123], UCLUST [124], CD-HIT [125], PyNAST [126] METAXA2 [127], CLUSTOM-CLOUD [128], SWARM [129], OptiClust [130] and NINJA-OPS [131], which when clustering do not take into account any existing database for reference sequences but rather try to construct their own phylogenetic tree and assign taxonomies to OTUs after aligning them. The same concept applies to homology-independent binning through applications like CONCOCT [132], GroopM [133], MetaFast [134], MetaBAT [135], MaxBin [136], VizBin [137], COCACOLA [138] and MetaProb [139]. This methodology is better suited when trying to identify metagenomes of habitats with largely unknown members or trying to identify pathogenic microorganisms of unknown origin. It is by far the most computationally demanding approach albeit the most accurate, as no reads are disregarded. On the contrary, when the host environment contains by large known species, like the gut microflora, a closed reference OTU picking strategy (or a homology-dependent one for shotgun data) can provide accurate results in really fast times by using algorithms, which look up reference sequences in the latest versions of databases like RDP [140], GreenGenes [141], SILVA [142], RefSeq [143], HPMCD [144], etc., and cluster the data according to their similarity with those. Implementations of this approach include Taxonomer [145], IMSA-A [146], BLCA [147] and SPINGO [148] for closed reference OTU picking, and MetaPhlAn [149], MEGAN6 [150], Centrifuge [151], MGMapper [152] and OPAL [153] for homology-dependent binning. The output of these pipelines, independent of the methodology used, is usually an OTU table, which contains all the OTUs found in a sample, how many times and their assigned taxonomy among various other metadata. The processes described above are summarized visually in Figure 2. Figure 2 View largeDownload slide 16S rRNA and shotgun metagenomics pipelines for extracting information on the host's gut microbiome. Figure 2 View largeDownload slide 16S rRNA and shotgun metagenomics pipelines for extracting information on the host's gut microbiome. Owing to the fact that different tools are required for shotgun and 16S rRNA approaches, with the help of specialized platforms for bioinformatics resource like OMICtools [154], researchers can create their own workflows to achieve results by combining applications from any of the aforementioned categories or use standardized ones like QIIME, mothur and many others [103, 106, 109, 155–164], which perform multiple tasks of data preparation and downstream analysis. It is the easiest way for scientists to acquire and analyze their microbiome data with the added benefit of creating standardized reproducible results. At this point, we should highlight the fact that metagenome bioinformatics are computationally cumbersome and require copious amounts of processing power, memory and storage but are rapidly advancing because of their rising popularity, the employment of Bioinformatics scientists and their open-source nature. It is widespread practice today for researchers to store their sequence and OTU data on online platforms after their publication to help promote knowledge of the microbiome. These platforms are in fact supported and sometimes financed by organizations and global microbiome initiatives like the Human Microbiome Project [165], whose goal is to standardize the process and disseminate the necessity of similar studies. This way we are rapidly acquiring not only the tools but also the actual data to perform evaluations between different approaches and meta-analyses to infer answers for hypotheses the original authors might not have considered. This is highly dependent on the correct metadata annotation of the stored data, constituting it crucial for reuse and repurposing. There is a variety of online solutions for metagenomics data publishing, a nonexhaustive list of which is included in Table 2. Users of these databases should take note that comparing studies or samples created via different methodologies can be problematic on principle, as the data might not be directly comparable but in need of further analysis. Table 2 Repositories containing public data sets of sequence/OTU data that can be used for metagenomics studies Database URL Description References EBI-metagenomics https://www.ebi.ac.uk/metagenomics/ Part of the European Nucleotide Archive, it offers a pipeline for raw sequence analysis and archiving of metagenomic data. The added value is the fact that users can view the analysis results of each sample [166] Human Microbiome Project Data Portal https://portal.hmpdacc.org/ Perhaps the most daunting of the databases, hmpdacc provides a way for users to browse and download data from the Human Microbiome Project. The interface is hard to navigate to find what you are looking for regarding specific conditions. The iHMP spin-off website which focuses on three specific health conditions (pregnancy, IBD and diabetes type 2) makes things a little easier just for those conditions [167] Human Pan-Microbe Community database http://www.hpmcd.org/index.php Taking an approach similar to IMG/M, HPMCD is offering comparison metagenomics based on microbial populations. The samples are based on EBI metagenomics samples [144] IMG/M https://img.jgi.doe.gov/cgi-bin/m/main.cgi The Integrated Microbial Genomes and Microbial Samples database takes a unique approach of providing microbial genomes from different studies and the ability to compare them. Perhaps not the most intuitive of the databases for reanalyses of specific conditions but rather the role of specific organisms [168] iMicrobe https://www.imicrobe.us/ iMicrobe provides an intuitive search for their data sets based on metadata, which is user-friendly. One drawback is similar to MG-RAST where whole studies cannot be downloaded at once but rather their individual samples. [169] MG-RAST http://metagenomics.anl.gov/ A constantly updated database and pipeline for NGS metagenomics. Data can be accessed via http, ftp and directly via their API. Perhaps a small drawback is the inability to download a whole study from their website something that is possible via ftp [170] QIITA https://qiita.ucsd.edu/ Web-based metagenomic database and pipeline of tools for 16S rRNA and shotgun data sets, originally created for the American Gut Project. QIITA offers data sets in various states of assembly from raw sequences to OTU tables. End user-friendly with resources, which can easily be added in a different pipeline for reanalysis [171] Repositive https://repositive.io/ Repositive is an all-purpose repository of genomic data created as a central hub for genomic data, but it contains metagenomic studies as well. Requires a free account to get started on the data [172] Database URL Description References EBI-metagenomics https://www.ebi.ac.uk/metagenomics/ Part of the European Nucleotide Archive, it offers a pipeline for raw sequence analysis and archiving of metagenomic data. The added value is the fact that users can view the analysis results of each sample [166] Human Microbiome Project Data Portal https://portal.hmpdacc.org/ Perhaps the most daunting of the databases, hmpdacc provides a way for users to browse and download data from the Human Microbiome Project. The interface is hard to navigate to find what you are looking for regarding specific conditions. The iHMP spin-off website which focuses on three specific health conditions (pregnancy, IBD and diabetes type 2) makes things a little easier just for those conditions [167] Human Pan-Microbe Community database http://www.hpmcd.org/index.php Taking an approach similar to IMG/M, HPMCD is offering comparison metagenomics based on microbial populations. The samples are based on EBI metagenomics samples [144] IMG/M https://img.jgi.doe.gov/cgi-bin/m/main.cgi The Integrated Microbial Genomes and Microbial Samples database takes a unique approach of providing microbial genomes from different studies and the ability to compare them. Perhaps not the most intuitive of the databases for reanalyses of specific conditions but rather the role of specific organisms [168] iMicrobe https://www.imicrobe.us/ iMicrobe provides an intuitive search for their data sets based on metadata, which is user-friendly. One drawback is similar to MG-RAST where whole studies cannot be downloaded at once but rather their individual samples. [169] MG-RAST http://metagenomics.anl.gov/ A constantly updated database and pipeline for NGS metagenomics. Data can be accessed via http, ftp and directly via their API. Perhaps a small drawback is the inability to download a whole study from their website something that is possible via ftp [170] QIITA https://qiita.ucsd.edu/ Web-based metagenomic database and pipeline of tools for 16S rRNA and shotgun data sets, originally created for the American Gut Project. QIITA offers data sets in various states of assembly from raw sequences to OTU tables. End user-friendly with resources, which can easily be added in a different pipeline for reanalysis [171] Repositive https://repositive.io/ Repositive is an all-purpose repository of genomic data created as a central hub for genomic data, but it contains metagenomic studies as well. Requires a free account to get started on the data [172] View Large Table 2 Repositories containing public data sets of sequence/OTU data that can be used for metagenomics studies Database URL Description References EBI-metagenomics https://www.ebi.ac.uk/metagenomics/ Part of the European Nucleotide Archive, it offers a pipeline for raw sequence analysis and archiving of metagenomic data. The added value is the fact that users can view the analysis results of each sample [166] Human Microbiome Project Data Portal https://portal.hmpdacc.org/ Perhaps the most daunting of the databases, hmpdacc provides a way for users to browse and download data from the Human Microbiome Project. The interface is hard to navigate to find what you are looking for regarding specific conditions. The iHMP spin-off website which focuses on three specific health conditions (pregnancy, IBD and diabetes type 2) makes things a little easier just for those conditions [167] Human Pan-Microbe Community database http://www.hpmcd.org/index.php Taking an approach similar to IMG/M, HPMCD is offering comparison metagenomics based on microbial populations. The samples are based on EBI metagenomics samples [144] IMG/M https://img.jgi.doe.gov/cgi-bin/m/main.cgi The Integrated Microbial Genomes and Microbial Samples database takes a unique approach of providing microbial genomes from different studies and the ability to compare them. Perhaps not the most intuitive of the databases for reanalyses of specific conditions but rather the role of specific organisms [168] iMicrobe https://www.imicrobe.us/ iMicrobe provides an intuitive search for their data sets based on metadata, which is user-friendly. One drawback is similar to MG-RAST where whole studies cannot be downloaded at once but rather their individual samples. [169] MG-RAST http://metagenomics.anl.gov/ A constantly updated database and pipeline for NGS metagenomics. Data can be accessed via http, ftp and directly via their API. Perhaps a small drawback is the inability to download a whole study from their website something that is possible via ftp [170] QIITA https://qiita.ucsd.edu/ Web-based metagenomic database and pipeline of tools for 16S rRNA and shotgun data sets, originally created for the American Gut Project. QIITA offers data sets in various states of assembly from raw sequences to OTU tables. End user-friendly with resources, which can easily be added in a different pipeline for reanalysis [171] Repositive https://repositive.io/ Repositive is an all-purpose repository of genomic data created as a central hub for genomic data, but it contains metagenomic studies as well. Requires a free account to get started on the data [172] Database URL Description References EBI-metagenomics https://www.ebi.ac.uk/metagenomics/ Part of the European Nucleotide Archive, it offers a pipeline for raw sequence analysis and archiving of metagenomic data. The added value is the fact that users can view the analysis results of each sample [166] Human Microbiome Project Data Portal https://portal.hmpdacc.org/ Perhaps the most daunting of the databases, hmpdacc provides a way for users to browse and download data from the Human Microbiome Project. The interface is hard to navigate to find what you are looking for regarding specific conditions. The iHMP spin-off website which focuses on three specific health conditions (pregnancy, IBD and diabetes type 2) makes things a little easier just for those conditions [167] Human Pan-Microbe Community database http://www.hpmcd.org/index.php Taking an approach similar to IMG/M, HPMCD is offering comparison metagenomics based on microbial populations. The samples are based on EBI metagenomics samples [144] IMG/M https://img.jgi.doe.gov/cgi-bin/m/main.cgi The Integrated Microbial Genomes and Microbial Samples database takes a unique approach of providing microbial genomes from different studies and the ability to compare them. Perhaps not the most intuitive of the databases for reanalyses of specific conditions but rather the role of specific organisms [168] iMicrobe https://www.imicrobe.us/ iMicrobe provides an intuitive search for their data sets based on metadata, which is user-friendly. One drawback is similar to MG-RAST where whole studies cannot be downloaded at once but rather their individual samples. [169] MG-RAST http://metagenomics.anl.gov/ A constantly updated database and pipeline for NGS metagenomics. Data can be accessed via http, ftp and directly via their API. Perhaps a small drawback is the inability to download a whole study from their website something that is possible via ftp [170] QIITA https://qiita.ucsd.edu/ Web-based metagenomic database and pipeline of tools for 16S rRNA and shotgun data sets, originally created for the American Gut Project. QIITA offers data sets in various states of assembly from raw sequences to OTU tables. End user-friendly with resources, which can easily be added in a different pipeline for reanalysis [171] Repositive https://repositive.io/ Repositive is an all-purpose repository of genomic data created as a central hub for genomic data, but it contains metagenomic studies as well. Requires a free account to get started on the data [172] View Large Information overload and microbiome analytics As with all -omics approaches, metagenomics is plighted by vast amounts of data which, although characterized using the techniques above, need to be analyzed, comprehended and rationalized. Apart from computers, humans also must be able to see these data in ways easily understood and offer conjecture to their involvement in human health. Certain metrics and visualization techniques were introduced with the advancement of Bioinformatics toward that goal. Most of the standardized workflows mentioned previously, like QIIME, perform analysis of the microbiome data and exportation of results in diagrams and figures. A categorization of analyses and feedback bioinformatics applications can provide us with is: Microbial community composition, hierarchy and quantitative representation (taxa abundance) These tools focus on representing which taxa are abundant and at which percentage, in the individual samples or in the sample groupings based on their metadata. Raw reads abundance percentages derive from counting the number of OTU sequences present in the samples or a comparison between them to calculate their relative abundance. Following the biological taxonomy of phylum-> class-> order-> family-> genus-> OTU (species), we visualize the microbial composition in distinct levels and even in hierarchies using phylogenetic trees, homocentric diagrams and barplots. Diversity analysis There are two basic metrics of Diversity analyses in microbial samples. α-Diversity, which represents the biodiversity of the samples (how rich a sample is in different microbial communities), and β-Diversity, which characterizes how different the composition of the microbiome in the samples is across groupings of metadata that characterize the environment (e.g. healthy controls versus patients). α-Diversity is usually calculated via rarefaction [173] and algorithms like Chao1, Shannon, etc., and represented via rarefaction or box plots, while β-diversity is predominantly calculated using UniFrac distance metrics [174] and illustrated with principal coordinates analysis plots. In the case of the latter, there is also the ability to use a jackknifing algorithm [175]. Multivariate statistical analysis of microbiome composition in correlation to sample metadata This category focuses on inferring biological associations between microbial species and specific sample groupings. It is important for researchers testing a specific hypothesis to know the differential abundance between sample groupings to see which taxa contribute in statistically significant measurements to dysbiosis. Negative binominal (DeSEQ2), RandomForest, Kruskal–Wallis, Wilcoxon rank test, analysis of variance, t-test and other parametric and non-parametric statistical tests are used to that effect. As metagenomics analysis is based on multiple testing, false discovery rate correction of the P statistical importance via algorithms like Bonferroni, Benjamini–Hochberg or the more recent StructFDR [176], which is specialized for metagenomic data, is important. Guides like GUSTA ME [177] and Statistics How to (http://www.statisticshowto.com/) offer a way for researchers to understand these statistical strategies faster to decide which one conforms to their needs. Also algorithms like MixMC [178] Pearson’s correlation heatmaps, canonical correspondence analysis, redundancy analysis, etc. [179], measure how quantitatively different the microbial composition is in different groupings and what changes researchers can expect to find while studying them. Network analysis Network metrics are engaged to detect microbial species that co-occur, are mutually exclusive or point to specific associations with the sample metadata. This helps researchers model microbial community interactions and infer relationships. Networks are visualized in their traditional node–edge form, where nodes usually represent individual taxa and edges represent their relationships. Pearson’s correlation, Spearman’s rho or the recent mLDM [180] are some of the algorithms used to calculate these relationships. Specialized network construction and analysis tools for microbe–microbe and microbe–host interactions like MMinte [181] have been created to provide a semantic point of view to the microbiome. Additionally, external all-purpose network analysis and visualization applications like Cytoscape [182], Gephi [183] and the Network Workbench Tool [184] can also be used, as many of the microbiome applications can export their constructed networks in appropriate formats. Biomarker discovery Biomarker discovery in metagenomics is the way to identify which specific microbial taxa and their combinations contribute to explanatory variables. Once again, parametric or nonparametric tests are applied to OTU tables, and their results are represented in various forms like odds ratio diagrams. These tests usually apply when one wants to compare two different states in tandem. In recent years, implementations, such as LeFSe [185], have been introduced, which can analyze multiple factors simultaneously to discover biomarkers of dysbiosis. Functional analysis of the microbiome—metabolomics Even though quantification of the microflora’s composition is important to understand the parties involved in dysbiosis and their association with pathophysiology, their actual functionality is the key for examining if they are the cause or mere casualties of disorders. As showcased earlier when talking about the gut–brain axis, microbial metabolic processes, the preeminent way of the microbes to interact with the host, play a vital role to health. Metabolomics is the large-scale study to identify and quantify metabolites, which can provide insights into the host environment during homeostasis or disease. Studies can be focused either on cellular processes that affect the microbiome by creating a nurturing or hostile environment for the microflora or on the extragenomic perturbations caused by microbial metabolites on the host. Usually, modern studies focus on the latter trying to prove or disprove correlation between certain microbial populations and host disorders. Metabolite identification can either occur by analyzing the results of traditional methods like chromatography, mass spectrometry and nuclear magnetic resonance [186–189] or by using metagenomics tools that infer the metabolic products of microbial populations via their genes. Similar to the OTU classification process, functional metagenomics require different approaches in their analysis and visualization of results. Owing to the nature of metagenomics downstream analysis tools to offer insights to multiple of the above categories, Table 3 summarizes some stand-alone implementations and R packages along with their functionalities. Most of the applications require the appropriate input of sequences or OTU tables to analyze and provide visualizations of their results. Even though Tool A might offer a wider variety of operations than Tool B and can be preferred, the truth is that most of them are interchangeable and their usage relies on scientific community adoption and subjective ease of use. Some might argue that the speed and computational requirements of some of the implementations are not subjective, and there are clear winners, but it all depends on the computational power of the end-user’s equipment. Bioinformaticians may choose to even adapt some of them to their own needs, as they are open source, and create their mix and match pipelines. What is important though, is that the interpretation of their statistical analyses, remains in the hands of the researchers and should be used properly regarding different hypotheses. Statistics by themselves if not critically viewed can lead toward skewed conclusions especially in metagenomics, where so many variables are relevant and should be considered. Some researchers might even choose to run their data through multiple applications with the same functionality to verify their findings and use each tool’s resolution and specificity to their benefit. Figure 3 also summarizes frequently asked questions, which may arise during metagenomics research and which of these categories of tools are able to provide answers to them. Table 3 Open-source implementations of microbiome downstream analysis Tool Microbial community composition, hierarchy and quantitative representation Diversity analysis Multivariate statistical analysis of microbiome composition in correlation to sample metadata Network analysis Biomarker discovery Functional analysis/ metabolomics Reference Stand-alone implementations BugBase ✓ ✓ ✓ ✓ [190] Calypso ✓ ✓ ✓ ✓ ✓ ✓ [191] COGNIZER ✓ [192] EMPeror ✓ ✓ [193] Explicet ✓ ✓ ✓ [194] FishTaco ✓ [195] FMAP ✓ [196] FragGeneScan ✓ [197] FuncTree ✓ [198] Galaxy/Hutlab N/A Genboree Microbiome Toolset ✓ ✓ ✓ ✓ [199] Glimmer-MG ✓ [200] GraPhlAn ✓ [201] HUMAnN2 ✓ [202] IMP ✓ ✓ ✓ ✓ ✓ ✓ [203] Krona ✓ [204] LEfSe ✓ ✓ [185] MEGAN6 ✓ ✓ ✓ ✓ [150] MetaCoMET ✓ ✓ ✓ [205] METAGENassist ✓ ✓ ✓ [206] MetaShot ✓ [161] Metaviz ✓ ✓ ✓ [207] MG-RAST ✓ ✓ ✓ [170] Microbiome Analyst ✓ ✓ ✓ ✓ ✓ ✓ [208] Mminte ✓ ✓ [181] MOCAT 2 ✓ ✓ [209] mothur ✓ ✓ ✓ [106] Parallel-META 3 ✓ ✓ ✓ ✓ ✓ ✓ [210] Phoenix 2 ✓ ✓ [211] PICRUSt ✓ [212] Prodigal ✓ [213] QIIME ✓ ✓ ✓ ✓ [111] Rhea ✓ ✓ ✓ [214] SAMSA ✓ [215] ShortBRED ✓ [216] STAMP ✓ ✓ ✓ ✓ [217] Tax4Fun ✓ [218] Taxonomer ✓ [145] VAMPS ✓ ✓ [219] Vikodak ✓ [220] R packages ade4 ✓ ✓ [221] enveomics ✓ ✓ ✓ [222] metaDprof ✓ ✓ [223] metagenomeSeq ✓ ✓ [224] MMiRKAT ✓ [225] mmnet ✓ ✓ ✓ [226] phyloseq ✓ ✓ ✓ ✓ [227] RAIDA ✓ [228] RevEcoR ✓ ✓ [229] ShotgunFunctionalizeR ✓ [230] vegan ✓ ✓ ✓ [231] Tool Microbial community composition, hierarchy and quantitative representation Diversity analysis Multivariate statistical analysis of microbiome composition in correlation to sample metadata Network analysis Biomarker discovery Functional analysis/ metabolomics Reference Stand-alone implementations BugBase ✓ ✓ ✓ ✓ [190] Calypso ✓ ✓ ✓ ✓ ✓ ✓ [191] COGNIZER ✓ [192] EMPeror ✓ ✓ [193] Explicet ✓ ✓ ✓ [194] FishTaco ✓ [195] FMAP ✓ [196] FragGeneScan ✓ [197] FuncTree ✓ [198] Galaxy/Hutlab N/A Genboree Microbiome Toolset ✓ ✓ ✓ ✓ [199] Glimmer-MG ✓ [200] GraPhlAn ✓ [201] HUMAnN2 ✓ [202] IMP ✓ ✓ ✓ ✓ ✓ ✓ [203] Krona ✓ [204] LEfSe ✓ ✓ [185] MEGAN6 ✓ ✓ ✓ ✓ [150] MetaCoMET ✓ ✓ ✓ [205] METAGENassist ✓ ✓ ✓ [206] MetaShot ✓ [161] Metaviz ✓ ✓ ✓ [207] MG-RAST ✓ ✓ ✓ [170] Microbiome Analyst ✓ ✓ ✓ ✓ ✓ ✓ [208] Mminte ✓ ✓ [181] MOCAT 2 ✓ ✓ [209] mothur ✓ ✓ ✓ [106] Parallel-META 3 ✓ ✓ ✓ ✓ ✓ ✓ [210] Phoenix 2 ✓ ✓ [211] PICRUSt ✓ [212] Prodigal ✓ [213] QIIME ✓ ✓ ✓ ✓ [111] Rhea ✓ ✓ ✓ [214] SAMSA ✓ [215] ShortBRED ✓ [216] STAMP ✓ ✓ ✓ ✓ [217] Tax4Fun ✓ [218] Taxonomer ✓ [145] VAMPS ✓ ✓ [219] Vikodak ✓ [220] R packages ade4 ✓ ✓ [221] enveomics ✓ ✓ ✓ [222] metaDprof ✓ ✓ [223] metagenomeSeq ✓ ✓ [224] MMiRKAT ✓ [225] mmnet ✓ ✓ ✓ [226] phyloseq ✓ ✓ ✓ ✓ [227] RAIDA ✓ [228] RevEcoR ✓ ✓ [229] ShotgunFunctionalizeR ✓ [230] vegan ✓ ✓ ✓ [231] Note: These tools use microbial sequences and/or OTU tables to extract information on the microflora’s composition and functionality. View Large Table 3 Open-source implementations of microbiome downstream analysis Tool Microbial community composition, hierarchy and quantitative representation Diversity analysis Multivariate statistical analysis of microbiome composition in correlation to sample metadata Network analysis Biomarker discovery Functional analysis/ metabolomics Reference Stand-alone implementations BugBase ✓ ✓ ✓ ✓ [190] Calypso ✓ ✓ ✓ ✓ ✓ ✓ [191] COGNIZER ✓ [192] EMPeror ✓ ✓ [193] Explicet ✓ ✓ ✓ [194] FishTaco ✓ [195] FMAP ✓ [196] FragGeneScan ✓ [197] FuncTree ✓ [198] Galaxy/Hutlab N/A Genboree Microbiome Toolset ✓ ✓ ✓ ✓ [199] Glimmer-MG ✓ [200] GraPhlAn ✓ [201] HUMAnN2 ✓ [202] IMP ✓ ✓ ✓ ✓ ✓ ✓ [203] Krona ✓ [204] LEfSe ✓ ✓ [185] MEGAN6 ✓ ✓ ✓ ✓ [150] MetaCoMET ✓ ✓ ✓ [205] METAGENassist ✓ ✓ ✓ [206] MetaShot ✓ [161] Metaviz ✓ ✓ ✓ [207] MG-RAST ✓ ✓ ✓ [170] Microbiome Analyst ✓ ✓ ✓ ✓ ✓ ✓ [208] Mminte ✓ ✓ [181] MOCAT 2 ✓ ✓ [209] mothur ✓ ✓ ✓ [106] Parallel-META 3 ✓ ✓ ✓ ✓ ✓ ✓ [210] Phoenix 2 ✓ ✓ [211] PICRUSt ✓ [212] Prodigal ✓ [213] QIIME ✓ ✓ ✓ ✓ [111] Rhea ✓ ✓ ✓ [214] SAMSA ✓ [215] ShortBRED ✓ [216] STAMP ✓ ✓ ✓ ✓ [217] Tax4Fun ✓ [218] Taxonomer ✓ [145] VAMPS ✓ ✓ [219] Vikodak ✓ [220] R packages ade4 ✓ ✓ [221] enveomics ✓ ✓ ✓ [222] metaDprof ✓ ✓ [223] metagenomeSeq ✓ ✓ [224] MMiRKAT ✓ [225] mmnet ✓ ✓ ✓ [226] phyloseq ✓ ✓ ✓ ✓ [227] RAIDA ✓ [228] RevEcoR ✓ ✓ [229] ShotgunFunctionalizeR ✓ [230] vegan ✓ ✓ ✓ [231] Tool Microbial community composition, hierarchy and quantitative representation Diversity analysis Multivariate statistical analysis of microbiome composition in correlation to sample metadata Network analysis Biomarker discovery Functional analysis/ metabolomics Reference Stand-alone implementations BugBase ✓ ✓ ✓ ✓ [190] Calypso ✓ ✓ ✓ ✓ ✓ ✓ [191] COGNIZER ✓ [192] EMPeror ✓ ✓ [193] Explicet ✓ ✓ ✓ [194] FishTaco ✓ [195] FMAP ✓ [196] FragGeneScan ✓ [197] FuncTree ✓ [198] Galaxy/Hutlab N/A Genboree Microbiome Toolset ✓ ✓ ✓ ✓ [199] Glimmer-MG ✓ [200] GraPhlAn ✓ [201] HUMAnN2 ✓ [202] IMP ✓ ✓ ✓ ✓ ✓ ✓ [203] Krona ✓ [204] LEfSe ✓ ✓ [185] MEGAN6 ✓ ✓ ✓ ✓ [150] MetaCoMET ✓ ✓ ✓ [205] METAGENassist ✓ ✓ ✓ [206] MetaShot ✓ [161] Metaviz ✓ ✓ ✓ [207] MG-RAST ✓ ✓ ✓ [170] Microbiome Analyst ✓ ✓ ✓ ✓ ✓ ✓ [208] Mminte ✓ ✓ [181] MOCAT 2 ✓ ✓ [209] mothur ✓ ✓ ✓ [106] Parallel-META 3 ✓ ✓ ✓ ✓ ✓ ✓ [210] Phoenix 2 ✓ ✓ [211] PICRUSt ✓ [212] Prodigal ✓ [213] QIIME ✓ ✓ ✓ ✓ [111] Rhea ✓ ✓ ✓ [214] SAMSA ✓ [215] ShortBRED ✓ [216] STAMP ✓ ✓ ✓ ✓ [217] Tax4Fun ✓ [218] Taxonomer ✓ [145] VAMPS ✓ ✓ [219] Vikodak ✓ [220] R packages ade4 ✓ ✓ [221] enveomics ✓ ✓ ✓ [222] metaDprof ✓ ✓ [223] metagenomeSeq ✓ ✓ [224] MMiRKAT ✓ [225] mmnet ✓ ✓ ✓ [226] phyloseq ✓ ✓ ✓ ✓ [227] RAIDA ✓ [228] RevEcoR ✓ ✓ [229] ShotgunFunctionalizeR ✓ [230] vegan ✓ ✓ ✓ [231] Note: These tools use microbial sequences and/or OTU tables to extract information on the microflora’s composition and functionality. View Large Figure 3 View largeDownload slide Common questions in metagenomics research and the specific categories of downstream analysis that can provide answers. Figure 3 View largeDownload slide Common questions in metagenomics research and the specific categories of downstream analysis that can provide answers. Finally, worth mentioning is that many commercial solutions, which in some cases come as bundles with sequencing equipment, provide similar functionality, as the tools mentioned above with the added benefit of offering training and troubleshooting support, but carrying the disadvantage of their cost. These solutions include products like ERA-7 (https://era7bioinformatics.com/), CLC Genomics Workbench (https://www.qiagenbioinformatics.com/products/clc-genomics-workbench/), Strand NGS (http://www.strand-ngs.com/) and NovoWorx (http://www.novocraft.com/products/novoworx/). Computational systems have catered to the needs of life sciences for many years now, following a parallel progress and evolution. Algorithms have been developed, applications coded and hardware constructed specifically for bioinformatics and medical informatics as demonstrated here. The goal of these efforts is to enhance research and to accommodate new and complex hypotheses that could be examined with speed and precision. Future strives will bring scientists closer to a complete modeling and emulation of the brain and the gut, allowing us to see, in silico, the machinations and evolution of the gut-brain axis even in real time. Recent strives toward that goal have shown great potential like the works of Cockrell et al. [232], Leber et al. [233], Abedi et al. [234] and others. It is our belief that these computational analyses will drive not only the identification but also the treatment of various conditions. Treating the disease, the patient or the patient–microflora complex. Will precision medicine be treating all of them? In 2015, the Precision Medicine Initiative (recently renamed to ‘All of Us’ [235, 236]) was announced by the US government to facilitate a better focus on personalized health and the type of treatment, which accounts for variability and identifies the unique features of each individual. With everything this review has shown about the microbiome and how close we are today to characterize it uniquely for everyone, because of our achievements in bioinformatics, we believe that the parallelism with this initiative is clear. If we are to talk about a person’s diagnosis, prognosis and therapy, it seems almost imperative to consider the whole microflora–host system. It is the entire system that suffers and, perhaps therein, lies the correct course of treatment or the necessary diagnostic and prognostic biomarkers. After all the microbiome has been implicated in regulating pharmacokinetics, pharmacodynamics and driving pharmacogenetics [237–239], providing added value to our investigations of drug metabolism and response. Exercise, diet and a lifestyle away from sedentary conditions have long been known to promote health for assorted reasons especially concerning the cardiovascular system [240, 241]. Today, we know that these factors perturb the gut’s microflora [242–244], driving the homeostasis and by extension the systemic health. Our diet and our medication regiment regulate our microflora’s composition in a larger scale, by adding new microorganisms or creating a hostile environment for others, affecting, among other systems, our gut–brain axis [245, 246]. By using the wisdom acquired via the downstream analysis of the microbiome, we can discuss targeted practices of diet and antibiotic usage, customized for everyone according to their microbial profile. It is an innovative approach to the well-known expression ‘We are what we eat’. There is also a special category of intervention, which includes probiotics, prebiotics and synbiotics that can influence the microflora, can be used as treatment for various conditions and have been the focus of many studies [247–252]. The terms, although popular in literature and gaining popularity in everyday life, are not well understood by the public. Probiotics are live organisms (bacteria, yeasts, etc.) that can supplement a person’s microflora when they are introduced in their diet. Prebiotics are ingredients that help specific microorganisms, already introduced to the organism, flourish and fight off pathogens and/or reach the appropriate numbers for dysbiosis. Finally, synbiotics are a mixture of the previous two groups. Owing to their mechanism of action, these dietary supplements can be used to target specific populations, which the current insights into dysbiosis have already identified by methods such as the ones described previously in this review. For example, Mehta et al. [253] have proposed the usage of lactic acid bacteria probiotics in reducing the oxidative stress implicated in AD, by suppressing D-galactose, which is implicated in increased reactive oxygen species production and nerve growth factor suppression. Finally, in recent years, a new term emerged, psychobiotics [254], which refers to living organisms (gut bacteria) introduced in the host’s system to treat mental disorders. Their method of action targets specifically the gut–brain axis via the neurotropic metabolic products of these microorganisms [255]. Although probiotics and prebiotics may be valuable additions to a personalized treatment regimen, they rely on daily consumption to be useful and contribute to homeostasis. In the past few years, the more targeted and permanent solution of fecal microbiota transplantation [256–258] has been successfully deployed to help the host’s microflora to be repopulated by ‘healthy’ symbionts. Based on what we know, for a transplantation to be successful a plethora of cofounding factors must be considered. What can be deemed as ‘healthy’ donor and ‘normal’ microflora? Is a transplant from someone living in the United States appropriate for someone in Asia? Considering location and different lifestyles, we must rely on our knowledge of the functional role of the microbiome as discussed previously. Also is the host’s lack of clinical symptomology enough to consider a transplant ‘healthy’ or do we have to test for ‘dormant’ GI pathogens [259]? Is fecal material a reliable source of microflora, as it can change constantly because of external factors [260]? Despite of the many difficulties, recent studies have shown promise in treating a variety of pathological conditions, including neuropsychological ones. For example, microflora transplantation has been successful in alleviating autism symptomology in a recent study by Kang et al. [261] where ASD-related behavior was improved by 22% following transplantation and up to 24% in a 8-week period after that (according to the Childhood Autism Rating Scale). Treating the microflora is not the only thing one must consider when trying to combat dysbiosis. One of the major reasons of microbial population loss is the broad usage of antibiotics [262]. Although critical for our health, the extended usage of these drugs has caused some issues going beyond the creation of antibiotic resistant bacteria [263]. Especially during early life, antibiotics can help combat pathogens introduced into the host but are also responsible for dysbiosis [264]. Once more, the need for targeted precision antibiotics comes into the foreground requiring an extensive understanding of their implications to the microflora synthesis and how populations vital to homeostasis can be spared. Complimenting antibacterial treatments with probiotics, which are not susceptible to the antibiotics themselves [265], can prove useful for customized approaches to the needs of patients [266, 267]. In the past 5 years, the microbiome has seen a significant boost in scientific interest and publications. A relative term search (microbiome, microbiota, microflora), in PubMed alone, yields over 35 000 results for just this period with an exponential growth each passing year. Some researchers [268] have even characterized the year 2016 as the ‘banner year’ for microbiome research, something that can be directly attributed to the bioinformatics approaches at our disposal and the constant flow of information linking it to systemic health. This shift toward a better understanding of all the mechanisms describing and being perturbed by our microbiome is driven by our need to be able to better understand the host–microflora relationship. The acquisition of this knowledge can lead, not only in more precise definition of the pathophysiological attributes of disorders but also to the customization of treatment for individuals or specific patient groups. Several aspects of today’s medicine are being driven by genomics, proteomics, epigenomics, metabolomics, microbiomics and their integration via systems biology, allowing researchers to accurately predict the onset, progression and pharmacological response of a pathological condition [269, 270]. Scientists are now able not only to precisely identify and evaluate the microbiome but also track its changes and the ones it provokes through time, dynamically tracking bacterial population abundance differences and metabolite production [271–273]. The complexity of the gut–brain–microbiome axis makes for an interesting target for the application of our research efforts and a perfect candidate to be supported by integrated multidisciplinary approaches [274]. As the embryonic stage via our maternal microbiome and developing rapidly in the first 3–5 years of life [275, 276], our microbial partners help shape the development of our CNS and behavior. During our lifespan, the gut microbiome contributes toward neurological and mental health. The cross talk between the microflora ecology and the host’s physiology is based on interactions on a genetic, protein and metabolic level for both sides involved. The studies previously mentioned in this review highlight the gut microbiome as a modulator of brain development and neurotransmitter signaling systems but also as a mediator of neurological, mental and behavioral function in adults. We are confronted with vast networks of signals and interactions, in which we are called to identify the essential components for homeostasis and understand what perturbations are applied by dysbiosis. It is important in a dynamic ecosystem that research will be focused on the factors that drive permanent or reversible changes that are essential in a variety of functions and their involvement in molecular mechanisms. These fundamental biological mechanisms can be explored via novel high-throughput computational methodologies that combine and analyze the evolution of the microbial communities and their genetic composition, microbial–host biological systems interaction and the effects of external environmental factors on the microbial–host ecosystem. More specifically, computational metagenomics cross-analysis and host genetic susceptibility/genomic background will provide new insights into the onset and progression of CNS disease. In addition, the characterization and quantification of the genomic composition of the microbiome under different environmental factors can provide information of the microbiome’s role as a cause or effect of disease, something that is currently under investigation. Translating the biological networks into computational ones, which include host-omics, meta-omics and related phenotypes in tandem, we can construct prediction models that can reveal valuable information on metabolic and other molecular components as well as signaling pathways mediated in brain health and disease. The development of new combinational databases [277], which proliferate the knowledge derived by our research, will help to make it accessible and usable by other investigators. These novel bioinformatics avenues lead to a better understanding of neurological and mental disease by pinpointing the modifiable factors that influence the microbiome and act as regulators of health. The outcome of this knowledge can be new therapeutic strategies that complement a possible prognostic and diagnostic role of the gut microbiome, in medicine at a personalized as well as a general population level. Key Points Gut–brain axis is a complex communication system mediating human health. Microflora–gut–brain axis is based on a bidirectional relationship. Shotgun and 16S rRNA sequencing precision is essential for our data. Computational downstream analysis of the microbiome provides answers regarding its composition and function. Microbiome research could offer a novel approach to precision medicine. Funding G.M.S. holds the Bioinformatics ERA Chair position funded by the European Commission Research Executive Agency (REA) Grant BIORISE (grant number 669026), under the Spreading Excellence, Widening Participation, Science with and for Society Framework. Nikolas Dovrolis is a PhD candidate of Pharmacology at the Democritus University of Thrace. He is a Computer Science graduate with a Master’s Degree in Molecular Biology and Genetics. George Kolios, MD, PhD, is a Professor of Pharmacology at Democritus University Thrace, Greece. He is a clinical Gastroenterologist, with extensive research in mucosal immunology, focused on intestinal inflammation and microbiota. George M. Spyrou, PhD, holds the Bioinformatics ERA Chair and is the Head of the Bioinformatics Group at the Cyprus Institute of Neurology and Genetics. Ioanna Maroulakou, PhD, is a Professor of Genetics at Democritus University of Thrace and has extensive experience and expertise in Translational Research and Acquired genetic disorders including neurodegenerative diseases. References 1 Berg RD. The indigenous gastrointestinal microflora . Trends Microbiol 1996 ; 4 ( 11 ): 430 – 5 . http://dx.doi.org/10.1016/0966-842X(96)10057-3 Google Scholar Crossref Search ADS PubMed 2 Franzosa EA , Huang K , Meadow JF. Identifying personal microbiomes using metagenomic codes . Proc Natl Acad Sci USA 2015 ; 112 ( 22 ): E2930 – 8 . Google Scholar Crossref Search ADS PubMed 3 Sekirov I , Russell SL , Antunes LCM , et al. Gut microbiota in health and disease . Physiol Rev 2010 ; 90 ( 3 ): 859 – 904 . http://dx.doi.org/10.1152/physrev.00045.2009 Google Scholar Crossref Search ADS PubMed 4 Turnbaugh PJ , Hamady M , Yatsunenko T , et al. A core gut microbiome in obese and lean twins . Nature 2009 ; 457 ( 7228 ): 480 – 4 . http://dx.doi.org/10.1038/nature07540 Google Scholar Crossref Search ADS PubMed 5 Levy M , Blacher E , Elinav E. Microbiome, metabolites and host immunity . Curr Opin Microbiol 2017 ; 35 : 8 – 15 . http://dx.doi.org/10.1016/j.mib.2016.10.003 Google Scholar Crossref Search ADS PubMed 6 Carding S , Verbeke K , Vipond DT , et al. Dysbiosis of the gut microbiota in disease . Microb Ecol Health Dis 2015 ; 26 ( 0 ):. 7 Flint HJ , Scott KP , Louis P , et al. The role of the gut microbiota in nutrition and health . Nat Rev Gastroenterol Hepatol 2012 ; 9 ( 10 ): 577 – 89 . http://dx.doi.org/10.1038/nrgastro.2012.156 Google Scholar Crossref Search ADS PubMed 8 Tognini P. Gut microbiota: a potential regulator of neurodevelopment . Front Cell Neurosci 2017 ; 11 : 25 . Google Scholar Crossref Search ADS PubMed 9 Rogers G , Keating D , Young R , et al. From gut dysbiosis to altered brain function and mental illness: mechanisms and pathways . Mol Psychiatry 2016 ; 21 ( 6 ): 738 – 48 . http://dx.doi.org/10.1038/mp.2016.50 Google Scholar Crossref Search ADS PubMed 10 Gruber J , Kennedy BK. Microbiome and longevity: gut microbes send signals to host mitochondria . Cell 2017 ; 169 ( 7 ): 1168 – 9 . http://dx.doi.org/10.1016/j.cell.2017.05.048 Google Scholar Crossref Search ADS PubMed 11 Han B , Sivaramakrishnan P , Lin C-CJ , et al. Microbial genetic composition tunes host longevity . Cell 2017 ; 169 ( 7 ): 1249 – 62.e1213 . Google Scholar Crossref Search ADS PubMed 12 Lee E-S , Song E-J , Nam Y-D. Dysbiosis of gut microbiome and its impact on epigenetic regulation . J Clin Epigene 2017 , in press. 13 Krautkramer KA , Kreznar JH , Romano KA , et al. Diet-microbiota interactions mediate global epigenetic programming in multiple host tissues . Mol Cell 2016 ; 164 : 982 – 92 . Google Scholar Crossref Search ADS 14 Tse JKY. Gut microbiota, nitric oxide and microglia as pre-requisites for neurodegenerative disorders . ACS Chem Neurosci 2017 ; 8 : 1438 – 47 . http://dx.doi.org/10.1021/acschemneuro.7b00176 Google Scholar Crossref Search ADS PubMed 15 Round JL , Mazmanian SK. The gut microbiota shapes intestinal immune responses during health and disease . Nat Rev Immunol 2009 ; 9 ( 5 ): 313 – 23 . http://dx.doi.org/10.1038/nri2515 Google Scholar Crossref Search ADS PubMed 16 Zmora N , Bashiardes S , Levy M , et al. The role of the immune system in metabolic health and disease . Cell Metab 2017 ; 25 ( 3 ): 506 – 21 . http://dx.doi.org/10.1016/j.cmet.2017.02.006 Google Scholar Crossref Search ADS PubMed 17 Braniste V , Al-Asmakh M , Kowal C , et al. The gut microbiota influences blood-brain barrier permeability in mice . Sci Transl Med 2014 ; 6 ( 263 ): 263ra158 . Google Scholar Crossref Search ADS PubMed 18 Holleran G , Lopetuso L , Ianiro G , et al. Gut microbiota and inflammatory bowel disease: an update . Minerva Gastroenterol Dietol 2017 ; 63 : 373 – 84 . Google Scholar PubMed 19 Tang WW , Hazen SL. The gut microbiome and its role in cardiovascular diseases . Circulation 2017 ; 135 ( 11 ): 1008 – 10 . http://dx.doi.org/10.1161/CIRCULATIONAHA.116.024251 Google Scholar Crossref Search ADS PubMed 20 Drosos I , Tavridou A , Kolios G. New aspects on the metabolic role of intestinal microbiota in the development of atherosclerosis . Metabolism 2015 ; 64 ( 4 ): 476 – 81 . http://dx.doi.org/10.1016/j.metabol.2015.01.007 Google Scholar Crossref Search ADS PubMed 21 Stefanaki C , Peppa M , Mastorakos G , et al. Examining the gut bacteriome, virome, and mycobiome in glucose metabolism disorders: are we on the right track? Metabolism 2017 ; 73 : 52 – 66 . Google Scholar Crossref Search ADS PubMed 22 Bhutia YD , Ogura J , Sivaprakasam S , et al. Gut microbiome and colon cancer: role of bacterial metabolites and their molecular targets in the host . Curr Colorectal Cancer Rep 2017 ; 13 ( 2 ): 111 – 18 . http://dx.doi.org/10.1007/s11888-017-0362-9 Google Scholar Crossref Search ADS PubMed 23 Bouter KE , van Raalte DH , Groen AK , et al. Role of the gut microbiome in the pathogenesis of obesity and obesity-related metabolic dysfunction . Gastroenterology 2017 ; 13 : 111 – 18 . 24 Liu J , Williams B , Frank D , et al. Inside out: HIV, the gut microbiome, and the mucosal immune system . J Immunol 2017 ; 198 ( 2 ): 605 – 14 . http://dx.doi.org/10.4049/jimmunol.1601355 Google Scholar Crossref Search ADS PubMed 25 Nallu A , Sharma S , Ramezani A , et al. Gut microbiome in chronic kidney disease: challenges and opportunities . Transl Res 2017 ; 179 : 24 – 37 . http://dx.doi.org/10.1016/j.trsl.2016.04.007 Google Scholar Crossref Search ADS PubMed 26 Ruff WE , Vieira SM , Kriegel MA. The role of the gut microbiota in the pathogenesis of antiphospholipid syndrome . Curr Rheumatol Rep 2015 ; 17 ( 1 ): 472.http://dx.doi.org/10.1007/s11926-014-0472-1 Google Scholar Crossref Search ADS PubMed 27 Wang Y , Kasper LH. The role of microbiome in central nervous system disorders . Brain Behav Immun 2014 ; 38 : 1 – 12 . http://dx.doi.org/10.1016/j.bbi.2013.12.015 Google Scholar Crossref Search ADS PubMed 28 Dinan TG , Cryan JF. The impact of gut microbiota on brain and behaviour: implications for psychiatry . Curr Opin Clin Nutr Metabol Care 2015 ; 18 ( 6 ): 552 – 8 . http://dx.doi.org/10.1097/MCO.0000000000000221 Google Scholar Crossref Search ADS 29 Gershon M. The Second Brain: A Groundbreaking New Understanding of Nervous Disorders of the Stomach and Intestine . Harper Collins , New York , 1999 . 30 Furness JB , Costa M , The enteric nervous system . Churchill Livingstone Edinburgh etc ., 1987 . 31 Furness JB. The enteric nervous system and neurogastroenterology . Nat Rev Gastroenterol Hepatol 2012 ; 9 ( 5 ): 286 – 94 . http://dx.doi.org/10.1038/nrgastro.2012.32 Google Scholar Crossref Search ADS PubMed 32 Holzer P , Farzi A , Neuropeptides and the microbiota-gut-brain axis. In: Microbial Endocrinology: The Microbiota-Gut-Brain Axis in Health and Disease . Springer , New York , 2014 , 195 – 219 . 33 Cryan JF , O'Mahony SM. The microbiome‐gut‐brain axis: from bowel to behavior . Neurogastroenterol Motil 2011 ; 23 ( 3 ): 187 – 92 . Google Scholar Crossref Search ADS PubMed 34 Bauer KC , Huus KE , Finlay BB. Microbes and the mind: emerging hallmarks of the gut microbiota–brain axis . Cell Microbiol 2016 ; 18 ( 5 ): 632 – 44 . http://dx.doi.org/10.1111/cmi.12585 Google Scholar Crossref Search ADS PubMed 35 Sampson TR , Mazmanian SK. Control of brain development, function, and behavior by the microbiome . Cell Host Microbe 2015 ; 17 ( 5 ): 565 – 76 . http://dx.doi.org/10.1016/j.chom.2015.04.011 Google Scholar Crossref Search ADS PubMed 36 Le Floc’h N , Otten W , Merlot E. Tryptophan metabolism, from nutrition to potential therapeutic applications . Amino Acids 2011 ; 41 ( 5 ): 1195 – 205 . Google Scholar Crossref Search ADS PubMed 37 Erny D , Hrabě de Angelis AL , Jaitin D , et al. Host microbiota constantly control maturation and function of microglia in the CNS . Nat Neurosci 2015 ; 18 ( 7 ): 965 – 77 . Google Scholar Crossref Search ADS PubMed 38 Bellono NW , Bayrer JR , Leitch DB , et al. Enterochromaffin cells are gut chemosensors that couple to sensory neural pathways . Cell 2017 ; 170 : 185 – 98.e16 . Google Scholar Crossref Search ADS PubMed 39 Greathouse KL , Faucher MA , Hastings-Tolsma M. The gut microbiome, obesity, and weight control in women‘s reproductive health . West J Nurs Res 2017 ; 39 : 1094 – 119 . Google Scholar Crossref Search ADS PubMed 40 Komaroff AL. The microbiome and risk for obesity and diabetes . Jama 2017 ; 317 ( 4 ): 355 – 6 . http://dx.doi.org/10.1001/jama.2016.20099 Google Scholar Crossref Search ADS PubMed 41 Sanmiguel CP , Jacobs J , Gupta A , et al. Surgically induced changes in gut microbiome and hedonic eating as related to weight loss: preliminary findings in obese women undergoing bariatric surgery . Psychosomatic Med 2017 ; 79 : 880 – 7 . http://dx.doi.org/10.1097/PSY.0000000000000494 Google Scholar Crossref Search ADS 42 Tap J , Derrien M , Törnblom H , et al. Identification of an intestinal microbiota signature associated with severity of irritable bowel syndrome . Gastroenterology 2017 ; 152 ( 1 ): 111 – 23. e118 . Google Scholar Crossref Search ADS PubMed 43 Ringel Y. The gut microbiome in irritable bowel syndrome and other functional bowel disorders . Gastroenterol Clin N Am 2017 ; 46 ( 1 ): 91 – 101 . http://dx.doi.org/10.1016/j.gtc.2016.09.014 Google Scholar Crossref Search ADS 44 Mahurkar-Joshi S , Labus JS , Jacobs J , et al. 143-Colonic mucosal microbiome is associated with mucosal microrna expression in irritable bowel syndrome . Gastroenterology 2017 ; 152 ( 5 ): S40 – 1 . Google Scholar Crossref Search ADS 45 Sanger GJ , Lee K. Hormones of the gut-brain axis as targets for the treatment of upper gastrointestinal disorders . Nat Rev Drug Discov 2008 ; 7 ( 3 ): 241.http://dx.doi.org/10.1038/nrd2444 Google Scholar Crossref Search ADS PubMed 46 Pärtty A , Kalliomäki M. Infant colic is still a mysterious disorder of the microbiota–gut–brain axis . Acta Paediatrica 2017 ; 106 ( 4 ): 528 – 9 . Google Scholar Crossref Search ADS PubMed 47 Tremlett H , Bauer KC , Appel‐Cresswell S , et al. The gut microbiome in human neurological disease: a review . Ann Neurol 2017 ; 81 : 369 – 82 . Google Scholar Crossref Search ADS PubMed 48 Yang I , Corwin EJ , Brennan PA , et al. The infant microbiome: implications for infant health and neurocognitive development . Nurs Res 2016 ; 65 ( 1 ): 76 – 88 . http://dx.doi.org/10.1097/NNR.0000000000000133 Google Scholar Crossref Search ADS PubMed 49 Sharon G , Sampson TR , Geschwind DH , et al. The central nervous system and the gut microbiome . Cell 2016 ; 167 ( 4 ): 915 – 32 . http://dx.doi.org/10.1016/j.cell.2016.10.027 Google Scholar Crossref Search ADS PubMed 50 Desbonnet L , Clarke G , Traplin A , et al. Gut microbiota depletion from early adolescence in mice: Implications for brain and behaviour . Brain Behav Immun 2015 ; 48 : 165 – 73 . http://dx.doi.org/10.1016/j.bbi.2015.04.004 Google Scholar Crossref Search ADS PubMed 51 Li Q , Han Y , Dy ABC , et al. The gut microbiota and autism spectrum disorders . Front Cell Neurosci 2017 ; 11 : 120 . http://dx.doi.org/10.3389/fncel.2017.00120 Google Scholar Crossref Search ADS PubMed 52 Braun J. Tightening the Case for Gut Microbiota in Autism-Spectrum Disorder . Elsevier , 2017 . 53 Ding HT , Taur Y , Walkup JT. Gut microbiota and autism: key concepts and findings . J Autism Dev Disord 2016 ; 47 : 480 – 9 . Google Scholar Crossref Search ADS 54 Strati F , Cavalieri D , Albanese D , et al. New evidences on the altered gut microbiota in autism spectrum disorders . Microbiome 2017 ; 5 ( 1 ): 24 . http://dx.doi.org/10.1186/s40168-017-0242-1 Google Scholar Crossref Search ADS PubMed 55 Gogou M , Gogou C. The effect of intestinal microbiome on autism spectrum disorder . J Pediatr Sci 2016 ; 8 ( 0 ). 56 Vuong HE , Hsiao EY. Emerging roles for the gut microbiome in autism spectrum disorder . Biol Psychiatry 2017 ; 81 ( 5 ): 411 – 23 . http://dx.doi.org/10.1016/j.biopsych.2016.08.024 Google Scholar Crossref Search ADS PubMed 57 Schwarz E , Maukonen J , Hyytiäinen T , et al. Analysis of microbiota in first episode psychosis identifies preliminary associations with symptom severity and treatment response . Schizophr Res 2017 , doi: 10.1016/j.schres.2017.04.017. 58 Evans SJ , Bassis CM , Hein R , et al. The gut microbiome composition associates with bipolar disorder and illness severity . J Psychiatr Res 2017 ; 87 : 23 – 9 . http://dx.doi.org/10.1016/j.jpsychires.2016.12.007 Google Scholar Crossref Search ADS PubMed 59 Nieto R , Kukuljan M , Silva H. BDNF and schizophrenia: from neurodevelopment to neuronal plasticity, learning, and memory . Front Psychiatry 2013 ; 4 : 45 . Google Scholar Crossref Search ADS PubMed 60 Marin IA , Goertz JE , Ren T , et al. Microbiota alteration is associated with the development of stress-induced despair behavior . Sci Rep 2017 ; 7 : 43859.http://dx.doi.org/10.1038/srep43859 Google Scholar Crossref Search ADS PubMed 61 Lothian J , Blampied NM , Rucklidge JJ. Effect of micronutrients on insomnia in adults a multiple-baseline study . Clin Psychol Sci 2016 ; 4 ( 6 ): 2167702616631740. Google Scholar Crossref Search ADS 62 D’Mello C , Swain MG. Immune-to-brain communication pathways in inflammation-associated sickness and depression. In: Inflammation-Associated Depression: Evidence, Mechanisms and Implications . Springer , Switzerland , 2017 : 73 – 94 . 63 MacQueen G , Surette M , Moayyedi P. The gut microbiota and psychiatric illness . J Psychiatry Neurosci 2017 ; 42 ( 2 ): 75.http://dx.doi.org/10.1503/jpn.170028 Google Scholar Crossref Search ADS PubMed 64 Hoban A , Stilling R , Moloney G , et al. The microbiome regulates amygdala-dependent fear recall . Mol Psychiatry 2017 , doi: 10.1038/mp.2017.100. 65 Zheng P , Zeng B , Zhou C , et al. Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host‘s metabolism . Mol Psychiatry 2016 ; 21 ( 6 ): 786 – 96 . Google Scholar Crossref Search ADS PubMed 66 Benakis C , Brea D , Caballero S , et al. Commensal microbiota affects ischemic stroke outcome by regulating intestinal [gamma][delta] T cells . Nat Med 2016 ; 22 : 516 – 23 . http://dx.doi.org/10.1038/nm.4068 Google Scholar Crossref Search ADS PubMed 67 Zhang Y-g , Wu S , Yi J , et al. Target intestinal microbiota to alleviate disease progression in amyotrophic lateral sclerosis . Clin Ther 2017 ; 39 : 322 – 36 . http://dx.doi.org/10.1016/j.clinthera.2016.12.014 Google Scholar Crossref Search ADS PubMed 68 Mirza A , Mao-Draayer Y. The gut microbiome and microbial translocation in multiple sclerosis . Clin Immunol 2017 , doi: 10.1016/j.clim.2017.03.001. 69 Hill‐Burns EM , Debelius JW , Morton JT , et al. Parkinson's disease and Parkinson's disease medications have distinct signatures of the gut microbiome . Mov Disord 2017 ; 32 : 739 – 49 . Google Scholar Crossref Search ADS PubMed 70 Pistollato F , Sumalla Cano S , Elio I , et al. Role of gut microbiota and nutrients in amyloid formation and pathogenesis of Alzheimer disease . Nutr Rev 2016 ; 74 ( 10 ): 624 – 34 . http://dx.doi.org/10.1093/nutrit/nuw023 Google Scholar Crossref Search ADS PubMed 71 Bonfili L , Cecarini V , Berardi S , et al. Microbiota modulation counteracts Alzheimer‘s disease progression influencing neuronal proteolysis and gut hormones plasma levels . Sci Rep 2017 ; 7 ( 1 ): 2426 . Google Scholar Crossref Search ADS PubMed 72 Schuster SC. Next-generation sequencing transforms today's biology . Nat Methods 2008 ; 5 ( 1 ): 16.http://dx.doi.org/10.1038/nmeth1156 Google Scholar Crossref Search ADS PubMed 73 Metzker ML. Sequencing technologies—the next generation . Nat Rev Genet 2010 ; 11 ( 1 ): 31 – 46 . Google Scholar Crossref Search ADS PubMed 74 Jovel J , Patterson J , Wang W , et al. Characterization of the gut microbiome using 16S or shotgun metagenomics . Front Microbiol 2016 ; 7 : 459 . Google Scholar Crossref Search ADS PubMed 75 Ranjan R , Rani A , Metwally A , et al. Analysis of the microbiome: advantages of whole genome shotgun versus 16S amplicon sequencing . Biochem Biophys Res Commun 2016 ; 469 ( 4 ): 967 – 77 . http://dx.doi.org/10.1016/j.bbrc.2015.12.083 Google Scholar Crossref Search ADS PubMed 76 Cui L , Morris A , Ghedin E. The human mycobiome in health and disease . Genome Med 2013 ; 5 ( 7 ): 63.http://dx.doi.org/10.1186/gm467 Google Scholar Crossref Search ADS PubMed 77 Huseyin CE , O’Toole PW , Cotter PD , et al. Forgotten fungi—the gut mycobiome in human health and disease . FEMS Microbiol Rev 2017 ; 41 : 479 – 511 . Google Scholar Crossref Search ADS PubMed 78 Zhao G , Wu G , Lim ES , et al. VirusSeeker, a computational pipeline for virus discovery and virome composition analysis . Virology 2017 ; 503 : 21 – 30 . http://dx.doi.org/10.1016/j.virol.2017.01.005 Google Scholar Crossref Search ADS PubMed 79 Czeczko P , Greenway SC , de Koning A. EzMap: a simple pipeline for reproducible analysis of the human virome . Bioinformatics 2017 ; 33 : 2573 – 4 . http://dx.doi.org/10.1093/bioinformatics/btx202 Google Scholar Crossref Search ADS PubMed 80 Handelsman J , Rondon MR , Brady SF , et al. Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products . Chem Biol 1998 ; 5 ( 10 ): R245 – 9 . Google Scholar Crossref Search ADS PubMed 81 Wooley JC , Godzik A , Friedberg I. A primer on metagenomics . PLoS Comput Biol 2010 ; 6 ( 2 ): e1000667. Google Scholar Crossref Search ADS PubMed 82 Handelsman J. Metagenomics: application of genomics to uncultured microorganisms . Microbiol Mol Biol Rev 2004 ; 68 ( 4 ): 669 – 85 . http://dx.doi.org/10.1128/MMBR.68.4.669-685.2004 Google Scholar Crossref Search ADS PubMed 83 Sharpton TJ. An introduction to the analysis of shotgun metagenomic data . Front Plant Sci 2014 ; 5 : 209. Google Scholar Crossref Search ADS PubMed 84 Head SR , Komori HK , LaMere SA , et al. Library construction for next-generation sequencing: overviews and challenges . Biotechniques 2014 ; 56 ( 2 ): 61 . Google Scholar Crossref Search ADS PubMed 85 Rintala A , Pietilä S , Munukka E , et al. Gut microbiota analysis results are highly dependent on the 16S rRNA gene target region, whereas the impact of DNA extraction is minor . J Biomol Tech 2017 ; 28 : 19 . Google Scholar PubMed 86 Olson ND , Treangen TJ , Hill CM , et al. Metagenomic assembly through the lens of validation: recent advances in assessing and improving the quality of genomes assembled from metagenomes . Brief Bioinform 2017 , doi: 10.1093/bib/bbx098. 87 Bradley RD , Hillis DM. Recombinant DNA sequences generated by PCR amplification . Mol Biol Evol 1997 ; 14 ( 5 ): 592 – 3 . http://dx.doi.org/10.1093/oxfordjournals.molbev.a025797 Google Scholar Crossref Search ADS PubMed 88 Jackman SD , Vandervalk BP , Mohamadi H , et al. ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter . Genome Res 2017 ; 27 : 768 – 77 . http://dx.doi.org/10.1101/gr.214346.116 Google Scholar Crossref Search ADS PubMed 89 Koren S , Treangen TJ , Pop M. Bambus 2: scaffolding metagenomes . Bioinformatics 2011 ; 27 ( 21 ): 2964 – 71 . http://dx.doi.org/10.1093/bioinformatics/btr520 Google Scholar Crossref Search ADS PubMed 90 Lin Y-Y , Hsieh C-H , Chen J-H , et al. De novo assembly of highly polymorphic metagenomic data using in situ generated reference sequences and a novel BLAST-based assembly pipeline . BMC Bioinformatics 2017 ; 18 ( 1 ): 223 . http://dx.doi.org/10.1186/s12859-017-1630-z Google Scholar Crossref Search ADS PubMed 91 Mysara M , Saeys Y , Leys N , et al. CATCh, an ensemble classifier for chimera detection in 16S rRNA sequencing studies . Appl Environ Microbiol 2015 ; 81 ( 5 ): 1573 – 84 . http://dx.doi.org/10.1128/AEM.02896-14 Google Scholar Crossref Search ADS PubMed 92 Haas BJ , Gevers D , Earl AM , et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons . Genome Res 2011 ; 21 ( 3 ): 494 – 504 . http://dx.doi.org/10.1101/gr.112730.110 Google Scholar Crossref Search ADS PubMed 93 Sayols S , Scherzinger D , Klein H. dupRadar: a Bioconductor package for the assessment of PCR artifacts in RNA-Seq data . BMC Bioinformatics 2016 ; 17 ( 1 ): 428.http://dx.doi.org/10.1186/s12859-016-1276-2 Google Scholar Crossref Search ADS PubMed 94 Schirmer M , D’Amore R , Ijaz UZ , et al. Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data . BMC Bioinformatics 2016 ; 17 ( 1 ): 125 . Google Scholar Crossref Search ADS PubMed 95 Peng Y , Leung HC , Yiu S-M , et al. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth . Bioinformatics 2012 ; 28 ( 11 ): 1420 – 8 . http://dx.doi.org/10.1093/bioinformatics/bts174 Google Scholar Crossref Search ADS PubMed 96 Jeraldo P , Kalari K , Chen X , et al. IM-TORNADO: a tool for comparison of 16S reads from paired-end libraries . PLoS One 2014 ; 9 ( 12 ): e114804 . Google Scholar Crossref Search ADS PubMed 97 Lai B , Wang F , Wang X , et al. InteMAP: Integrated metagenomic assembly pipeline for NGS short reads . BMC Bioinformatics 2015 ; 16 ( 1 ): 244 . http://dx.doi.org/10.1186/s12859-015-0686-x Google Scholar Crossref Search ADS PubMed 98 Mysara M , Leys N , Raes J , et al. IPED: a highly efficient denoising tool for Illumina MiSeq Paired-end 16S rRNA gene amplicon sequencing data . BMC Bioinformatics 2016 ; 17 ( 1 ): 192 . http://dx.doi.org/10.1186/s12859-016-1061-2 Google Scholar Crossref Search ADS PubMed 99 Lai B , Ding R , Li Y , et al. A de novo metagenomic assembly program for shotgun DNA reads . Bioinformatics 2012 ; 28 ( 11 ): 1455 – 62 . http://dx.doi.org/10.1093/bioinformatics/bts162 Google Scholar Crossref Search ADS PubMed 100 Parikh HI , Koparde VN , Bradley SP , et al. MeFiT: merging and filtering tool for illumina paired-end reads for 16S rRNA amplicon sequencing . BMC Bioinformatics 2016 ; 17 ( 1 ): 491 . http://dx.doi.org/10.1186/s12859-016-1358-1 Google Scholar Crossref Search ADS PubMed 101 Li D , Liu C-M , Luo R , et al. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph . Bioinformatics 2015 ; 31 ( 10 ): 1674 – 6 . http://dx.doi.org/10.1093/bioinformatics/btv033 Google Scholar Crossref Search ADS PubMed 102 Unno T. Bioinformatic suggestions on MiSeq-based microbial community analysis . J Microbiol Biotechnol 2015 ; 25 ( 6 ): 765 – 70 . http://dx.doi.org/10.4014/jmb.1409.09057 Google Scholar Crossref Search ADS PubMed 103 Treangen TJ , Koren S , Sommer DD , et al. MetAMOS: a modular and open source metagenomic assembly and analysis pipeline . Genome Biol 2013 ; 14 ( 1 ): R2 . Google Scholar Crossref Search ADS PubMed 104 Nurk S , Meleshko D , Korobeynikov A , et al. metaSPAdes: a new versatile de novo metagenomics assembler . Genome Res 2017 ; 27 : 824 – 34 . Google Scholar Crossref Search ADS PubMed 105 Namiki T , Hachiya T , Tanaka H , et al. MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads . Nucleic Acids Res 2012 ; 40 ( 20 ): e155 . Google Scholar Crossref Search ADS PubMed 106 Schloss PD , Westcott SL , Ryabin T , et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities . Appl Environ Microbiol 2009 ; 75 ( 23 ): 7537 – 41 . http://dx.doi.org/10.1128/AEM.01541-09 Google Scholar Crossref Search ADS PubMed 107 Mysara M , Leys N , Raes J , et al. NoDe: a fast error-correction algorithm for pyrosequencing amplicon reads . BMC Bioinformatics 2015 ; 16 : 88.http://dx.doi.org/10.1186/s12859-015-0520-5 Google Scholar Crossref Search ADS PubMed 108 Mysara M , Njima M , Leys N , et al. From reads to operational taxonomic units: an ensemble processing pipeline for MiSeq amplicon sequencing data . Gigascience 2017 ; 6 ( 2 ): 1 – 10 . http://dx.doi.org/10.1093/gigascience/giw017 Google Scholar Crossref Search ADS PubMed 109 Cuccuru G , Orsini M , Pinna A , et al. Orione, a web-based framework for NGS analysis in microbiology . Bioinformatics 2014 ; 30 ( 13 ): 1928 – 9 . http://dx.doi.org/10.1093/bioinformatics/btu135 Google Scholar Crossref Search ADS PubMed 110 Ruby JG , Bellare P , DeRisi JL. PRICE: software for the targeted assembly of components of (Meta) genomic sequence data . G3 2013 ; 3 : 865 – 80 . http://dx.doi.org/10.1534/g3.113.005967 Google Scholar Crossref Search ADS PubMed 111 Caporaso JG , Kuczynski J , Stombaugh J , et al. QIIME allows analysis of high-throughput community sequencing data . Nat Methods 2010 ; 7 ( 5 ): 335 – 6 . http://dx.doi.org/10.1038/nmeth.f.303 Google Scholar Crossref Search ADS PubMed 112 Okonechnikov K , Conesa A , García-Alcalde F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data . Bioinformatics 2016 ; 32 : 292 – 4 . Google Scholar PubMed 113 Boisvert S , Raymond F , Godzaridis É , et al. Ray Meta: scalable de novo metagenome assembly and profiling . Genome Biol 2012 ; 13 ( 12 ): R122 . Google Scholar Crossref Search ADS PubMed 114 Mangul S , Yang HT , Strauli N , et al. Dumpster diving in RNA-sequencing to find the source of every last read . bioRxiv 2016 :053041. 115 Hardwick SA , Chen WY , Wong T , et al. Spliced synthetic genes as internal controls in RNA sequencing experiments . Nat Methods 2016 ; 13 ( 9 ): 792 – 8 . http://dx.doi.org/10.1038/nmeth.3958 Google Scholar Crossref Search ADS PubMed 116 Pimentel H , Bray N , Puente S , et al. . Supplementary materials for “Differential analysis of RNA-Seq incorporating quantification uncertainty” . bioRxiv 2016 . 117 Gregor I , Schönhuth A , McHardy AC. Snowball: strain aware gene assembly of metagenomes . Bioinformatics 2016 ; 32 ( 17 ): i649 – 57 . Google Scholar Crossref Search ADS PubMed 118 Bolger AM , Lohse M , Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data . Bioinformatics 2014 ; 30 : 2114 – 20 . http://dx.doi.org/10.1093/bioinformatics/btu170 Google Scholar Crossref Search ADS PubMed 119 Edgar RC , Haas BJ , Clemente JC , et al. UCHIME improves sensitivity and speed of chimera detection . Bioinformatics 2011 ; 27 ( 16 ): 2194 – 200 . http://dx.doi.org/10.1093/bioinformatics/btr381 Google Scholar Crossref Search ADS PubMed 120 Rognes T , Flouri T , Nichols B , et al. VSEARCH: a versatile open source tool for metagenomics . PeerJ 2016 ; 4 : e2584. Google Scholar Crossref Search ADS PubMed 121 Wang Q , Fish JA , Gilman M , et al. Xander: employing a novel method for efficient gene-targeted metagenomic assembly . Microbiome 2015 ; 3 ( 1 ): 32 . http://dx.doi.org/10.1186/s40168-015-0093-6 Google Scholar Crossref Search ADS PubMed 122 Nawrocki EP , Kolbe DL , Eddy SR , Infernal 1. 0: inference of RNA alignments . Bioinformatics 2009 ; 25 ( 10 ): 1335 – 7 . http://dx.doi.org/10.1093/bioinformatics/btp157 Google Scholar Crossref Search ADS PubMed 123 Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads . Nat Methods 2013 ; 10 ( 10 ): 996 – 8 . http://dx.doi.org/10.1038/nmeth.2604 Google Scholar Crossref Search ADS PubMed 124 Edgar RC. Search and clustering orders of magnitude faster than BLAST . Bioinformatics 2010 ; 26 ( 19 ): 2460 – 1 . http://dx.doi.org/10.1093/bioinformatics/btq461 Google Scholar Crossref Search ADS PubMed 125 Li W , Fu L , Niu B , et al. Ultrafast clustering algorithms for metagenomic sequence analysis . Brief Bioinform 2012 ; 13 : 656 – 68 . http://dx.doi.org/10.1093/bib/bbs035 Google Scholar Crossref Search ADS PubMed 126 Caporaso JG , Bittinger K , Bushman FD , et al. PyNAST: a flexible tool for aligning sequences to a template alignment . Bioinformatics 2010 ; 26 ( 2 ): 266 – 7 . http://dx.doi.org/10.1093/bioinformatics/btp636 Google Scholar Crossref Search ADS PubMed 127 Bengtsson‐Palme J , Hartmann M , Eriksson KM , et al. METAXA2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data . Mol Ecol Resour 2015 ; 15 ( 6 ): 1403 – 14 . Google Scholar Crossref Search ADS PubMed 128 Oh J , Choi C-H , Park M-K , et al. Clustom-cloud: In-memory data grid-based software for clustering 16s rrna sequence data in the cloud environment . PLoS One 2016 ; 11 ( 3 ): e0151064 . Google Scholar Crossref Search ADS PubMed 129 Mahé F , Rognes T , Quince C , et al. Swarm v2: highly-scalable and high-resolution amplicon clustering . PeerJ 2015 ; 3 : e1420. Google Scholar Crossref Search ADS PubMed 130 Westcott SL , Schloss PD. OptiClust, an improved method for assigning amplicon-based sequence data to operational taxonomic units . mSphere 2017 ; 2 :e00073-17. 131 Al-Ghalith GA , Montassier E , Ward HN , et al. NINJA-OPS: fast accurate marker gene alignment using concatenated ribosomes . PLoS Comput Biol 2016 ; 12 ( 1 ): e1004658 . Google Scholar Crossref Search ADS PubMed 132 Alneberg J , Bjarnason BS , De Bruijn I , et al. Binning metagenomic contigs by coverage and composition . Nat Methods 2014 ; 11 ( 11 ): 1144 – 46 . http://dx.doi.org/10.1038/nmeth.3103 Google Scholar Crossref Search ADS PubMed 133 Imelfort M , Parks D , Woodcroft BJ , et al. GroopM: an automated tool for the recovery of population genomes from related metagenomes . PeerJ 2014 ; 2 : e603. Google Scholar Crossref Search ADS PubMed 134 Ulyantsev VI , Kazakov SV , Dubinkina VB , et al. MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data . Bioinformatics 2016 ; 32 : 2760 – 7 . http://dx.doi.org/10.1093/bioinformatics/btw312 Google Scholar Crossref Search ADS PubMed 135 Kang DD , Froula J , Egan R , et al. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities . PeerJ 2015 ; 3 : e1165. Google Scholar Crossref Search ADS PubMed 136 Wu Y-W , Simmons BA , Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets . Bioinformatics 2015 ; 32 : 605 – 7 . Google Scholar Crossref Search ADS PubMed 137 Laczny CC , Sternal T , Plugaru V , et al. VizBin-an application for reference-independent visualization and human-augmented binning of metagenomic data . Microbiome 2015 ; 3 ( 1 ): 1 . http://dx.doi.org/10.1186/s40168-014-0066-1 Google Scholar Crossref Search ADS PubMed 138 Lu YY , Chen T , Fuhrman JA , et al. COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment, and paired-end read LinkAge . Bioinformatics 2017 ; 33 : 791 – 8 . Google Scholar PubMed 139 Girotto S , Pizzi C , Comin M. MetaProb: accurate metagenomic reads binning based on probabilistic sequence signatures . Bioinformatics 2016 ; 32 ( 17 ): i567 – 75 . Google Scholar Crossref Search ADS PubMed 140 Cole JR , Wang Q , Fish JA , et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis . Nucleic Acids Res 2014 ; 42 : D633 – 42 . Google Scholar Crossref Search ADS PubMed 141 DeSantis TZ , Hugenholtz P , Larsen N , et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB . Appl Environ Microbiol 2006 ; 72 ( 7 ): 5069 – 72 . http://dx.doi.org/10.1128/AEM.03006-05 Google Scholar Crossref Search ADS PubMed 142 Pruesse E , Quast C , Knittel K , et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB . Nucleic Acids Res 2007 ; 35 ( 21 ): 7188 – 96 . http://dx.doi.org/10.1093/nar/gkm864 Google Scholar Crossref Search ADS PubMed 143 O'Leary NA , Wright MW , Brister JR , et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation . Nucleic Acids Res 2016 ; 44 : D733 – 45 . Google Scholar Crossref Search ADS PubMed 144 Forster SC , Browne HP , Kumar N , et al. HPMCD: the database of human microbial communities from metagenomic datasets and microbial reference genomes . Nucleic Acids Res 2016 ; 44 ( D1 ): D604 – 9 . Google Scholar Crossref Search ADS PubMed 145 Flygare S , Simmon K , Miller C , et al. Taxonomer: an interactive metagenomics analysis portal for universal pathogen detection and host mRNA expression profiling . Genome Biol 2016 ; 17 ( 1 ): 111 . http://dx.doi.org/10.1186/s13059-016-0969-1 Google Scholar Crossref Search ADS PubMed 146 Cox JW , Ballweg RA , Taft DH , et al. A fast and robust protocol for metataxonomic analysis using RNAseq data . Microbiome 2017 ; 5 ( 1 ): 7 . http://dx.doi.org/10.1186/s40168-016-0219-5 Google Scholar Crossref Search ADS PubMed 147 Gao X , Lin H , Revanna K , et al. A Bayesian taxonomic classification method for 16S rRNA gene sequences with improved species-level accuracy . BMC Bioinformatics 2017 ; 18 ( 1 ): 247 . http://dx.doi.org/10.1186/s12859-017-1670-4 Google Scholar Crossref Search ADS PubMed 148 Allard G , Ryan FJ , Jeffery IB , et al. SPINGO: a rapid species-classifier for microbial amplicon sequences . BMC Bioinformatics 2015 ; 16 : 324.http://dx.doi.org/10.1186/s12859-015-0747-1 Google Scholar Crossref Search ADS PubMed 149 Segata N , Waldron L , Ballarini A , et al. Metagenomic microbial community profiling using unique clade-specific marker genes . Nat Methods 2012 ; 9 ( 8 ): 811 – 14 . http://dx.doi.org/10.1038/nmeth.2066 Google Scholar Crossref Search ADS PubMed 150 Huson DH , Weber N. Microbial community analysis using MEGAN . Methods Enzymol 2013 ; 531 : 465 – 85 . Google Scholar Crossref Search ADS PubMed 151 Kim D , Song L , Breitwieser FP , et al. Centrifuge: rapid and sensitive classification of metagenomic sequences . Genome Res 2016 ; 26 : 1721 – 9 . http://dx.doi.org/10.1101/gr.210641.116 Google Scholar Crossref Search ADS PubMed 152 Petersen TN , Lukjancenko O , Thomsen MCF , et al. MGmapper: Reference based mapping and taxonomy annotation of metagenomics sequence reads . PLoS One 2017 ; 12 ( 6 ): e0176469 . Google Scholar Crossref Search ADS PubMed 153 Luo Y , Yu YW , Zeng J , et al. Metagenomic binning through low density hashing . bioRxiv 2017 : 133116 . 154 Henry VJ , Bandrowski AE , Pepin A-S , et al. OMICtools: an informative directory for multi-omic data analysis . Database 2014 ; 2014 : bau069. Google Scholar Crossref Search ADS PubMed 155 Comeau AM , Douglas GM , Langille MGI , Eisen J. Microbiome helper: a custom and streamlined workflow for microbiome research . mSystems 2017 ; 2 ( 1 ): e00127-16 . Google Scholar Crossref Search ADS PubMed 156 Kultima JR , Sunagawa S , Li J , et al. MOCAT: a metagenomics assembly and gene prediction toolkit . PLoS One 2012 ; 7 ( 10 ): e47656 . Google Scholar Crossref Search ADS PubMed 157 Narayanasamy S , Jarosz Y , Muller EE , et al. IMP: a pipeline for reproducible metagenomic and metatranscriptomic analyses . bioRxiv 2016 : 039263 . 158 Lin H-H , Liao Y-C. drVM: a new tool for efficient genome assembly of known eukaryotic viruses from metagenomes . Gigascience 2017 ; 6 ( 2 ): 1 – 10 . http://dx.doi.org/10.1093/gigascience/gix003 Google Scholar Crossref Search ADS 159 Broeksema B , Calusinska M , McGee F , et al. ICoVeR–an interactive visualization tool for verification and refinement of metagenomic bins . BMC Bioinformatics 2017 ; 18 ( 1 ): 233 . http://dx.doi.org/10.1186/s12859-017-1653-5 Google Scholar Crossref Search ADS PubMed 160 Kerepesi C , Bánky D , Grolmusz V. AmphoraNet: the webserver implementation of the AMPHORA2 metagenomic workflow suite . Gene 2014 ; 533 ( 2 ): 538 – 40 . Google Scholar Crossref Search ADS PubMed 161 Fosso B , Santamaria M , D’Antonio M , et al. MetaShot: an accurate workflow for taxon classification of host-associated microbiome from shotgun metagenomic data . Bioinformatics 2017 ; 33 : 1730 – 2 . Google Scholar PubMed 162 Giongo A , Crabb DB , Davis-Richardson AG , et al. PANGEA: pipeline for analysis of next generation amplicons . ISME J 2010 ; 4 ( 7 ): 852 – 61 . http://dx.doi.org/10.1038/ismej.2010.16 Google Scholar Crossref Search ADS PubMed 163 Office of Cyber Infrastructure and Computational Biology (OCICB) N . Nephele. http://nephele.niaid.nih.gov 2016 . 164 Hildebrand F , Tadeo R , Voigt AY , et al. LotuS: an efficient and user-friendly OTU processing pipeline . Microbiome 2014 ; 2 ( 1 ): 30 . http://dx.doi.org/10.1186/2049-2618-2-30 Google Scholar Crossref Search ADS PubMed 165 Turnbaugh PJ , Ley RE , Hamady M , et al. The human microbiome project: exploring the microbial part of ourselves in a changing world . Nature 2007 ; 449 ( 7164 ): 804 . http://dx.doi.org/10.1038/nature06244 Google Scholar Crossref Search ADS PubMed 166 Mitchell A , Bucchini F , Cochrane G , et al. EBI metagenomics in 2016-an expanding and evolving resource for the analysis and archiving of metagenomic data . Nucleic Acids Res 2016 ; 44 : D595 – 603 . Google Scholar Crossref Search ADS PubMed 167 Peterson J , Garges S , Giovanni M , et al. The NIH human microbiome project . Genome Res 2009 ; 19 ( 12 ): 2317 – 23 . http://dx.doi.org/10.1101/gr.096651.109 Google Scholar Crossref Search ADS PubMed 168 Markowitz VM , Chen I-MA , Palaniappan K , et al. IMG: the integrated microbial genomes database and comparative analysis system . Nucleic Acids Res 2012 ; 40 ( D1 ): D115 – 22 . Google Scholar Crossref Search ADS PubMed 169 Hurwitz B. iMicrobe: advancing clinical and environmental microbial research using the iPlant cyberinfrastructure. In: Plant and animal genome XXII conference . San Diego, CA , 2014 . 170 Meyer F , Paarmann D , D'Souza M , et al. The metagenomics RAST server–a public resource for the automatic phylogenetic and functional analysis of metagenomes . BMC Bioinformatics 2008 ; 9 : 386.http://dx.doi.org/10.1186/1471-2105-9-386 Google Scholar Crossref Search ADS PubMed 171 Hyde ER , Sanders J , Tripathi A , et al. Comparing 16S rRNA Marker Gene and Shotgun Metagenomics Datasets in the American Gut Project Using State of the Art Tools . 172 Kovalevskaya NV , Whicher C , Richardson TD , et al. DNAdigest and repositive: connecting the World of Genomic Data . PLoS Biol 2016 ; 14 : e1002418. Google Scholar Crossref Search ADS PubMed 173 Simberloff D. Properties of the rarefaction diversity measurement . Am Nat 1972 ; 106 ( 949 ): 414 – 18 . http://dx.doi.org/10.1086/282781 Google Scholar Crossref Search ADS 174 Lozupone C , Knight R. UniFrac: a new phylogenetic method for comparing microbial communities . Appl Environ Microbiol 2005 ; 71 ( 12 ): 8228 – 35 . http://dx.doi.org/10.1128/AEM.71.12.8228-8235.2005 Google Scholar Crossref Search ADS PubMed 175 Heltshe JF , Forrester NE. Estimating species richness using the jackknife procedure . Biometrics 1983 ; 39 ( 1 ): 1 – 11 . http://dx.doi.org/10.2307/2530802 Google Scholar Crossref Search ADS PubMed 176 Xiao J , Cao H , Chen J. False discovery rate control incorporating phylogenetic tree increases detection power in microbiome-wide multiple testing . Bioinformatics 2017 ; 33 : 2873 – 81 . http://dx.doi.org/10.1093/bioinformatics/btx311 Google Scholar Crossref Search ADS PubMed 177 Buttigieg PL , Ramette A. A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses . FEMS Microbiol Ecol 2014 ; 90 ( 3 ): 543 – 50 . http://dx.doi.org/10.1111/1574-6941.12437 Google Scholar Crossref Search ADS PubMed 178 Le Cao K-A , Costello M-E , Lakis VA , et al. mixMC: a multivariate statistical framework to gain insight into Microbial Communities . PLoS One 2016 ; 11 : e0160169 . Google Scholar Crossref Search ADS PubMed 179 Ramette A. Multivariate analyses in microbial ecology . FEMS Microbiol Ecol 2007 ; 62 ( 2 ): 142 – 60 . http://dx.doi.org/10.1111/j.1574-6941.2007.00375.x Google Scholar Crossref Search ADS PubMed 180 Yang Y , Chen N , Chen T. mLDM: a new hierarchical Bayesian statistical model for sparse microbioal association discovery . bioRxiv 2016 :042630. 181 Mendes-Soares H , Mundy M , Soares LM , et al. MMinte: an application for predicting metabolic interactions among the microbial species in a community . BMC Bioinformatics 2016 ; 17 : 343 . http://dx.doi.org/10.1186/s12859-016-1230-3 Google Scholar Crossref Search ADS PubMed 182 Shannon P , Markiel A , Ozier O , et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks . Genome Res 2003 ; 13 ( 11 ): 2498 – 504 . http://dx.doi.org/10.1101/gr.1239303 Google Scholar Crossref Search ADS PubMed 183 Bastian M , Heymann S , Jacomy M. Gephi: an open source software for exploring and manipulating networks . ICWSM 2009 ; 8 : 361 – 2 . 184 Vespignani A , Wasserman S , Wernert E , et al. Network Workbench Tool . 185 Segata N , Izard J , Waldron L , et al. Metagenomic biomarker discovery and explanation . Genome Biol 2011 ; 12 ( 6 ): R60 . Google Scholar Crossref Search ADS PubMed 186 Turnbaugh PJ , Ley RE , Mahowald MA , et al. An obesity-associated gut microbiome with increased capacity for energy harvest . Nature 2006 ; 444 ( 7122 ): 1027 – 31 . Google Scholar Crossref Search ADS PubMed 187 Connelly S , Bristol A , Hubert S , et al. Clinical-stage, oral β-lactamase enzyme to prevent clostridium difficile infection triggered by antibiotic-mediated gut microbiome disruption. In: Open Forum Infectious Diseases . Oxford University Press , 2016 , 2221 . 188 Alexander JL , Scott A , Mroz A , et al. 91 Mass spectrometry imaging (MSI) of microbiome-metabolome interactions in colorectal cancer . Gastroenterology 2016 ; 150 ( 4 ): S23 . Google Scholar Crossref Search ADS 189 Weir TL , Manter DK , Sheflin AM , et al. Stool microbiome and metabolome differences between colorectal cancer patients and healthy adults . PLoS One 2013 ; 8 ( 8 ): e70803 . Google Scholar Crossref Search ADS PubMed 190 Ward T , Larson J , Meulemans J , et al. BugBase predicts organism level microbiome phenotypes . bioRxiv 2017 :133462. 191 Zakrzewski M , Proietti C , Ellis JJ , et al. Calypso: a user-friendly web-server for mining and visualizing microbiome–environment interactions . Bioinformatics 2016 ; 33 : 782 – 3 . 192 Bose T , Haque MM , Reddy C , et al. COGNIZER: a framework for functional annotation of metagenomic datasets . PLoS One 2015 ; 10 ( 11 ): e0142102 . Google Scholar Crossref Search ADS PubMed 193 Vázquez-Baeza Y , Pirrung M , Gonzalez A , et al. EMPeror: a tool for visualizing high-throughput microbial community data . Gigascience 2013 ; 2 ( 1 ): 16 . Google Scholar Crossref Search ADS PubMed 194 Robertson CE , Harris JK , Wagner BD , et al. Explicet: Graphical user interface software for metadata-driven management, analysis, and visualization of microbiome data . Bioinformatics 2013 ; 29 : 3100 – 1 . http://dx.doi.org/10.1093/bioinformatics/btt526 Google Scholar Crossref Search ADS PubMed 195 Manor O , Borenstein E. Systematic characterization and analysis of the taxonomic drivers of functional shifts in the human microbiome . Cell Host Microbe 2017 ; 21 ( 2 ): 254 – 67 . http://dx.doi.org/10.1016/j.chom.2016.12.014 Google Scholar Crossref Search ADS PubMed 196 Kim J , Kim MS , Koh AY , et al. FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies . BMC Bioinformatics 2016 ; 17 ( 1 ): 420 . http://dx.doi.org/10.1186/s12859-016-1278-0 Google Scholar Crossref Search ADS PubMed 197 Rho M , Tang H , Ye Y. FragGeneScan: predicting genes in short and error-prone reads . Nucleic Acids Res 2010 ; 38 ( 20 ): e191 . Google Scholar Crossref Search ADS PubMed 198 Uchiyama T , Irie M , Mori H , et al. FuncTree: functional analysis and visualization for large-scale omics data . PLoS One 2015 ; 10 ( 5 ): e0126967 . Google Scholar Crossref Search ADS PubMed 199 Riehle K , Coarfa C , Jackson A , et al. The Genboree Microbiome Toolset and the analysis of 16S rRNA microbial sequences . BMC Bioinformatics 2012 ; 13(Suppl 13) : S11 . Google Scholar Crossref Search ADS PubMed 200 Kelley DR , Liu B , Delcher AL , et al. Gene prediction with Glimmer for metagenomic sequences augmented by classification and clustering . Nucleic Acids Res 2012 ; 40 ( 1 ): e9 . Google Scholar Crossref Search ADS PubMed 201 Asnicar F , Weingart G , Tickle TL , et al. Compact graphical representation of phylogenetic data and metadata with GraPhlAn . PeerJ 2015 ; 3 : e1029. Google Scholar Crossref Search ADS PubMed 202 Abubucker S , Segata N , Goll J , et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome . PLoS Comput Biol 2012 ; 8 ( 6 ): e1002358 . Google Scholar Crossref Search ADS PubMed 203 Narayanasamy S , Jarosz Y , Muller EE , et al. IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses . Genome Biol 2016 ; 17 : 260 . http://dx.doi.org/10.1186/s13059-016-1116-8 Google Scholar Crossref Search ADS PubMed 204 Ondov BD , Bergman NH , Phillippy AM. Interactive metagenomic visualization in a Web browser . BMC Bioinformatics 2011 ; 12 : 385.http://dx.doi.org/10.1186/1471-2105-12-385 Google Scholar Crossref Search ADS PubMed 205 Wang Y , Xu L , Gu YQ , et al. MetaCoMET: a web platform for discovery and visualization of the core microbiome . Bioinformatics 2016 ; 32 : 3469 – 70 . Google Scholar PubMed 206 Arndt D , Xia J , Liu Y , et al. METAGENassist: a comprehensive web server for comparative metagenomics . Nucleic Acids Res 2012 ; 40 : W88 – 95 . Google Scholar Crossref Search ADS PubMed 207 Wagner J , Chelaru F , Kancherla J , et al. Metaviz: interactive statistical and visual analysis of metagenomic data . bioRxiv 2017 :105205. 208 Dhariwal A , Chong J , Habib S , et al. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data . Nucleic Acids Res 2017 ; 45 : W180 – 8 . Google Scholar Crossref Search ADS PubMed 209 Kultima JR , Coelho LP , Forslund K , et al. MOCAT2: a metagenomic assembly, annotation and profiling framework . Bioinformatics 2016 ; 32 ( 16 ): 2520 – 3 . http://dx.doi.org/10.1093/bioinformatics/btw183 Google Scholar Crossref Search ADS PubMed 210 Jing G , Sun Z , Wang H , et al. Parallel-META 3: Comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities . Sci Rep 2017 ; 7 : 40371.http://dx.doi.org/10.1038/srep40371 Google Scholar Crossref Search ADS PubMed 211 Soh J , Dong X , Caffrey SM , et al. Phoenix 2: a locally installable large-scale 16S rRNA gene sequence analysis pipeline with Web interface . J Biotechnol 2013 ; 167 ( 4 ): 393 – 403 . http://dx.doi.org/10.1016/j.jbiotec.2013.07.004 Google Scholar Crossref Search ADS PubMed 212 Langille MG , Zaneveld J , Caporaso JG , et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences . Nat Biotechnol 2013 ; 31 ( 9 ): 814 – 21 . http://dx.doi.org/10.1038/nbt.2676 Google Scholar Crossref Search ADS PubMed 213 Hyatt D , Chen G-L , LoCascio PF , et al. Prodigal: prokaryotic gene recognition and translation initiation site identification . BMC Bioinformatics 2010 ; 11 : 119.http://dx.doi.org/10.1186/1471-2105-11-119 Google Scholar Crossref Search ADS PubMed 214 Lagkouvardos I , Fischer S , Kumar N , et al. Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons . PeerJ 2017 ; 5 : e2836. Google Scholar Crossref Search ADS PubMed 215 Westreich ST , Korf I , Mills DA , et al. SAMSA: a comprehensive metatranscriptome analysis pipeline . BMC Bioinformatics 2016 ; 17 ( 1 ): 399 . http://dx.doi.org/10.1186/s12859-016-1270-8 Google Scholar Crossref Search ADS PubMed 216 Kaminski J , Gibson MK , Franzosa EA , et al. High-specificity targeted functional profiling in microbial communities with ShortBRED . PLoS Comput Biol 2015 ; 11 ( 12 ): e1004557 . Google Scholar Crossref Search ADS PubMed 217 Parks DH , Tyson GW , Hugenholtz P , et al. STAMP: statistical analysis of taxonomic and functional profiles . Bioinformatics 2014 ; 30 ( 21 ): 3123 – 4 . http://dx.doi.org/10.1093/bioinformatics/btu494 Google Scholar Crossref Search ADS PubMed 218 Aßhauer KP , Wemheuer B , Daniel R , et al. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data . Bioinformatics 2015 ; 31 ( 17 ): 2882 – 4 . Google Scholar Crossref Search ADS PubMed 219 Huse SM , Welch DBM , Voorhis A , et al. VAMPS: a website for visualization and analysis of microbial population structures . BMC Bioinformatics 2014 ; 15 : 41.http://dx.doi.org/10.1186/1471-2105-15-41 Google Scholar Crossref Search ADS PubMed 220 Nagpal S , Haque MM , Mande SS , Ahmed N. Vikodak-A modular framework for inferring functional potential of microbial communities from 16S metagenomic datasets . PLoS One 2016 ; 11 ( 2 ): e0148347. Google Scholar Crossref Search ADS PubMed 221 Dray S , Dufour A-B. The ade4 package: implementing the duality diagram for ecologists . J Stat Softw 2007 ; 22 ( 4 ): 1 – 20 . Google Scholar Crossref Search ADS 222 Rodriguez-R LM , Konstantinidis KT. The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes . PeerJ Preprints 2016 ; 4 : e1900v1 . 223 Luo D , Ziebell S , An L. An informative approach on differential abundance analysis for time-course metagenomic sequencing data . Bioinformatics 2017 ; 33 : 1286 – 92 . Google Scholar Crossref Search ADS PubMed 224 Paulson JN , Stine OC , Bravo HC , Pop M. Differential abundance analysis for microbial marker-gene surveys . Nat Methods 2013 ; 10 ( 12 ): 1200 – 2 . http://dx.doi.org/10.1038/nmeth.2658 Google Scholar Crossref Search ADS PubMed 225 Zhan X , Tong X , Zhao N. A small‐sample multivariate kernel machine test for microbiome association studies . Genet Epidemiol 2017 ; 41 : 210 – 20 . Google Scholar Crossref Search ADS PubMed 226 Cao Y , Zheng X , Li F , et al. mmnet: an R package for metagenomics systems biology analysis . Biomed Res Int 2015 ; 2015 : 1 . 227 McMurdie PJ , Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data . PLoS One 2013 ; 8 ( 4 ): e61217. Google Scholar Crossref Search ADS PubMed 228 Sohn MB , Du R , An L. A robust approach for identifying differentially abundant features in metagenomic samples . Bioinformatics 2015 ; 31 : 2269 – 75 . http://dx.doi.org/10.1093/bioinformatics/btv165 Google Scholar Crossref Search ADS PubMed 229 Cao Y , Wang Y , Zheng X , et al. RevEcoR: an R package for the reverse ecology analysis of microbiomes . BMC Bioinformatics 2016 ; 17 ( 1 ): 294 . http://dx.doi.org/10.1186/s12859-016-1088-4 Google Scholar Crossref Search ADS PubMed 230 Kristiansson E , Hugenholtz P , Dalevi D. ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes . Bioinformatics 2009 ; 25 ( 20 ): 2737 – 8 . http://dx.doi.org/10.1093/bioinformatics/btp508 Google Scholar Crossref Search ADS PubMed 231 Oksanen J , Kindt R , Legendre P , et al. The vegan package . Commun Ecol Package 2007 ; 10 : 631 – 7 . 232 Cockrell C , Christley S , An G , Gabhann FM. Investigation of inflammation and tissue patterning in the gut using a spatially explicit general-purpose model of enteric tissue (SEGMEnT) . PLoS Comput Biol 2014 ; 10 ( 3 ): e1003507. Google Scholar Crossref Search ADS PubMed 233 Leber A , Viladomiu M , Hontecillas R , et al. Systems modeling of interactions between mucosal immunity and the gut microbiome during clostridium difficile infection . PLoS One 2015 ; 10 ( 7 ): e0134849 . Google Scholar Crossref Search ADS PubMed 234 Abedi V , Hontecillas R , Hoops S , et al. ENISI multiscale modeling of mucosal immune responses driven by high performance computing. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) . IEEE, 2015 , p. 680–4. 235 Collins FS , Varmus H. A new initiative on precision medicine . N Engl J Med 2015 ; 372 ( 9 ): 793 – 5 . http://dx.doi.org/10.1056/NEJMp1500523 Google Scholar Crossref Search ADS PubMed 236 Initiative PM. Working group, the precision medicine initiative cohort program: building the foundation for 21st century medicine. PMI Working Group Report to the Advisory Committee to the Director, 2015 . 237 Somberg JC. The Human Microbiome and Therapeutics . LWW , 2012 . 238 ElRakaiby M , Dutilh BE , Rizkallah MR , et al. Pharmacomicrobiomics: the impact of human microbiome variations on systems pharmacology and personalized therapeutics . Omics 2014 ; 18 ( 7 ): 402 – 14 . http://dx.doi.org/10.1089/omi.2014.0018 Google Scholar Crossref Search ADS PubMed 239 Kuntz TM , Gilbert JA. Introducing the microbiome into precision medicine . Trends Pharmacol Sci 2017 ; 38 ( 1 ): 81 – 91 . http://dx.doi.org/10.1016/j.tips.2016.10.001 Google Scholar Crossref Search ADS PubMed 240 Johnson KW , Shameer K , Glicksberg BS , et al. Enabling precision cardiology through multiscale biology and systems medicine . JACC Basic Transl Sci 2017 ; 2 ( 3 ): 311 – 27 . http://dx.doi.org/10.1016/j.jacbts.2016.11.010 Google Scholar Crossref Search ADS PubMed 241 Antman EM , Loscalzo J. Precision medicine in cardiology . Nat Rev Cardiol 2016 ; 13 ( 10 ): 591 – 602 . http://dx.doi.org/10.1038/nrcardio.2016.101 Google Scholar Crossref Search ADS PubMed 242 Hold GL. The gut microbiota, dietary extremes and exercise . Gut 2014 ; 63 ( 12 ): 1838 – 9 . http://dx.doi.org/10.1136/gutjnl-2014-307305 Google Scholar Crossref Search ADS PubMed 243 Kang SS , Jeraldo PR , Kurti A , et al. Diet and exercise orthogonally alter the gut microbiome and reveal independent associations with anxiety and cognition . Mol Neurodegener 2014 ; 9 ( 1 ): 36 . http://dx.doi.org/10.1186/1750-1326-9-36 Google Scholar Crossref Search ADS PubMed 244 Barton W , Penney NC , Cronin O , et al. The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level . Gut 2017 , doi: 10.1136/gutjnl-2016-313627. 245 Sandhu KV , Sherwin E , Schellekens H , et al. Feeding the microbiota-gut-brain axis: diet, microbiome, and neuropsychiatry . Transl Res 2017 ; 179 : 223 – 44 . http://dx.doi.org/10.1016/j.trsl.2016.10.002 Google Scholar Crossref Search ADS PubMed 246 Bokulich NA , Chung J , Battaglia T , et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life . Sci Transl Med 2016 ; 8 ( 343 ): 343ra382 . Google Scholar Crossref Search ADS 247 Preidis GA , Versalovic J. Targeting the human microbiome with antibiotics, probiotics, and prebiotics: gastroenterology enters the metagenomics era . Gastroenterology 2009 ; 136 ( 6 ): 2015 – 31 . http://dx.doi.org/10.1053/j.gastro.2009.01.072 Google Scholar Crossref Search ADS PubMed 248 Petschow B , Doré J , Hibberd P , et al. Probiotics, prebiotics, and the host microbiome: the science of translation . Ann N Y Acad Sci 2013 ; 1306 : 1 – 17 . Google Scholar Crossref Search ADS PubMed 249 Damaskos D , Kolios G. Probiotics and prebiotics in inflammatory bowel disease: microflora ‘on the scope’ . Br J Clin Pharmacol 2008 ; 65 ( 4 ): 453 – 67 . Google Scholar Crossref Search ADS PubMed 250 Schrezenmeir J , de Vrese M. Probiotics, prebiotics, and synbiotics—approaching a definition . Am J Clin Nutr 2001 ; 73(2 Suppl) : 361s – 4s . Google Scholar Crossref Search ADS 251 Ghouri YA , Richards DM , Rahimi EF , et al. Systematic review of randomized controlled trials of probiotics, prebiotics, and synbiotics in inflammatory bowel disease . Clin Exp Gastroenterol 2014 ; 7 : 473 . Google Scholar PubMed 252 Frei R , Akdis M , O’Mahony L. Prebiotics, probiotics, synbiotics, and the immune system: experimental data and clinical evidence . Curr Opin Gastroenterol 2015 ; 31 ( 2 ): 153 – 8 . Google Scholar Crossref Search ADS PubMed 253 Mehta V , Bhatt K , Desai N , et al. Probiotics: an adjuvant therapy for D-galactose induced Alzheimer's disease . J Med Res Innov 2017 ; 1 : 30 – 3 . Google Scholar Crossref Search ADS 254 Dinan TG , Stanton C , Cryan JF. Psychobiotics: a novel class of psychotropic . Biol Psychiatry 2013 ; 74 ( 10 ): 720 – 6 . http://dx.doi.org/10.1016/j.biopsych.2013.05.001 Google Scholar Crossref Search ADS PubMed 255 Wall R , Cryan JF , Ross RP , et al. Bacterial neuroactive compounds produced by psychobiotics. Microbial endocrinology: The microbiota-gut-brain axis in health and disease . Springer , 2014 , 221 – 39 . 256 Borody TJ , Khoruts A. Fecal microbiota transplantation and emerging applications . Nat Rev Gastroenterol Hepatol 2012 ; 9 : 88 – 96 . Google Scholar Crossref Search ADS 257 Smits LP , Bouter KE , de Vos WM , et al. Therapeutic potential of fecal microbiota transplantation . Gastroenterology 2013 ; 145 ( 5 ): 946 – 53 . http://dx.doi.org/10.1053/j.gastro.2013.08.058 Google Scholar Crossref Search ADS PubMed 258 Khoruts A , Weingarden AR. Emergence of fecal microbiota transplantation as an approach to repair disrupted microbial gut ecology . Immunol Lett 2014 ; 162 ( 2 ): 77 – 81 . http://dx.doi.org/10.1016/j.imlet.2014.07.016 Google Scholar Crossref Search ADS PubMed 259 Paramsothy S , Borody TJ , Lin E , et al. Donor recruitment for fecal microbiota transplantation . Inflamm Bowel Dis 2015 ; 21 ( 7 ): 1600 – 6 . http://dx.doi.org/10.1097/MIB.0000000000000405 Google Scholar Crossref Search ADS PubMed 260 Wolf‐Meyer MJ. Normal, regular, and standard: scaling the body through fecal microbial transplants . Med Anthropol Q 2016 ; 31 : 297 – 314 . Google Scholar Crossref Search ADS 261 Kang D-W , Adams JB , Gregory AC , et al. Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study . Microbiome 2017 ; 5 ( 1 ): 10 . http://dx.doi.org/10.1186/s40168-016-0225-7 Google Scholar Crossref Search ADS PubMed 262 Modi SR , Collins JJ , Relman DA. Antibiotics and the gut microbiota . J Clin Investig 2014 ; 124 ( 10 ): 4212.http://dx.doi.org/10.1172/JCI72333 Google Scholar Crossref Search ADS PubMed 263 Andersson DI. Persistence of antibiotic resistant bacteria . Curr Opin Microbiol 2003 ; 6 ( 5 ): 452 – 6 . http://dx.doi.org/10.1016/j.mib.2003.09.001 Google Scholar Crossref Search ADS PubMed 264 Zeissig S , Blumberg RS. Life at the beginning: perturbation of the microbiota by antibiotics in early life and its role in health and disease . Nat Immunol 2014 ; 15 ( 4 ): 307 – 10 . http://dx.doi.org/10.1038/ni.2847 Google Scholar Crossref Search ADS PubMed 265 Dubreuil L , Mahieux S , Neut C. Antibiotic Susceptibility of Probiotic Strains. Is it Reasonable to Combine Probiotics with Antibiotics? Gastroenterology 2017 ; 152 ( 5 ): S821. Google Scholar Crossref Search ADS 266 Sharma J , Chauhan D , Goyal A. Enhancement of antimicrobial activity of antibiotics by probiotics against Escherichia coli-An in vitro study . Adv Appl Sci Res 2014 ; 5 : 14 – 18 . 267 Adnan B , Lutvo S , Sabina K , et al. P329 Advantages to taking antibiotics with probiotics in children with reduction of complications diarrhoea . BMJ 2017 ; 102 . 268 Garrett WS. Gut microbiota in 2016: a banner year for gut microbiota research . Nat Rev Gastroenterol Hepatol 2017 ; 14 : 78 – 80 . http://dx.doi.org/10.1038/nrgastro.2016.207 Google Scholar Crossref Search ADS PubMed 269 Kantae V , Krekels EH , Van Esdonk MJ , et al. Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy . Metabolomics 2017 ; 13 ( 1 ): 9 . http://dx.doi.org/10.1007/s11306-016-1143-1 Google Scholar Crossref Search ADS PubMed 270 Enright EF , Gahan CG , Joyce SA , et al. Focus: microbiome: the impact of the gut microbiota on drug metabolism and clinical outcome . Yale J Biol Med 2016 ; 89 ( 3 ): 375 . Google Scholar PubMed 271 Koch C , Müller S. Personalized microbiome dynamics-Cytometric fingerprints for routine diagnostics . Mol Aspects Med 2017 , in press. 272 Halfvarson J , Brislawn CJ , Lamendella R , et al. Dynamics of the human gut microbiome in inflammatory bowel disease . Nat Microbiol 2017 ; 2 : 17004.http://dx.doi.org/10.1038/nmicrobiol.2017.4 Google Scholar Crossref Search ADS PubMed 273 Smith AH , Łukasik P , O'Connor MP , et al. Patterns, causes and consequences of defensive microbiome dynamics across multiple scales . Mol Ecol 2015 ; 24 ( 5 ): 1135 – 49 . Google Scholar Crossref Search ADS PubMed 274 Dorrestein PC , Mazmanian SK , Knight R. From microbiomess to metabolomes to function during host-microbial interactions . Immunity 2014 ; 40 : 824. Google Scholar Crossref Search ADS PubMed 275 von Mutius E. The shape of the microbiome in early life . Nat Med 2017 ; 23 ( 3 ): 274 – 5 . http://dx.doi.org/10.1038/nm.4299 Google Scholar Crossref Search ADS PubMed 276 Dunlop AL , Mulle JG , Ferranti EP , et al. The maternal microbiome and pregnancy outcomes that impact infant health: a review . Adv Neonat Care 2015 ; 15 ( 6 ): 377 . http://dx.doi.org/10.1097/ANC.0000000000000218 Google Scholar Crossref Search ADS 277 Zhulin IB. Databases for microbiologists . J Bacteriol 2015 ; 197 ( 15 ): 2458 – 67 . http://dx.doi.org/10.1128/JB.00330-15 Google Scholar Crossref Search ADS PubMed © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Briefings in Bioinformatics Oxford University Press

Computational profiling of the gut–brain axis: microflora dysbiosis insights to neurological disorders

Loading next page...
1
 
/lp/ou_press/computational-profiling-of-the-gut-brain-axis-microflora-dysbiosis-DEU7kNXhRC

References (288)

Publisher
Oxford University Press
Copyright
© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
ISSN
1467-5463
eISSN
1477-4054
DOI
10.1093/bib/bbx154
Publisher site
See Article on Publisher Site

Abstract

Abstract Almost 2500 years after Hippocrates’ observations on health and its direct association to the gastrointestinal tract, a paradigm shift has recently occurred, making the gut and its symbionts (bacteria, fungi, archaea and viruses) a point of convergence for studies. It is nowadays well established that the gut microflora’s compositional diversity regulates via its genes (the microbiome) the host’s health and provides preliminary insights into disease progression and regulation. The microbiome’s involvement is evident in immunological and physiological studies that link changes in its biodiversity to its contributions to the host’s phenotype but also in neurological investigations, substantiating the aptly named gut–brain axis. The definitive mechanisms of this last bidirectional interaction will be our main focus because it presents researchers with a new conundrum. In this review, we prospect current literature for computational analysis methodologies that accommodate the need for better understanding of the microbiome–gut–brain interactions and neurological disorder onset and progression, through cross-disciplinary systems biology applications. We will present bioinformatics tools used in exploring these synergies that help build and interpret microbial 16S ribosomal RNA data sets, produced by shotgun and high-throughput sequencing of healthy and neurological disorder samples stored in biological databases. These approaches provide alternative means for researchers to form hypotheses to their inquests faster, cheaper and swith precision. The goal of these studies relies on the integration of combined metagenomics and metabolomics assessments. An accurate characterization of the microbiome and its functionality can support new diagnostic, prognostic and therapeutic strategies for neurological disorders, customized for each individual host. gut–brain axis, microflora, microbiome, neurological disorders, precision medicine, computational metagenomics The host and its microflora: an interesting symbiosis The philosophical expression ‘no man is an island’ takes a whole new meaning, when one considers the fact that from the time of birth, each of us coexists with an assortment of bacteria, fungi, archaea and viruses. These ∼1014 microorganisms constitute the human microflora [1] (also known as microbiota) colonizing the skin, mouth, lungs, reproductive and gastrointestinal (GI) tract of everyone, creating a mutualistic biological interaction, a symbiosis. Especially the gut, with its physiology and large surface, acts as the perfect host environment for the microflora’s development, exhibiting the greatest diversity and abundance of bacterial populations. The composition of the human microflora, although evolving through the early stages of life and being perturbed by habitat, lifestyle, medication and health, is unique in each individual, creating a form of personal ‘fingerprint’ [2]. This evolution includes interactions between the members of the microflora fighting for ‘dominance’ among themselves. There are of course similarities across the field with bacterial phyla like Bacteroidetes, Firmicutes and Actinobacteria being present in every host [3], but the difference lies in the abundance of their subpopulations. Interestingly enough, in 2009, Turnbaugh et al. [4] observed that even though the microflora composition may vary between individuals, its core function remains the same in similar pathophysiological conditions. In recent years, the combined genetic composition of the microflora, called the microbiome, has been implicated directly with numerous aspects of human health in ways that previously were, and in many cases still remain, unknown [5]. The beneficial role of the host–microflora relationship is dependent on a semi-stable homeostasis which, when disturbed, leads to dysbiosis [6], a status inducing or signifying pathological conditions. Under homeostasis, the functional role [7] of the microbiome includes defense versus pathogens and inflammation via its interactions with the mucosa, vitamin synthesis, energy production, metabolism alteration, dietary modifications like turning fibers into short-chain fatty acids (SCFAs) while contributing to neurodevelopment [8], adult brain function [9] and longevity [10, 11]. During dysbiosis on the other hand, certain microbial populations become differentially abundant driving their metabolic contributions to follow accordingly, strongly affecting the host epigenome [12–14]. The gut microflora actively attributes to the development and maintenance of the gut immune system [15, 16], the permeability of the blood brain barrier (BBB) [17] and its imbalance has already been linked to various pathological conditions like inflammatory bowel diseases (IBDs) [18], cardiovascular conditions [19], atherosclerosis [20], diabetes [21], cancer [22], metabolic syndrome [23], human immunodeficiency virus (HIV) [24], chronic kidney disease [25], antiphospholipid syndrome [26] and most importantly for the premise of this review various neurological [27] and neuropsychiatric [28] conditions. The gut–brain–microbiome axis It has been known for a while now that the enteric nervous system acts as a kind of second ‘brain’ [29, 30] providing a bridge between the gut, the mucosal immune system, the neuroendocrine system, the autonomic nervous system, the vagus nerve and by extension the brain [31]. Previous hypotheses pointed at the brain as the instigator of this relationship trying to ‘control’ the gut, but later studies pointed at a bidirectional relationship. These observations provided the basis for the investigations of the gut–brain axis on a more advanced level revealing four distinct signaling pathways composed of neural, immunological, endocrinological and microbial communications [32]. With the newfound knowledge of the microflora’s implication in human health, the axis expanded to include the microbiome among its components forming what can be found in literature as the microbiome–gut–brain axis [33, 34]. Microbial metabolites interact with the host environment, controlling immune responses via the mucosa, reaching the brain via the bloodstream and modulating neural responses. It is clear that there is a whole ecosystem that affects the homeostasis and pathological conditions alike, via known and unknown mechanisms [35]. For example, the microbiome’s contribution to the metabolism of tryptophan, an essential amino acid for the synthesis of serotonin in the central nervous system (CNS), leads to its absorption by the gut and the crossing of the BBB [36]. The SCFAs, which are immunoregulating metabolites of gut microflora, influence microglia homeostasis and shape brain development [37]. Nitric oxide inhibition via microbial metabolites contributes to microglia maturation [14]. Recently, Bellono et al. [38] have shown that enterochromaffin cells express chemosensors that regulate serotonin-sensitive nerve fibers and establish a direct communication between the gut ecosystem and the nervous system. Current knowledge has linked the gut–brain axis to variable systematic pathological conditions like obesity [39–41], irritable bowel syndrome [42–44], upper GI disorders [45] like gastroparesis, dyspepsia and anorexia, infant colic [46] but mainly to neurological conditions affecting mental state and development, memory and behavior [47]. Clinical and preclinical studies have delved into characterizing the gut microflora dysbiosis in neurological conditions, pointing at differentially abundant microbial genera. From the early stages of life through adolescence, the gut microflora appears to influence not only normal neurological development but also the onset and/or the progression of pathological conditions like autism, schizophrenia, psychosis and bipolar disorder in both animal models and patients [48–50]. Autism spectrum disorders (ASDs), which are characterized by pathological neurodevelopment, have been linked to altered microbiome states in recent studies [51–56]. Increases of the population of bacteria of the genus Lactobacillus have been identified in patients exhibiting first episodes of psychosis and correlated positively with symptom severity (whereas Lachnospiraceae and Ruminococcaceae correlated negatively) in a study by Schwarz et al. [57]. These kinds of differences in microbial composition could possibly provide future strategies in the development of diagnostic tools for various disorders. A longitudinal study performed by Evans et al. [58] highlighted the population loss of Faecalibacterium as important in bipolar disorder, after excluding covariant factors. In 2013 Nieto et al. [59], using oral antibiotics in mice altered the gut microbial composition leading to an increase of brain-derived neurotrophic factor’s expression in the hippocampus that is implicated in cognitive impairment, morphological and functional synaptic pathology and contribution to N-methyl-D-aspartate receptor dysfunction. This dysfunction has been associated with schizophrenia. The gut–brain axis continues to shape our neurological and mental health beyond adolescence. Stress [60], insomnia [61], depression [62], anxiety [63] and even fear-related signaling [64], although not fatal in most cases, directly affect the quality of life of millions daily, regardless of age. As an example, Zheng et al. [65] in a 2016 paper, presented a four-part study, which at first tested germ-free mice and observed a reduction of depression-like symptoms prompting a microbiota–gut–brain axis involvement in depression. They, then, continued the experiment on patients exhibiting major depression disorder (MDD) versus healthy controls to find significant differences in the abundance of the bacterial phyla Firmicutes, Actinobacteria and Bacteroidetes. The third step was fecal microflora transplantation from both MDD and healthy controls to the germ-free mice, which concluded that the mice recipients of the ‘MDD microflora’ after 2 weeks showed increased depression-like and anxiety-like symptomology. Finally, by applying functional shotgun metagenomics, they investigated the metabolic effects of microbiota on ‘MDD microflora’ mice and identified several dysregulated metabolic pathways, especially those involved with carbohydrate metabolism and its function in depression. When it comes to quality of life and in some cases even mortality, strokes and progressive neurodegenerative diseases show dramatic percentages in the ageing population [36]. The microbiota–gut–brain axis has been implicated in the outcome of ischemic brain injury [66] and also in amyotrophic lateral sclerosis [67], multiple sclerosis [68], Parkinson’s [69] and Alzheimer’s disease (AD) [70]. A few months before this review, Bonfili et al. [71] using 3xTg-AD mouse models (transgenic mice with three mutations associated with familial AD) investigated the role of microflora regulation via administration of SLAB51 probiotics (a mixture of lactic acid bacteria and bifidobacteria) in the etiopathology of AD. Their experiments provided insights in regulating amyloid load, counteracting cognitive decline and brain damage, increasing gut hormone concentrations and regulating proteosomal and autophagic pathways. They calculated statistically significant microflora compositional and functional changes between wild-type and AD models, after probiotic treatment, specifically attributed to the increase in Bifidobacterium spp., the reduction in Campylobacterales and their role in inflammation via the regulation of pro-inflammatory cytokines. As evident from the above examples, preclinical and clinical studies can be enhanced significantly by bioinformatics approaches, enriching our apprehension of the microbiome’s involvement. The findings of such approaches provide a unique perspective to the composition and functional role of the microbiome, allowing researchers to theorize on dysbiosis as a cause or an effect of specific conditions and at the same time investigating the effects of intervention to the microflora (Figure 1). The next chapters of this review highlight these technology-based methodologies and provide the outline of how the insilico process formulates in microbiome studies. Figure 1 View largeDownload slide A graphical abstract of this review highlighting the gut–brain axis communication pathways, the host mechanisms the microflora regulates and some of its major perturbagens. It also presents a basic pipeline of computational analysis found in contemporary microbiome publications. Figure 1 View largeDownload slide A graphical abstract of this review highlighting the gut–brain axis communication pathways, the host mechanisms the microflora regulates and some of its major perturbagens. It also presents a basic pipeline of computational analysis found in contemporary microbiome publications. Computational metagenomic approaches In the field of metagenomics research, some fundamental questions often arise: How do we know so much about the microbiome and how did we get there so fast after decades of speculation? How exactly do we know what the microbiome is composed of? Can we identify interactions between populations of the microflora? How did we associate specific members of the microflora and their metabolic products with a diverse spectrum of health conditions? The response lies in the technological advantage, gene and next-generation sequencing (NGS) [72, 73] has provided for the uncultured microflora and the fast strides of Bioinformatics. Before delving into the functional role of microbial populations in the pathophysiology of disorders, we must be able to identify them with high sensitivity and specificity. NGS has provided to a large extend these capabilities by introducing shotgun along the 16S ribosomal RNA (rRNA) sequencing [74, 75]. The 16S rRNA gene is considered to be the de facto housekeeping gene of bacterial and archaeal populations. At this point, the first concession of studying the microbiome is introduced in the form of focusing on the bacteriome’s (bacterial microbiome) implications and often foregoing the mycobiome’s (fungal microbiome) [76, 77] and virome’s [78, 79] (viral microbiome), which both have been associated with pathological conditions but are still largely understudied. This concession is largely based on the richness (quantification of how many distinct species) and abundance (quantification of how many members the species have) of bacterial populations over those of the fungi and viruses but also on their ease of detection and better understanding of their biological processes. Metagenomics [80, 81] is the term introduced to specify the study of the metagenome, which is the combined DNA composition of environmental samples. In the case of human microflora, in fecal and histological biopsy samples, it refers to the identification and quantification of the genetic contributions of microbial subpopulations. [82]. Shotgun metagenomics, although more expensive, provide a higher resolution and accuracy of the results but those become more complex because they include all the microorganisms of a sample [83], including host DNA. 16S rRNA metagenomics, on the other hand, are more accessible and faster to achieve in a laboratory setting when the focus of the study is the bacteria and archaea in multiple control and patient samples. Both approaches use a practice that introduces an amount of variance between different studies, the utilization of NGS library construction for RNA or DNA [84]. Additionally, the 16S rRNA standard operating procedure requires another step in the library building with a fair amount of uncertainty, the amplification of hypervariable regions of the 16S rRNA gene via multiplex polymerase chain reaction (PCR) primers [85]. In both cases, whether it is the sequencing of a whole sample or of the 16S rRNA amplicons, we end up with small reads (25–500 base pairs) allowing for microorganisms who are unknown or in small abundances to be detected. These reads require extensive bioinformatics preprocessing with specialized tools for read trimming, merging, assembly, scaffolding and mapping [86]. Table 1 provides an overview of preprocessing tools and supplies information on their ability to perform: Table 1 Applications for the preprocessing of microbial sequence reads Tool Trimming, merging, scaffolding, assembly Quality contol Denoising Chimera detection Reference Abyss 2.0 ✓ [88] Bambus 2 ✓ [89] BBAP ✓ [90] CATCh ✓ [91] ChimeraSlayer ✓ [92] dupRadar ✓ [93] EP_metagenomic ✓ [94] IDBA-UD ✓ [95] IM-TORNADO ✓ ✓ ✓ ✓ [96] InteMAP ✓ [97] IPED ✓ [98] MAP ✓ [99] MeFiT ✓ [100] MEGAHIT ✓ [101] MESER ✓ [102] MetAMOS ✓ ✓ ✓ ✓ [103] metaSPAdes ✓ [104] MetaVelvet ✓ [105] mothur ✓ ✓ ✓ ✓ [106] NoDe ✓ [107] OCToPUS ✓ ✓ ✓ ✓ [108] Orione ✓ ✓ ✓ ✓ [109] PRICE ✓ [110] QIIME ✓ ✓ ✓ ✓ [111] Qualimap2 ✓ [112] Ray Meta ✓ [113] ROP ✓ [114] Sequins ✓ [115] sleuth ✓ [116] Snowball ✓ ✓ [117] Trimmomatic ✓ ✓ [118] UCHIME ✓ [119] VSEARCH ✓ [120] Xander ✓ [121] Tool Trimming, merging, scaffolding, assembly Quality contol Denoising Chimera detection Reference Abyss 2.0 ✓ [88] Bambus 2 ✓ [89] BBAP ✓ [90] CATCh ✓ [91] ChimeraSlayer ✓ [92] dupRadar ✓ [93] EP_metagenomic ✓ [94] IDBA-UD ✓ [95] IM-TORNADO ✓ ✓ ✓ ✓ [96] InteMAP ✓ [97] IPED ✓ [98] MAP ✓ [99] MeFiT ✓ [100] MEGAHIT ✓ [101] MESER ✓ [102] MetAMOS ✓ ✓ ✓ ✓ [103] metaSPAdes ✓ [104] MetaVelvet ✓ [105] mothur ✓ ✓ ✓ ✓ [106] NoDe ✓ [107] OCToPUS ✓ ✓ ✓ ✓ [108] Orione ✓ ✓ ✓ ✓ [109] PRICE ✓ [110] QIIME ✓ ✓ ✓ ✓ [111] Qualimap2 ✓ [112] Ray Meta ✓ [113] ROP ✓ [114] Sequins ✓ [115] sleuth ✓ [116] Snowball ✓ ✓ [117] Trimmomatic ✓ ✓ [118] UCHIME ✓ [119] VSEARCH ✓ [120] Xander ✓ [121] Note: These steps precede the microbial characterization (binning/OTU picking). View Large Table 1 Applications for the preprocessing of microbial sequence reads Tool Trimming, merging, scaffolding, assembly Quality contol Denoising Chimera detection Reference Abyss 2.0 ✓ [88] Bambus 2 ✓ [89] BBAP ✓ [90] CATCh ✓ [91] ChimeraSlayer ✓ [92] dupRadar ✓ [93] EP_metagenomic ✓ [94] IDBA-UD ✓ [95] IM-TORNADO ✓ ✓ ✓ ✓ [96] InteMAP ✓ [97] IPED ✓ [98] MAP ✓ [99] MeFiT ✓ [100] MEGAHIT ✓ [101] MESER ✓ [102] MetAMOS ✓ ✓ ✓ ✓ [103] metaSPAdes ✓ [104] MetaVelvet ✓ [105] mothur ✓ ✓ ✓ ✓ [106] NoDe ✓ [107] OCToPUS ✓ ✓ ✓ ✓ [108] Orione ✓ ✓ ✓ ✓ [109] PRICE ✓ [110] QIIME ✓ ✓ ✓ ✓ [111] Qualimap2 ✓ [112] Ray Meta ✓ [113] ROP ✓ [114] Sequins ✓ [115] sleuth ✓ [116] Snowball ✓ ✓ [117] Trimmomatic ✓ ✓ [118] UCHIME ✓ [119] VSEARCH ✓ [120] Xander ✓ [121] Tool Trimming, merging, scaffolding, assembly Quality contol Denoising Chimera detection Reference Abyss 2.0 ✓ [88] Bambus 2 ✓ [89] BBAP ✓ [90] CATCh ✓ [91] ChimeraSlayer ✓ [92] dupRadar ✓ [93] EP_metagenomic ✓ [94] IDBA-UD ✓ [95] IM-TORNADO ✓ ✓ ✓ ✓ [96] InteMAP ✓ [97] IPED ✓ [98] MAP ✓ [99] MeFiT ✓ [100] MEGAHIT ✓ [101] MESER ✓ [102] MetAMOS ✓ ✓ ✓ ✓ [103] metaSPAdes ✓ [104] MetaVelvet ✓ [105] mothur ✓ ✓ ✓ ✓ [106] NoDe ✓ [107] OCToPUS ✓ ✓ ✓ ✓ [108] Orione ✓ ✓ ✓ ✓ [109] PRICE ✓ [110] QIIME ✓ ✓ ✓ ✓ [111] Qualimap2 ✓ [112] Ray Meta ✓ [113] ROP ✓ [114] Sequins ✓ [115] sleuth ✓ [116] Snowball ✓ ✓ [117] Trimmomatic ✓ ✓ [118] UCHIME ✓ [119] VSEARCH ✓ [120] Xander ✓ [121] Note: These steps precede the microbial characterization (binning/OTU picking). View Large Read preprocessing Quality control, to ensure error reads, artifacts and bias are detected and corrected Denoising, to remove the noise often introduced by DNA/RNA preparation and PCR Chimera detection, to identify and remove chimeras, which are artificial recombinants formed during the PCR amplification stage [87] It is obvious that there is no clear winner on sequencing methodologies but rather a better suited for the job in front of us. The products of the sequencing process, regardless of the technology used, are distinct sequences of the microflora members of the samples reported in fasta or fastq files and a mapping file containing all the necessary metadata for the samples. These files will be the input of the next steps for the identification of the species the sequences belong to and assigning them taxonomies. Operational taxonomic unit (OTU) is a term introduced to describe clusters of similar sequences, which might represent a species. Although not necessarily flawless, this approach typically uses a 97% similarity of sequences for the clustering and leads to the selection of 1 sequence per OTU to represent the taxa it belongs to via phylogenetic alignment. Various bioinformatics approaches and algorithms exist for this process, which also known as binning, either in workflows or in individual implementations of homology- and prediction- based methods both for shotgun and 16S rRNA metagenomics. Most of these algorithms rely primarily on two specific practices and hybrid implementations of them: denovo and closed reference OTU picking for 16S rRNA data or homology-independent/dependent binning for shotgun data accordingly. Denovo OTU picking is largely based on prediction-based implementations like Infernal [122], UPARSE [123], UCLUST [124], CD-HIT [125], PyNAST [126] METAXA2 [127], CLUSTOM-CLOUD [128], SWARM [129], OptiClust [130] and NINJA-OPS [131], which when clustering do not take into account any existing database for reference sequences but rather try to construct their own phylogenetic tree and assign taxonomies to OTUs after aligning them. The same concept applies to homology-independent binning through applications like CONCOCT [132], GroopM [133], MetaFast [134], MetaBAT [135], MaxBin [136], VizBin [137], COCACOLA [138] and MetaProb [139]. This methodology is better suited when trying to identify metagenomes of habitats with largely unknown members or trying to identify pathogenic microorganisms of unknown origin. It is by far the most computationally demanding approach albeit the most accurate, as no reads are disregarded. On the contrary, when the host environment contains by large known species, like the gut microflora, a closed reference OTU picking strategy (or a homology-dependent one for shotgun data) can provide accurate results in really fast times by using algorithms, which look up reference sequences in the latest versions of databases like RDP [140], GreenGenes [141], SILVA [142], RefSeq [143], HPMCD [144], etc., and cluster the data according to their similarity with those. Implementations of this approach include Taxonomer [145], IMSA-A [146], BLCA [147] and SPINGO [148] for closed reference OTU picking, and MetaPhlAn [149], MEGAN6 [150], Centrifuge [151], MGMapper [152] and OPAL [153] for homology-dependent binning. The output of these pipelines, independent of the methodology used, is usually an OTU table, which contains all the OTUs found in a sample, how many times and their assigned taxonomy among various other metadata. The processes described above are summarized visually in Figure 2. Figure 2 View largeDownload slide 16S rRNA and shotgun metagenomics pipelines for extracting information on the host's gut microbiome. Figure 2 View largeDownload slide 16S rRNA and shotgun metagenomics pipelines for extracting information on the host's gut microbiome. Owing to the fact that different tools are required for shotgun and 16S rRNA approaches, with the help of specialized platforms for bioinformatics resource like OMICtools [154], researchers can create their own workflows to achieve results by combining applications from any of the aforementioned categories or use standardized ones like QIIME, mothur and many others [103, 106, 109, 155–164], which perform multiple tasks of data preparation and downstream analysis. It is the easiest way for scientists to acquire and analyze their microbiome data with the added benefit of creating standardized reproducible results. At this point, we should highlight the fact that metagenome bioinformatics are computationally cumbersome and require copious amounts of processing power, memory and storage but are rapidly advancing because of their rising popularity, the employment of Bioinformatics scientists and their open-source nature. It is widespread practice today for researchers to store their sequence and OTU data on online platforms after their publication to help promote knowledge of the microbiome. These platforms are in fact supported and sometimes financed by organizations and global microbiome initiatives like the Human Microbiome Project [165], whose goal is to standardize the process and disseminate the necessity of similar studies. This way we are rapidly acquiring not only the tools but also the actual data to perform evaluations between different approaches and meta-analyses to infer answers for hypotheses the original authors might not have considered. This is highly dependent on the correct metadata annotation of the stored data, constituting it crucial for reuse and repurposing. There is a variety of online solutions for metagenomics data publishing, a nonexhaustive list of which is included in Table 2. Users of these databases should take note that comparing studies or samples created via different methodologies can be problematic on principle, as the data might not be directly comparable but in need of further analysis. Table 2 Repositories containing public data sets of sequence/OTU data that can be used for metagenomics studies Database URL Description References EBI-metagenomics https://www.ebi.ac.uk/metagenomics/ Part of the European Nucleotide Archive, it offers a pipeline for raw sequence analysis and archiving of metagenomic data. The added value is the fact that users can view the analysis results of each sample [166] Human Microbiome Project Data Portal https://portal.hmpdacc.org/ Perhaps the most daunting of the databases, hmpdacc provides a way for users to browse and download data from the Human Microbiome Project. The interface is hard to navigate to find what you are looking for regarding specific conditions. The iHMP spin-off website which focuses on three specific health conditions (pregnancy, IBD and diabetes type 2) makes things a little easier just for those conditions [167] Human Pan-Microbe Community database http://www.hpmcd.org/index.php Taking an approach similar to IMG/M, HPMCD is offering comparison metagenomics based on microbial populations. The samples are based on EBI metagenomics samples [144] IMG/M https://img.jgi.doe.gov/cgi-bin/m/main.cgi The Integrated Microbial Genomes and Microbial Samples database takes a unique approach of providing microbial genomes from different studies and the ability to compare them. Perhaps not the most intuitive of the databases for reanalyses of specific conditions but rather the role of specific organisms [168] iMicrobe https://www.imicrobe.us/ iMicrobe provides an intuitive search for their data sets based on metadata, which is user-friendly. One drawback is similar to MG-RAST where whole studies cannot be downloaded at once but rather their individual samples. [169] MG-RAST http://metagenomics.anl.gov/ A constantly updated database and pipeline for NGS metagenomics. Data can be accessed via http, ftp and directly via their API. Perhaps a small drawback is the inability to download a whole study from their website something that is possible via ftp [170] QIITA https://qiita.ucsd.edu/ Web-based metagenomic database and pipeline of tools for 16S rRNA and shotgun data sets, originally created for the American Gut Project. QIITA offers data sets in various states of assembly from raw sequences to OTU tables. End user-friendly with resources, which can easily be added in a different pipeline for reanalysis [171] Repositive https://repositive.io/ Repositive is an all-purpose repository of genomic data created as a central hub for genomic data, but it contains metagenomic studies as well. Requires a free account to get started on the data [172] Database URL Description References EBI-metagenomics https://www.ebi.ac.uk/metagenomics/ Part of the European Nucleotide Archive, it offers a pipeline for raw sequence analysis and archiving of metagenomic data. The added value is the fact that users can view the analysis results of each sample [166] Human Microbiome Project Data Portal https://portal.hmpdacc.org/ Perhaps the most daunting of the databases, hmpdacc provides a way for users to browse and download data from the Human Microbiome Project. The interface is hard to navigate to find what you are looking for regarding specific conditions. The iHMP spin-off website which focuses on three specific health conditions (pregnancy, IBD and diabetes type 2) makes things a little easier just for those conditions [167] Human Pan-Microbe Community database http://www.hpmcd.org/index.php Taking an approach similar to IMG/M, HPMCD is offering comparison metagenomics based on microbial populations. The samples are based on EBI metagenomics samples [144] IMG/M https://img.jgi.doe.gov/cgi-bin/m/main.cgi The Integrated Microbial Genomes and Microbial Samples database takes a unique approach of providing microbial genomes from different studies and the ability to compare them. Perhaps not the most intuitive of the databases for reanalyses of specific conditions but rather the role of specific organisms [168] iMicrobe https://www.imicrobe.us/ iMicrobe provides an intuitive search for their data sets based on metadata, which is user-friendly. One drawback is similar to MG-RAST where whole studies cannot be downloaded at once but rather their individual samples. [169] MG-RAST http://metagenomics.anl.gov/ A constantly updated database and pipeline for NGS metagenomics. Data can be accessed via http, ftp and directly via their API. Perhaps a small drawback is the inability to download a whole study from their website something that is possible via ftp [170] QIITA https://qiita.ucsd.edu/ Web-based metagenomic database and pipeline of tools for 16S rRNA and shotgun data sets, originally created for the American Gut Project. QIITA offers data sets in various states of assembly from raw sequences to OTU tables. End user-friendly with resources, which can easily be added in a different pipeline for reanalysis [171] Repositive https://repositive.io/ Repositive is an all-purpose repository of genomic data created as a central hub for genomic data, but it contains metagenomic studies as well. Requires a free account to get started on the data [172] View Large Table 2 Repositories containing public data sets of sequence/OTU data that can be used for metagenomics studies Database URL Description References EBI-metagenomics https://www.ebi.ac.uk/metagenomics/ Part of the European Nucleotide Archive, it offers a pipeline for raw sequence analysis and archiving of metagenomic data. The added value is the fact that users can view the analysis results of each sample [166] Human Microbiome Project Data Portal https://portal.hmpdacc.org/ Perhaps the most daunting of the databases, hmpdacc provides a way for users to browse and download data from the Human Microbiome Project. The interface is hard to navigate to find what you are looking for regarding specific conditions. The iHMP spin-off website which focuses on three specific health conditions (pregnancy, IBD and diabetes type 2) makes things a little easier just for those conditions [167] Human Pan-Microbe Community database http://www.hpmcd.org/index.php Taking an approach similar to IMG/M, HPMCD is offering comparison metagenomics based on microbial populations. The samples are based on EBI metagenomics samples [144] IMG/M https://img.jgi.doe.gov/cgi-bin/m/main.cgi The Integrated Microbial Genomes and Microbial Samples database takes a unique approach of providing microbial genomes from different studies and the ability to compare them. Perhaps not the most intuitive of the databases for reanalyses of specific conditions but rather the role of specific organisms [168] iMicrobe https://www.imicrobe.us/ iMicrobe provides an intuitive search for their data sets based on metadata, which is user-friendly. One drawback is similar to MG-RAST where whole studies cannot be downloaded at once but rather their individual samples. [169] MG-RAST http://metagenomics.anl.gov/ A constantly updated database and pipeline for NGS metagenomics. Data can be accessed via http, ftp and directly via their API. Perhaps a small drawback is the inability to download a whole study from their website something that is possible via ftp [170] QIITA https://qiita.ucsd.edu/ Web-based metagenomic database and pipeline of tools for 16S rRNA and shotgun data sets, originally created for the American Gut Project. QIITA offers data sets in various states of assembly from raw sequences to OTU tables. End user-friendly with resources, which can easily be added in a different pipeline for reanalysis [171] Repositive https://repositive.io/ Repositive is an all-purpose repository of genomic data created as a central hub for genomic data, but it contains metagenomic studies as well. Requires a free account to get started on the data [172] Database URL Description References EBI-metagenomics https://www.ebi.ac.uk/metagenomics/ Part of the European Nucleotide Archive, it offers a pipeline for raw sequence analysis and archiving of metagenomic data. The added value is the fact that users can view the analysis results of each sample [166] Human Microbiome Project Data Portal https://portal.hmpdacc.org/ Perhaps the most daunting of the databases, hmpdacc provides a way for users to browse and download data from the Human Microbiome Project. The interface is hard to navigate to find what you are looking for regarding specific conditions. The iHMP spin-off website which focuses on three specific health conditions (pregnancy, IBD and diabetes type 2) makes things a little easier just for those conditions [167] Human Pan-Microbe Community database http://www.hpmcd.org/index.php Taking an approach similar to IMG/M, HPMCD is offering comparison metagenomics based on microbial populations. The samples are based on EBI metagenomics samples [144] IMG/M https://img.jgi.doe.gov/cgi-bin/m/main.cgi The Integrated Microbial Genomes and Microbial Samples database takes a unique approach of providing microbial genomes from different studies and the ability to compare them. Perhaps not the most intuitive of the databases for reanalyses of specific conditions but rather the role of specific organisms [168] iMicrobe https://www.imicrobe.us/ iMicrobe provides an intuitive search for their data sets based on metadata, which is user-friendly. One drawback is similar to MG-RAST where whole studies cannot be downloaded at once but rather their individual samples. [169] MG-RAST http://metagenomics.anl.gov/ A constantly updated database and pipeline for NGS metagenomics. Data can be accessed via http, ftp and directly via their API. Perhaps a small drawback is the inability to download a whole study from their website something that is possible via ftp [170] QIITA https://qiita.ucsd.edu/ Web-based metagenomic database and pipeline of tools for 16S rRNA and shotgun data sets, originally created for the American Gut Project. QIITA offers data sets in various states of assembly from raw sequences to OTU tables. End user-friendly with resources, which can easily be added in a different pipeline for reanalysis [171] Repositive https://repositive.io/ Repositive is an all-purpose repository of genomic data created as a central hub for genomic data, but it contains metagenomic studies as well. Requires a free account to get started on the data [172] View Large Information overload and microbiome analytics As with all -omics approaches, metagenomics is plighted by vast amounts of data which, although characterized using the techniques above, need to be analyzed, comprehended and rationalized. Apart from computers, humans also must be able to see these data in ways easily understood and offer conjecture to their involvement in human health. Certain metrics and visualization techniques were introduced with the advancement of Bioinformatics toward that goal. Most of the standardized workflows mentioned previously, like QIIME, perform analysis of the microbiome data and exportation of results in diagrams and figures. A categorization of analyses and feedback bioinformatics applications can provide us with is: Microbial community composition, hierarchy and quantitative representation (taxa abundance) These tools focus on representing which taxa are abundant and at which percentage, in the individual samples or in the sample groupings based on their metadata. Raw reads abundance percentages derive from counting the number of OTU sequences present in the samples or a comparison between them to calculate their relative abundance. Following the biological taxonomy of phylum-> class-> order-> family-> genus-> OTU (species), we visualize the microbial composition in distinct levels and even in hierarchies using phylogenetic trees, homocentric diagrams and barplots. Diversity analysis There are two basic metrics of Diversity analyses in microbial samples. α-Diversity, which represents the biodiversity of the samples (how rich a sample is in different microbial communities), and β-Diversity, which characterizes how different the composition of the microbiome in the samples is across groupings of metadata that characterize the environment (e.g. healthy controls versus patients). α-Diversity is usually calculated via rarefaction [173] and algorithms like Chao1, Shannon, etc., and represented via rarefaction or box plots, while β-diversity is predominantly calculated using UniFrac distance metrics [174] and illustrated with principal coordinates analysis plots. In the case of the latter, there is also the ability to use a jackknifing algorithm [175]. Multivariate statistical analysis of microbiome composition in correlation to sample metadata This category focuses on inferring biological associations between microbial species and specific sample groupings. It is important for researchers testing a specific hypothesis to know the differential abundance between sample groupings to see which taxa contribute in statistically significant measurements to dysbiosis. Negative binominal (DeSEQ2), RandomForest, Kruskal–Wallis, Wilcoxon rank test, analysis of variance, t-test and other parametric and non-parametric statistical tests are used to that effect. As metagenomics analysis is based on multiple testing, false discovery rate correction of the P statistical importance via algorithms like Bonferroni, Benjamini–Hochberg or the more recent StructFDR [176], which is specialized for metagenomic data, is important. Guides like GUSTA ME [177] and Statistics How to (http://www.statisticshowto.com/) offer a way for researchers to understand these statistical strategies faster to decide which one conforms to their needs. Also algorithms like MixMC [178] Pearson’s correlation heatmaps, canonical correspondence analysis, redundancy analysis, etc. [179], measure how quantitatively different the microbial composition is in different groupings and what changes researchers can expect to find while studying them. Network analysis Network metrics are engaged to detect microbial species that co-occur, are mutually exclusive or point to specific associations with the sample metadata. This helps researchers model microbial community interactions and infer relationships. Networks are visualized in their traditional node–edge form, where nodes usually represent individual taxa and edges represent their relationships. Pearson’s correlation, Spearman’s rho or the recent mLDM [180] are some of the algorithms used to calculate these relationships. Specialized network construction and analysis tools for microbe–microbe and microbe–host interactions like MMinte [181] have been created to provide a semantic point of view to the microbiome. Additionally, external all-purpose network analysis and visualization applications like Cytoscape [182], Gephi [183] and the Network Workbench Tool [184] can also be used, as many of the microbiome applications can export their constructed networks in appropriate formats. Biomarker discovery Biomarker discovery in metagenomics is the way to identify which specific microbial taxa and their combinations contribute to explanatory variables. Once again, parametric or nonparametric tests are applied to OTU tables, and their results are represented in various forms like odds ratio diagrams. These tests usually apply when one wants to compare two different states in tandem. In recent years, implementations, such as LeFSe [185], have been introduced, which can analyze multiple factors simultaneously to discover biomarkers of dysbiosis. Functional analysis of the microbiome—metabolomics Even though quantification of the microflora’s composition is important to understand the parties involved in dysbiosis and their association with pathophysiology, their actual functionality is the key for examining if they are the cause or mere casualties of disorders. As showcased earlier when talking about the gut–brain axis, microbial metabolic processes, the preeminent way of the microbes to interact with the host, play a vital role to health. Metabolomics is the large-scale study to identify and quantify metabolites, which can provide insights into the host environment during homeostasis or disease. Studies can be focused either on cellular processes that affect the microbiome by creating a nurturing or hostile environment for the microflora or on the extragenomic perturbations caused by microbial metabolites on the host. Usually, modern studies focus on the latter trying to prove or disprove correlation between certain microbial populations and host disorders. Metabolite identification can either occur by analyzing the results of traditional methods like chromatography, mass spectrometry and nuclear magnetic resonance [186–189] or by using metagenomics tools that infer the metabolic products of microbial populations via their genes. Similar to the OTU classification process, functional metagenomics require different approaches in their analysis and visualization of results. Owing to the nature of metagenomics downstream analysis tools to offer insights to multiple of the above categories, Table 3 summarizes some stand-alone implementations and R packages along with their functionalities. Most of the applications require the appropriate input of sequences or OTU tables to analyze and provide visualizations of their results. Even though Tool A might offer a wider variety of operations than Tool B and can be preferred, the truth is that most of them are interchangeable and their usage relies on scientific community adoption and subjective ease of use. Some might argue that the speed and computational requirements of some of the implementations are not subjective, and there are clear winners, but it all depends on the computational power of the end-user’s equipment. Bioinformaticians may choose to even adapt some of them to their own needs, as they are open source, and create their mix and match pipelines. What is important though, is that the interpretation of their statistical analyses, remains in the hands of the researchers and should be used properly regarding different hypotheses. Statistics by themselves if not critically viewed can lead toward skewed conclusions especially in metagenomics, where so many variables are relevant and should be considered. Some researchers might even choose to run their data through multiple applications with the same functionality to verify their findings and use each tool’s resolution and specificity to their benefit. Figure 3 also summarizes frequently asked questions, which may arise during metagenomics research and which of these categories of tools are able to provide answers to them. Table 3 Open-source implementations of microbiome downstream analysis Tool Microbial community composition, hierarchy and quantitative representation Diversity analysis Multivariate statistical analysis of microbiome composition in correlation to sample metadata Network analysis Biomarker discovery Functional analysis/ metabolomics Reference Stand-alone implementations BugBase ✓ ✓ ✓ ✓ [190] Calypso ✓ ✓ ✓ ✓ ✓ ✓ [191] COGNIZER ✓ [192] EMPeror ✓ ✓ [193] Explicet ✓ ✓ ✓ [194] FishTaco ✓ [195] FMAP ✓ [196] FragGeneScan ✓ [197] FuncTree ✓ [198] Galaxy/Hutlab N/A Genboree Microbiome Toolset ✓ ✓ ✓ ✓ [199] Glimmer-MG ✓ [200] GraPhlAn ✓ [201] HUMAnN2 ✓ [202] IMP ✓ ✓ ✓ ✓ ✓ ✓ [203] Krona ✓ [204] LEfSe ✓ ✓ [185] MEGAN6 ✓ ✓ ✓ ✓ [150] MetaCoMET ✓ ✓ ✓ [205] METAGENassist ✓ ✓ ✓ [206] MetaShot ✓ [161] Metaviz ✓ ✓ ✓ [207] MG-RAST ✓ ✓ ✓ [170] Microbiome Analyst ✓ ✓ ✓ ✓ ✓ ✓ [208] Mminte ✓ ✓ [181] MOCAT 2 ✓ ✓ [209] mothur ✓ ✓ ✓ [106] Parallel-META 3 ✓ ✓ ✓ ✓ ✓ ✓ [210] Phoenix 2 ✓ ✓ [211] PICRUSt ✓ [212] Prodigal ✓ [213] QIIME ✓ ✓ ✓ ✓ [111] Rhea ✓ ✓ ✓ [214] SAMSA ✓ [215] ShortBRED ✓ [216] STAMP ✓ ✓ ✓ ✓ [217] Tax4Fun ✓ [218] Taxonomer ✓ [145] VAMPS ✓ ✓ [219] Vikodak ✓ [220] R packages ade4 ✓ ✓ [221] enveomics ✓ ✓ ✓ [222] metaDprof ✓ ✓ [223] metagenomeSeq ✓ ✓ [224] MMiRKAT ✓ [225] mmnet ✓ ✓ ✓ [226] phyloseq ✓ ✓ ✓ ✓ [227] RAIDA ✓ [228] RevEcoR ✓ ✓ [229] ShotgunFunctionalizeR ✓ [230] vegan ✓ ✓ ✓ [231] Tool Microbial community composition, hierarchy and quantitative representation Diversity analysis Multivariate statistical analysis of microbiome composition in correlation to sample metadata Network analysis Biomarker discovery Functional analysis/ metabolomics Reference Stand-alone implementations BugBase ✓ ✓ ✓ ✓ [190] Calypso ✓ ✓ ✓ ✓ ✓ ✓ [191] COGNIZER ✓ [192] EMPeror ✓ ✓ [193] Explicet ✓ ✓ ✓ [194] FishTaco ✓ [195] FMAP ✓ [196] FragGeneScan ✓ [197] FuncTree ✓ [198] Galaxy/Hutlab N/A Genboree Microbiome Toolset ✓ ✓ ✓ ✓ [199] Glimmer-MG ✓ [200] GraPhlAn ✓ [201] HUMAnN2 ✓ [202] IMP ✓ ✓ ✓ ✓ ✓ ✓ [203] Krona ✓ [204] LEfSe ✓ ✓ [185] MEGAN6 ✓ ✓ ✓ ✓ [150] MetaCoMET ✓ ✓ ✓ [205] METAGENassist ✓ ✓ ✓ [206] MetaShot ✓ [161] Metaviz ✓ ✓ ✓ [207] MG-RAST ✓ ✓ ✓ [170] Microbiome Analyst ✓ ✓ ✓ ✓ ✓ ✓ [208] Mminte ✓ ✓ [181] MOCAT 2 ✓ ✓ [209] mothur ✓ ✓ ✓ [106] Parallel-META 3 ✓ ✓ ✓ ✓ ✓ ✓ [210] Phoenix 2 ✓ ✓ [211] PICRUSt ✓ [212] Prodigal ✓ [213] QIIME ✓ ✓ ✓ ✓ [111] Rhea ✓ ✓ ✓ [214] SAMSA ✓ [215] ShortBRED ✓ [216] STAMP ✓ ✓ ✓ ✓ [217] Tax4Fun ✓ [218] Taxonomer ✓ [145] VAMPS ✓ ✓ [219] Vikodak ✓ [220] R packages ade4 ✓ ✓ [221] enveomics ✓ ✓ ✓ [222] metaDprof ✓ ✓ [223] metagenomeSeq ✓ ✓ [224] MMiRKAT ✓ [225] mmnet ✓ ✓ ✓ [226] phyloseq ✓ ✓ ✓ ✓ [227] RAIDA ✓ [228] RevEcoR ✓ ✓ [229] ShotgunFunctionalizeR ✓ [230] vegan ✓ ✓ ✓ [231] Note: These tools use microbial sequences and/or OTU tables to extract information on the microflora’s composition and functionality. View Large Table 3 Open-source implementations of microbiome downstream analysis Tool Microbial community composition, hierarchy and quantitative representation Diversity analysis Multivariate statistical analysis of microbiome composition in correlation to sample metadata Network analysis Biomarker discovery Functional analysis/ metabolomics Reference Stand-alone implementations BugBase ✓ ✓ ✓ ✓ [190] Calypso ✓ ✓ ✓ ✓ ✓ ✓ [191] COGNIZER ✓ [192] EMPeror ✓ ✓ [193] Explicet ✓ ✓ ✓ [194] FishTaco ✓ [195] FMAP ✓ [196] FragGeneScan ✓ [197] FuncTree ✓ [198] Galaxy/Hutlab N/A Genboree Microbiome Toolset ✓ ✓ ✓ ✓ [199] Glimmer-MG ✓ [200] GraPhlAn ✓ [201] HUMAnN2 ✓ [202] IMP ✓ ✓ ✓ ✓ ✓ ✓ [203] Krona ✓ [204] LEfSe ✓ ✓ [185] MEGAN6 ✓ ✓ ✓ ✓ [150] MetaCoMET ✓ ✓ ✓ [205] METAGENassist ✓ ✓ ✓ [206] MetaShot ✓ [161] Metaviz ✓ ✓ ✓ [207] MG-RAST ✓ ✓ ✓ [170] Microbiome Analyst ✓ ✓ ✓ ✓ ✓ ✓ [208] Mminte ✓ ✓ [181] MOCAT 2 ✓ ✓ [209] mothur ✓ ✓ ✓ [106] Parallel-META 3 ✓ ✓ ✓ ✓ ✓ ✓ [210] Phoenix 2 ✓ ✓ [211] PICRUSt ✓ [212] Prodigal ✓ [213] QIIME ✓ ✓ ✓ ✓ [111] Rhea ✓ ✓ ✓ [214] SAMSA ✓ [215] ShortBRED ✓ [216] STAMP ✓ ✓ ✓ ✓ [217] Tax4Fun ✓ [218] Taxonomer ✓ [145] VAMPS ✓ ✓ [219] Vikodak ✓ [220] R packages ade4 ✓ ✓ [221] enveomics ✓ ✓ ✓ [222] metaDprof ✓ ✓ [223] metagenomeSeq ✓ ✓ [224] MMiRKAT ✓ [225] mmnet ✓ ✓ ✓ [226] phyloseq ✓ ✓ ✓ ✓ [227] RAIDA ✓ [228] RevEcoR ✓ ✓ [229] ShotgunFunctionalizeR ✓ [230] vegan ✓ ✓ ✓ [231] Tool Microbial community composition, hierarchy and quantitative representation Diversity analysis Multivariate statistical analysis of microbiome composition in correlation to sample metadata Network analysis Biomarker discovery Functional analysis/ metabolomics Reference Stand-alone implementations BugBase ✓ ✓ ✓ ✓ [190] Calypso ✓ ✓ ✓ ✓ ✓ ✓ [191] COGNIZER ✓ [192] EMPeror ✓ ✓ [193] Explicet ✓ ✓ ✓ [194] FishTaco ✓ [195] FMAP ✓ [196] FragGeneScan ✓ [197] FuncTree ✓ [198] Galaxy/Hutlab N/A Genboree Microbiome Toolset ✓ ✓ ✓ ✓ [199] Glimmer-MG ✓ [200] GraPhlAn ✓ [201] HUMAnN2 ✓ [202] IMP ✓ ✓ ✓ ✓ ✓ ✓ [203] Krona ✓ [204] LEfSe ✓ ✓ [185] MEGAN6 ✓ ✓ ✓ ✓ [150] MetaCoMET ✓ ✓ ✓ [205] METAGENassist ✓ ✓ ✓ [206] MetaShot ✓ [161] Metaviz ✓ ✓ ✓ [207] MG-RAST ✓ ✓ ✓ [170] Microbiome Analyst ✓ ✓ ✓ ✓ ✓ ✓ [208] Mminte ✓ ✓ [181] MOCAT 2 ✓ ✓ [209] mothur ✓ ✓ ✓ [106] Parallel-META 3 ✓ ✓ ✓ ✓ ✓ ✓ [210] Phoenix 2 ✓ ✓ [211] PICRUSt ✓ [212] Prodigal ✓ [213] QIIME ✓ ✓ ✓ ✓ [111] Rhea ✓ ✓ ✓ [214] SAMSA ✓ [215] ShortBRED ✓ [216] STAMP ✓ ✓ ✓ ✓ [217] Tax4Fun ✓ [218] Taxonomer ✓ [145] VAMPS ✓ ✓ [219] Vikodak ✓ [220] R packages ade4 ✓ ✓ [221] enveomics ✓ ✓ ✓ [222] metaDprof ✓ ✓ [223] metagenomeSeq ✓ ✓ [224] MMiRKAT ✓ [225] mmnet ✓ ✓ ✓ [226] phyloseq ✓ ✓ ✓ ✓ [227] RAIDA ✓ [228] RevEcoR ✓ ✓ [229] ShotgunFunctionalizeR ✓ [230] vegan ✓ ✓ ✓ [231] Note: These tools use microbial sequences and/or OTU tables to extract information on the microflora’s composition and functionality. View Large Figure 3 View largeDownload slide Common questions in metagenomics research and the specific categories of downstream analysis that can provide answers. Figure 3 View largeDownload slide Common questions in metagenomics research and the specific categories of downstream analysis that can provide answers. Finally, worth mentioning is that many commercial solutions, which in some cases come as bundles with sequencing equipment, provide similar functionality, as the tools mentioned above with the added benefit of offering training and troubleshooting support, but carrying the disadvantage of their cost. These solutions include products like ERA-7 (https://era7bioinformatics.com/), CLC Genomics Workbench (https://www.qiagenbioinformatics.com/products/clc-genomics-workbench/), Strand NGS (http://www.strand-ngs.com/) and NovoWorx (http://www.novocraft.com/products/novoworx/). Computational systems have catered to the needs of life sciences for many years now, following a parallel progress and evolution. Algorithms have been developed, applications coded and hardware constructed specifically for bioinformatics and medical informatics as demonstrated here. The goal of these efforts is to enhance research and to accommodate new and complex hypotheses that could be examined with speed and precision. Future strives will bring scientists closer to a complete modeling and emulation of the brain and the gut, allowing us to see, in silico, the machinations and evolution of the gut-brain axis even in real time. Recent strives toward that goal have shown great potential like the works of Cockrell et al. [232], Leber et al. [233], Abedi et al. [234] and others. It is our belief that these computational analyses will drive not only the identification but also the treatment of various conditions. Treating the disease, the patient or the patient–microflora complex. Will precision medicine be treating all of them? In 2015, the Precision Medicine Initiative (recently renamed to ‘All of Us’ [235, 236]) was announced by the US government to facilitate a better focus on personalized health and the type of treatment, which accounts for variability and identifies the unique features of each individual. With everything this review has shown about the microbiome and how close we are today to characterize it uniquely for everyone, because of our achievements in bioinformatics, we believe that the parallelism with this initiative is clear. If we are to talk about a person’s diagnosis, prognosis and therapy, it seems almost imperative to consider the whole microflora–host system. It is the entire system that suffers and, perhaps therein, lies the correct course of treatment or the necessary diagnostic and prognostic biomarkers. After all the microbiome has been implicated in regulating pharmacokinetics, pharmacodynamics and driving pharmacogenetics [237–239], providing added value to our investigations of drug metabolism and response. Exercise, diet and a lifestyle away from sedentary conditions have long been known to promote health for assorted reasons especially concerning the cardiovascular system [240, 241]. Today, we know that these factors perturb the gut’s microflora [242–244], driving the homeostasis and by extension the systemic health. Our diet and our medication regiment regulate our microflora’s composition in a larger scale, by adding new microorganisms or creating a hostile environment for others, affecting, among other systems, our gut–brain axis [245, 246]. By using the wisdom acquired via the downstream analysis of the microbiome, we can discuss targeted practices of diet and antibiotic usage, customized for everyone according to their microbial profile. It is an innovative approach to the well-known expression ‘We are what we eat’. There is also a special category of intervention, which includes probiotics, prebiotics and synbiotics that can influence the microflora, can be used as treatment for various conditions and have been the focus of many studies [247–252]. The terms, although popular in literature and gaining popularity in everyday life, are not well understood by the public. Probiotics are live organisms (bacteria, yeasts, etc.) that can supplement a person’s microflora when they are introduced in their diet. Prebiotics are ingredients that help specific microorganisms, already introduced to the organism, flourish and fight off pathogens and/or reach the appropriate numbers for dysbiosis. Finally, synbiotics are a mixture of the previous two groups. Owing to their mechanism of action, these dietary supplements can be used to target specific populations, which the current insights into dysbiosis have already identified by methods such as the ones described previously in this review. For example, Mehta et al. [253] have proposed the usage of lactic acid bacteria probiotics in reducing the oxidative stress implicated in AD, by suppressing D-galactose, which is implicated in increased reactive oxygen species production and nerve growth factor suppression. Finally, in recent years, a new term emerged, psychobiotics [254], which refers to living organisms (gut bacteria) introduced in the host’s system to treat mental disorders. Their method of action targets specifically the gut–brain axis via the neurotropic metabolic products of these microorganisms [255]. Although probiotics and prebiotics may be valuable additions to a personalized treatment regimen, they rely on daily consumption to be useful and contribute to homeostasis. In the past few years, the more targeted and permanent solution of fecal microbiota transplantation [256–258] has been successfully deployed to help the host’s microflora to be repopulated by ‘healthy’ symbionts. Based on what we know, for a transplantation to be successful a plethora of cofounding factors must be considered. What can be deemed as ‘healthy’ donor and ‘normal’ microflora? Is a transplant from someone living in the United States appropriate for someone in Asia? Considering location and different lifestyles, we must rely on our knowledge of the functional role of the microbiome as discussed previously. Also is the host’s lack of clinical symptomology enough to consider a transplant ‘healthy’ or do we have to test for ‘dormant’ GI pathogens [259]? Is fecal material a reliable source of microflora, as it can change constantly because of external factors [260]? Despite of the many difficulties, recent studies have shown promise in treating a variety of pathological conditions, including neuropsychological ones. For example, microflora transplantation has been successful in alleviating autism symptomology in a recent study by Kang et al. [261] where ASD-related behavior was improved by 22% following transplantation and up to 24% in a 8-week period after that (according to the Childhood Autism Rating Scale). Treating the microflora is not the only thing one must consider when trying to combat dysbiosis. One of the major reasons of microbial population loss is the broad usage of antibiotics [262]. Although critical for our health, the extended usage of these drugs has caused some issues going beyond the creation of antibiotic resistant bacteria [263]. Especially during early life, antibiotics can help combat pathogens introduced into the host but are also responsible for dysbiosis [264]. Once more, the need for targeted precision antibiotics comes into the foreground requiring an extensive understanding of their implications to the microflora synthesis and how populations vital to homeostasis can be spared. Complimenting antibacterial treatments with probiotics, which are not susceptible to the antibiotics themselves [265], can prove useful for customized approaches to the needs of patients [266, 267]. In the past 5 years, the microbiome has seen a significant boost in scientific interest and publications. A relative term search (microbiome, microbiota, microflora), in PubMed alone, yields over 35 000 results for just this period with an exponential growth each passing year. Some researchers [268] have even characterized the year 2016 as the ‘banner year’ for microbiome research, something that can be directly attributed to the bioinformatics approaches at our disposal and the constant flow of information linking it to systemic health. This shift toward a better understanding of all the mechanisms describing and being perturbed by our microbiome is driven by our need to be able to better understand the host–microflora relationship. The acquisition of this knowledge can lead, not only in more precise definition of the pathophysiological attributes of disorders but also to the customization of treatment for individuals or specific patient groups. Several aspects of today’s medicine are being driven by genomics, proteomics, epigenomics, metabolomics, microbiomics and their integration via systems biology, allowing researchers to accurately predict the onset, progression and pharmacological response of a pathological condition [269, 270]. Scientists are now able not only to precisely identify and evaluate the microbiome but also track its changes and the ones it provokes through time, dynamically tracking bacterial population abundance differences and metabolite production [271–273]. The complexity of the gut–brain–microbiome axis makes for an interesting target for the application of our research efforts and a perfect candidate to be supported by integrated multidisciplinary approaches [274]. As the embryonic stage via our maternal microbiome and developing rapidly in the first 3–5 years of life [275, 276], our microbial partners help shape the development of our CNS and behavior. During our lifespan, the gut microbiome contributes toward neurological and mental health. The cross talk between the microflora ecology and the host’s physiology is based on interactions on a genetic, protein and metabolic level for both sides involved. The studies previously mentioned in this review highlight the gut microbiome as a modulator of brain development and neurotransmitter signaling systems but also as a mediator of neurological, mental and behavioral function in adults. We are confronted with vast networks of signals and interactions, in which we are called to identify the essential components for homeostasis and understand what perturbations are applied by dysbiosis. It is important in a dynamic ecosystem that research will be focused on the factors that drive permanent or reversible changes that are essential in a variety of functions and their involvement in molecular mechanisms. These fundamental biological mechanisms can be explored via novel high-throughput computational methodologies that combine and analyze the evolution of the microbial communities and their genetic composition, microbial–host biological systems interaction and the effects of external environmental factors on the microbial–host ecosystem. More specifically, computational metagenomics cross-analysis and host genetic susceptibility/genomic background will provide new insights into the onset and progression of CNS disease. In addition, the characterization and quantification of the genomic composition of the microbiome under different environmental factors can provide information of the microbiome’s role as a cause or effect of disease, something that is currently under investigation. Translating the biological networks into computational ones, which include host-omics, meta-omics and related phenotypes in tandem, we can construct prediction models that can reveal valuable information on metabolic and other molecular components as well as signaling pathways mediated in brain health and disease. The development of new combinational databases [277], which proliferate the knowledge derived by our research, will help to make it accessible and usable by other investigators. These novel bioinformatics avenues lead to a better understanding of neurological and mental disease by pinpointing the modifiable factors that influence the microbiome and act as regulators of health. The outcome of this knowledge can be new therapeutic strategies that complement a possible prognostic and diagnostic role of the gut microbiome, in medicine at a personalized as well as a general population level. Key Points Gut–brain axis is a complex communication system mediating human health. Microflora–gut–brain axis is based on a bidirectional relationship. Shotgun and 16S rRNA sequencing precision is essential for our data. Computational downstream analysis of the microbiome provides answers regarding its composition and function. Microbiome research could offer a novel approach to precision medicine. Funding G.M.S. holds the Bioinformatics ERA Chair position funded by the European Commission Research Executive Agency (REA) Grant BIORISE (grant number 669026), under the Spreading Excellence, Widening Participation, Science with and for Society Framework. Nikolas Dovrolis is a PhD candidate of Pharmacology at the Democritus University of Thrace. He is a Computer Science graduate with a Master’s Degree in Molecular Biology and Genetics. George Kolios, MD, PhD, is a Professor of Pharmacology at Democritus University Thrace, Greece. He is a clinical Gastroenterologist, with extensive research in mucosal immunology, focused on intestinal inflammation and microbiota. George M. Spyrou, PhD, holds the Bioinformatics ERA Chair and is the Head of the Bioinformatics Group at the Cyprus Institute of Neurology and Genetics. Ioanna Maroulakou, PhD, is a Professor of Genetics at Democritus University of Thrace and has extensive experience and expertise in Translational Research and Acquired genetic disorders including neurodegenerative diseases. References 1 Berg RD. The indigenous gastrointestinal microflora . Trends Microbiol 1996 ; 4 ( 11 ): 430 – 5 . http://dx.doi.org/10.1016/0966-842X(96)10057-3 Google Scholar Crossref Search ADS PubMed 2 Franzosa EA , Huang K , Meadow JF. Identifying personal microbiomes using metagenomic codes . Proc Natl Acad Sci USA 2015 ; 112 ( 22 ): E2930 – 8 . Google Scholar Crossref Search ADS PubMed 3 Sekirov I , Russell SL , Antunes LCM , et al. Gut microbiota in health and disease . Physiol Rev 2010 ; 90 ( 3 ): 859 – 904 . http://dx.doi.org/10.1152/physrev.00045.2009 Google Scholar Crossref Search ADS PubMed 4 Turnbaugh PJ , Hamady M , Yatsunenko T , et al. A core gut microbiome in obese and lean twins . Nature 2009 ; 457 ( 7228 ): 480 – 4 . http://dx.doi.org/10.1038/nature07540 Google Scholar Crossref Search ADS PubMed 5 Levy M , Blacher E , Elinav E. Microbiome, metabolites and host immunity . Curr Opin Microbiol 2017 ; 35 : 8 – 15 . http://dx.doi.org/10.1016/j.mib.2016.10.003 Google Scholar Crossref Search ADS PubMed 6 Carding S , Verbeke K , Vipond DT , et al. Dysbiosis of the gut microbiota in disease . Microb Ecol Health Dis 2015 ; 26 ( 0 ):. 7 Flint HJ , Scott KP , Louis P , et al. The role of the gut microbiota in nutrition and health . Nat Rev Gastroenterol Hepatol 2012 ; 9 ( 10 ): 577 – 89 . http://dx.doi.org/10.1038/nrgastro.2012.156 Google Scholar Crossref Search ADS PubMed 8 Tognini P. Gut microbiota: a potential regulator of neurodevelopment . Front Cell Neurosci 2017 ; 11 : 25 . Google Scholar Crossref Search ADS PubMed 9 Rogers G , Keating D , Young R , et al. From gut dysbiosis to altered brain function and mental illness: mechanisms and pathways . Mol Psychiatry 2016 ; 21 ( 6 ): 738 – 48 . http://dx.doi.org/10.1038/mp.2016.50 Google Scholar Crossref Search ADS PubMed 10 Gruber J , Kennedy BK. Microbiome and longevity: gut microbes send signals to host mitochondria . Cell 2017 ; 169 ( 7 ): 1168 – 9 . http://dx.doi.org/10.1016/j.cell.2017.05.048 Google Scholar Crossref Search ADS PubMed 11 Han B , Sivaramakrishnan P , Lin C-CJ , et al. Microbial genetic composition tunes host longevity . Cell 2017 ; 169 ( 7 ): 1249 – 62.e1213 . Google Scholar Crossref Search ADS PubMed 12 Lee E-S , Song E-J , Nam Y-D. Dysbiosis of gut microbiome and its impact on epigenetic regulation . J Clin Epigene 2017 , in press. 13 Krautkramer KA , Kreznar JH , Romano KA , et al. Diet-microbiota interactions mediate global epigenetic programming in multiple host tissues . Mol Cell 2016 ; 164 : 982 – 92 . Google Scholar Crossref Search ADS 14 Tse JKY. Gut microbiota, nitric oxide and microglia as pre-requisites for neurodegenerative disorders . ACS Chem Neurosci 2017 ; 8 : 1438 – 47 . http://dx.doi.org/10.1021/acschemneuro.7b00176 Google Scholar Crossref Search ADS PubMed 15 Round JL , Mazmanian SK. The gut microbiota shapes intestinal immune responses during health and disease . Nat Rev Immunol 2009 ; 9 ( 5 ): 313 – 23 . http://dx.doi.org/10.1038/nri2515 Google Scholar Crossref Search ADS PubMed 16 Zmora N , Bashiardes S , Levy M , et al. The role of the immune system in metabolic health and disease . Cell Metab 2017 ; 25 ( 3 ): 506 – 21 . http://dx.doi.org/10.1016/j.cmet.2017.02.006 Google Scholar Crossref Search ADS PubMed 17 Braniste V , Al-Asmakh M , Kowal C , et al. The gut microbiota influences blood-brain barrier permeability in mice . Sci Transl Med 2014 ; 6 ( 263 ): 263ra158 . Google Scholar Crossref Search ADS PubMed 18 Holleran G , Lopetuso L , Ianiro G , et al. Gut microbiota and inflammatory bowel disease: an update . Minerva Gastroenterol Dietol 2017 ; 63 : 373 – 84 . Google Scholar PubMed 19 Tang WW , Hazen SL. The gut microbiome and its role in cardiovascular diseases . Circulation 2017 ; 135 ( 11 ): 1008 – 10 . http://dx.doi.org/10.1161/CIRCULATIONAHA.116.024251 Google Scholar Crossref Search ADS PubMed 20 Drosos I , Tavridou A , Kolios G. New aspects on the metabolic role of intestinal microbiota in the development of atherosclerosis . Metabolism 2015 ; 64 ( 4 ): 476 – 81 . http://dx.doi.org/10.1016/j.metabol.2015.01.007 Google Scholar Crossref Search ADS PubMed 21 Stefanaki C , Peppa M , Mastorakos G , et al. Examining the gut bacteriome, virome, and mycobiome in glucose metabolism disorders: are we on the right track? Metabolism 2017 ; 73 : 52 – 66 . Google Scholar Crossref Search ADS PubMed 22 Bhutia YD , Ogura J , Sivaprakasam S , et al. Gut microbiome and colon cancer: role of bacterial metabolites and their molecular targets in the host . Curr Colorectal Cancer Rep 2017 ; 13 ( 2 ): 111 – 18 . http://dx.doi.org/10.1007/s11888-017-0362-9 Google Scholar Crossref Search ADS PubMed 23 Bouter KE , van Raalte DH , Groen AK , et al. Role of the gut microbiome in the pathogenesis of obesity and obesity-related metabolic dysfunction . Gastroenterology 2017 ; 13 : 111 – 18 . 24 Liu J , Williams B , Frank D , et al. Inside out: HIV, the gut microbiome, and the mucosal immune system . J Immunol 2017 ; 198 ( 2 ): 605 – 14 . http://dx.doi.org/10.4049/jimmunol.1601355 Google Scholar Crossref Search ADS PubMed 25 Nallu A , Sharma S , Ramezani A , et al. Gut microbiome in chronic kidney disease: challenges and opportunities . Transl Res 2017 ; 179 : 24 – 37 . http://dx.doi.org/10.1016/j.trsl.2016.04.007 Google Scholar Crossref Search ADS PubMed 26 Ruff WE , Vieira SM , Kriegel MA. The role of the gut microbiota in the pathogenesis of antiphospholipid syndrome . Curr Rheumatol Rep 2015 ; 17 ( 1 ): 472.http://dx.doi.org/10.1007/s11926-014-0472-1 Google Scholar Crossref Search ADS PubMed 27 Wang Y , Kasper LH. The role of microbiome in central nervous system disorders . Brain Behav Immun 2014 ; 38 : 1 – 12 . http://dx.doi.org/10.1016/j.bbi.2013.12.015 Google Scholar Crossref Search ADS PubMed 28 Dinan TG , Cryan JF. The impact of gut microbiota on brain and behaviour: implications for psychiatry . Curr Opin Clin Nutr Metabol Care 2015 ; 18 ( 6 ): 552 – 8 . http://dx.doi.org/10.1097/MCO.0000000000000221 Google Scholar Crossref Search ADS 29 Gershon M. The Second Brain: A Groundbreaking New Understanding of Nervous Disorders of the Stomach and Intestine . Harper Collins , New York , 1999 . 30 Furness JB , Costa M , The enteric nervous system . Churchill Livingstone Edinburgh etc ., 1987 . 31 Furness JB. The enteric nervous system and neurogastroenterology . Nat Rev Gastroenterol Hepatol 2012 ; 9 ( 5 ): 286 – 94 . http://dx.doi.org/10.1038/nrgastro.2012.32 Google Scholar Crossref Search ADS PubMed 32 Holzer P , Farzi A , Neuropeptides and the microbiota-gut-brain axis. In: Microbial Endocrinology: The Microbiota-Gut-Brain Axis in Health and Disease . Springer , New York , 2014 , 195 – 219 . 33 Cryan JF , O'Mahony SM. The microbiome‐gut‐brain axis: from bowel to behavior . Neurogastroenterol Motil 2011 ; 23 ( 3 ): 187 – 92 . Google Scholar Crossref Search ADS PubMed 34 Bauer KC , Huus KE , Finlay BB. Microbes and the mind: emerging hallmarks of the gut microbiota–brain axis . Cell Microbiol 2016 ; 18 ( 5 ): 632 – 44 . http://dx.doi.org/10.1111/cmi.12585 Google Scholar Crossref Search ADS PubMed 35 Sampson TR , Mazmanian SK. Control of brain development, function, and behavior by the microbiome . Cell Host Microbe 2015 ; 17 ( 5 ): 565 – 76 . http://dx.doi.org/10.1016/j.chom.2015.04.011 Google Scholar Crossref Search ADS PubMed 36 Le Floc’h N , Otten W , Merlot E. Tryptophan metabolism, from nutrition to potential therapeutic applications . Amino Acids 2011 ; 41 ( 5 ): 1195 – 205 . Google Scholar Crossref Search ADS PubMed 37 Erny D , Hrabě de Angelis AL , Jaitin D , et al. Host microbiota constantly control maturation and function of microglia in the CNS . Nat Neurosci 2015 ; 18 ( 7 ): 965 – 77 . Google Scholar Crossref Search ADS PubMed 38 Bellono NW , Bayrer JR , Leitch DB , et al. Enterochromaffin cells are gut chemosensors that couple to sensory neural pathways . Cell 2017 ; 170 : 185 – 98.e16 . Google Scholar Crossref Search ADS PubMed 39 Greathouse KL , Faucher MA , Hastings-Tolsma M. The gut microbiome, obesity, and weight control in women‘s reproductive health . West J Nurs Res 2017 ; 39 : 1094 – 119 . Google Scholar Crossref Search ADS PubMed 40 Komaroff AL. The microbiome and risk for obesity and diabetes . Jama 2017 ; 317 ( 4 ): 355 – 6 . http://dx.doi.org/10.1001/jama.2016.20099 Google Scholar Crossref Search ADS PubMed 41 Sanmiguel CP , Jacobs J , Gupta A , et al. Surgically induced changes in gut microbiome and hedonic eating as related to weight loss: preliminary findings in obese women undergoing bariatric surgery . Psychosomatic Med 2017 ; 79 : 880 – 7 . http://dx.doi.org/10.1097/PSY.0000000000000494 Google Scholar Crossref Search ADS 42 Tap J , Derrien M , Törnblom H , et al. Identification of an intestinal microbiota signature associated with severity of irritable bowel syndrome . Gastroenterology 2017 ; 152 ( 1 ): 111 – 23. e118 . Google Scholar Crossref Search ADS PubMed 43 Ringel Y. The gut microbiome in irritable bowel syndrome and other functional bowel disorders . Gastroenterol Clin N Am 2017 ; 46 ( 1 ): 91 – 101 . http://dx.doi.org/10.1016/j.gtc.2016.09.014 Google Scholar Crossref Search ADS 44 Mahurkar-Joshi S , Labus JS , Jacobs J , et al. 143-Colonic mucosal microbiome is associated with mucosal microrna expression in irritable bowel syndrome . Gastroenterology 2017 ; 152 ( 5 ): S40 – 1 . Google Scholar Crossref Search ADS 45 Sanger GJ , Lee K. Hormones of the gut-brain axis as targets for the treatment of upper gastrointestinal disorders . Nat Rev Drug Discov 2008 ; 7 ( 3 ): 241.http://dx.doi.org/10.1038/nrd2444 Google Scholar Crossref Search ADS PubMed 46 Pärtty A , Kalliomäki M. Infant colic is still a mysterious disorder of the microbiota–gut–brain axis . Acta Paediatrica 2017 ; 106 ( 4 ): 528 – 9 . Google Scholar Crossref Search ADS PubMed 47 Tremlett H , Bauer KC , Appel‐Cresswell S , et al. The gut microbiome in human neurological disease: a review . Ann Neurol 2017 ; 81 : 369 – 82 . Google Scholar Crossref Search ADS PubMed 48 Yang I , Corwin EJ , Brennan PA , et al. The infant microbiome: implications for infant health and neurocognitive development . Nurs Res 2016 ; 65 ( 1 ): 76 – 88 . http://dx.doi.org/10.1097/NNR.0000000000000133 Google Scholar Crossref Search ADS PubMed 49 Sharon G , Sampson TR , Geschwind DH , et al. The central nervous system and the gut microbiome . Cell 2016 ; 167 ( 4 ): 915 – 32 . http://dx.doi.org/10.1016/j.cell.2016.10.027 Google Scholar Crossref Search ADS PubMed 50 Desbonnet L , Clarke G , Traplin A , et al. Gut microbiota depletion from early adolescence in mice: Implications for brain and behaviour . Brain Behav Immun 2015 ; 48 : 165 – 73 . http://dx.doi.org/10.1016/j.bbi.2015.04.004 Google Scholar Crossref Search ADS PubMed 51 Li Q , Han Y , Dy ABC , et al. The gut microbiota and autism spectrum disorders . Front Cell Neurosci 2017 ; 11 : 120 . http://dx.doi.org/10.3389/fncel.2017.00120 Google Scholar Crossref Search ADS PubMed 52 Braun J. Tightening the Case for Gut Microbiota in Autism-Spectrum Disorder . Elsevier , 2017 . 53 Ding HT , Taur Y , Walkup JT. Gut microbiota and autism: key concepts and findings . J Autism Dev Disord 2016 ; 47 : 480 – 9 . Google Scholar Crossref Search ADS 54 Strati F , Cavalieri D , Albanese D , et al. New evidences on the altered gut microbiota in autism spectrum disorders . Microbiome 2017 ; 5 ( 1 ): 24 . http://dx.doi.org/10.1186/s40168-017-0242-1 Google Scholar Crossref Search ADS PubMed 55 Gogou M , Gogou C. The effect of intestinal microbiome on autism spectrum disorder . J Pediatr Sci 2016 ; 8 ( 0 ). 56 Vuong HE , Hsiao EY. Emerging roles for the gut microbiome in autism spectrum disorder . Biol Psychiatry 2017 ; 81 ( 5 ): 411 – 23 . http://dx.doi.org/10.1016/j.biopsych.2016.08.024 Google Scholar Crossref Search ADS PubMed 57 Schwarz E , Maukonen J , Hyytiäinen T , et al. Analysis of microbiota in first episode psychosis identifies preliminary associations with symptom severity and treatment response . Schizophr Res 2017 , doi: 10.1016/j.schres.2017.04.017. 58 Evans SJ , Bassis CM , Hein R , et al. The gut microbiome composition associates with bipolar disorder and illness severity . J Psychiatr Res 2017 ; 87 : 23 – 9 . http://dx.doi.org/10.1016/j.jpsychires.2016.12.007 Google Scholar Crossref Search ADS PubMed 59 Nieto R , Kukuljan M , Silva H. BDNF and schizophrenia: from neurodevelopment to neuronal plasticity, learning, and memory . Front Psychiatry 2013 ; 4 : 45 . Google Scholar Crossref Search ADS PubMed 60 Marin IA , Goertz JE , Ren T , et al. Microbiota alteration is associated with the development of stress-induced despair behavior . Sci Rep 2017 ; 7 : 43859.http://dx.doi.org/10.1038/srep43859 Google Scholar Crossref Search ADS PubMed 61 Lothian J , Blampied NM , Rucklidge JJ. Effect of micronutrients on insomnia in adults a multiple-baseline study . Clin Psychol Sci 2016 ; 4 ( 6 ): 2167702616631740. Google Scholar Crossref Search ADS 62 D’Mello C , Swain MG. Immune-to-brain communication pathways in inflammation-associated sickness and depression. In: Inflammation-Associated Depression: Evidence, Mechanisms and Implications . Springer , Switzerland , 2017 : 73 – 94 . 63 MacQueen G , Surette M , Moayyedi P. The gut microbiota and psychiatric illness . J Psychiatry Neurosci 2017 ; 42 ( 2 ): 75.http://dx.doi.org/10.1503/jpn.170028 Google Scholar Crossref Search ADS PubMed 64 Hoban A , Stilling R , Moloney G , et al. The microbiome regulates amygdala-dependent fear recall . Mol Psychiatry 2017 , doi: 10.1038/mp.2017.100. 65 Zheng P , Zeng B , Zhou C , et al. Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host‘s metabolism . Mol Psychiatry 2016 ; 21 ( 6 ): 786 – 96 . Google Scholar Crossref Search ADS PubMed 66 Benakis C , Brea D , Caballero S , et al. Commensal microbiota affects ischemic stroke outcome by regulating intestinal [gamma][delta] T cells . Nat Med 2016 ; 22 : 516 – 23 . http://dx.doi.org/10.1038/nm.4068 Google Scholar Crossref Search ADS PubMed 67 Zhang Y-g , Wu S , Yi J , et al. Target intestinal microbiota to alleviate disease progression in amyotrophic lateral sclerosis . Clin Ther 2017 ; 39 : 322 – 36 . http://dx.doi.org/10.1016/j.clinthera.2016.12.014 Google Scholar Crossref Search ADS PubMed 68 Mirza A , Mao-Draayer Y. The gut microbiome and microbial translocation in multiple sclerosis . Clin Immunol 2017 , doi: 10.1016/j.clim.2017.03.001. 69 Hill‐Burns EM , Debelius JW , Morton JT , et al. Parkinson's disease and Parkinson's disease medications have distinct signatures of the gut microbiome . Mov Disord 2017 ; 32 : 739 – 49 . Google Scholar Crossref Search ADS PubMed 70 Pistollato F , Sumalla Cano S , Elio I , et al. Role of gut microbiota and nutrients in amyloid formation and pathogenesis of Alzheimer disease . Nutr Rev 2016 ; 74 ( 10 ): 624 – 34 . http://dx.doi.org/10.1093/nutrit/nuw023 Google Scholar Crossref Search ADS PubMed 71 Bonfili L , Cecarini V , Berardi S , et al. Microbiota modulation counteracts Alzheimer‘s disease progression influencing neuronal proteolysis and gut hormones plasma levels . Sci Rep 2017 ; 7 ( 1 ): 2426 . Google Scholar Crossref Search ADS PubMed 72 Schuster SC. Next-generation sequencing transforms today's biology . Nat Methods 2008 ; 5 ( 1 ): 16.http://dx.doi.org/10.1038/nmeth1156 Google Scholar Crossref Search ADS PubMed 73 Metzker ML. Sequencing technologies—the next generation . Nat Rev Genet 2010 ; 11 ( 1 ): 31 – 46 . Google Scholar Crossref Search ADS PubMed 74 Jovel J , Patterson J , Wang W , et al. Characterization of the gut microbiome using 16S or shotgun metagenomics . Front Microbiol 2016 ; 7 : 459 . Google Scholar Crossref Search ADS PubMed 75 Ranjan R , Rani A , Metwally A , et al. Analysis of the microbiome: advantages of whole genome shotgun versus 16S amplicon sequencing . Biochem Biophys Res Commun 2016 ; 469 ( 4 ): 967 – 77 . http://dx.doi.org/10.1016/j.bbrc.2015.12.083 Google Scholar Crossref Search ADS PubMed 76 Cui L , Morris A , Ghedin E. The human mycobiome in health and disease . Genome Med 2013 ; 5 ( 7 ): 63.http://dx.doi.org/10.1186/gm467 Google Scholar Crossref Search ADS PubMed 77 Huseyin CE , O’Toole PW , Cotter PD , et al. Forgotten fungi—the gut mycobiome in human health and disease . FEMS Microbiol Rev 2017 ; 41 : 479 – 511 . Google Scholar Crossref Search ADS PubMed 78 Zhao G , Wu G , Lim ES , et al. VirusSeeker, a computational pipeline for virus discovery and virome composition analysis . Virology 2017 ; 503 : 21 – 30 . http://dx.doi.org/10.1016/j.virol.2017.01.005 Google Scholar Crossref Search ADS PubMed 79 Czeczko P , Greenway SC , de Koning A. EzMap: a simple pipeline for reproducible analysis of the human virome . Bioinformatics 2017 ; 33 : 2573 – 4 . http://dx.doi.org/10.1093/bioinformatics/btx202 Google Scholar Crossref Search ADS PubMed 80 Handelsman J , Rondon MR , Brady SF , et al. Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products . Chem Biol 1998 ; 5 ( 10 ): R245 – 9 . Google Scholar Crossref Search ADS PubMed 81 Wooley JC , Godzik A , Friedberg I. A primer on metagenomics . PLoS Comput Biol 2010 ; 6 ( 2 ): e1000667. Google Scholar Crossref Search ADS PubMed 82 Handelsman J. Metagenomics: application of genomics to uncultured microorganisms . Microbiol Mol Biol Rev 2004 ; 68 ( 4 ): 669 – 85 . http://dx.doi.org/10.1128/MMBR.68.4.669-685.2004 Google Scholar Crossref Search ADS PubMed 83 Sharpton TJ. An introduction to the analysis of shotgun metagenomic data . Front Plant Sci 2014 ; 5 : 209. Google Scholar Crossref Search ADS PubMed 84 Head SR , Komori HK , LaMere SA , et al. Library construction for next-generation sequencing: overviews and challenges . Biotechniques 2014 ; 56 ( 2 ): 61 . Google Scholar Crossref Search ADS PubMed 85 Rintala A , Pietilä S , Munukka E , et al. Gut microbiota analysis results are highly dependent on the 16S rRNA gene target region, whereas the impact of DNA extraction is minor . J Biomol Tech 2017 ; 28 : 19 . Google Scholar PubMed 86 Olson ND , Treangen TJ , Hill CM , et al. Metagenomic assembly through the lens of validation: recent advances in assessing and improving the quality of genomes assembled from metagenomes . Brief Bioinform 2017 , doi: 10.1093/bib/bbx098. 87 Bradley RD , Hillis DM. Recombinant DNA sequences generated by PCR amplification . Mol Biol Evol 1997 ; 14 ( 5 ): 592 – 3 . http://dx.doi.org/10.1093/oxfordjournals.molbev.a025797 Google Scholar Crossref Search ADS PubMed 88 Jackman SD , Vandervalk BP , Mohamadi H , et al. ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter . Genome Res 2017 ; 27 : 768 – 77 . http://dx.doi.org/10.1101/gr.214346.116 Google Scholar Crossref Search ADS PubMed 89 Koren S , Treangen TJ , Pop M. Bambus 2: scaffolding metagenomes . Bioinformatics 2011 ; 27 ( 21 ): 2964 – 71 . http://dx.doi.org/10.1093/bioinformatics/btr520 Google Scholar Crossref Search ADS PubMed 90 Lin Y-Y , Hsieh C-H , Chen J-H , et al. De novo assembly of highly polymorphic metagenomic data using in situ generated reference sequences and a novel BLAST-based assembly pipeline . BMC Bioinformatics 2017 ; 18 ( 1 ): 223 . http://dx.doi.org/10.1186/s12859-017-1630-z Google Scholar Crossref Search ADS PubMed 91 Mysara M , Saeys Y , Leys N , et al. CATCh, an ensemble classifier for chimera detection in 16S rRNA sequencing studies . Appl Environ Microbiol 2015 ; 81 ( 5 ): 1573 – 84 . http://dx.doi.org/10.1128/AEM.02896-14 Google Scholar Crossref Search ADS PubMed 92 Haas BJ , Gevers D , Earl AM , et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons . Genome Res 2011 ; 21 ( 3 ): 494 – 504 . http://dx.doi.org/10.1101/gr.112730.110 Google Scholar Crossref Search ADS PubMed 93 Sayols S , Scherzinger D , Klein H. dupRadar: a Bioconductor package for the assessment of PCR artifacts in RNA-Seq data . BMC Bioinformatics 2016 ; 17 ( 1 ): 428.http://dx.doi.org/10.1186/s12859-016-1276-2 Google Scholar Crossref Search ADS PubMed 94 Schirmer M , D’Amore R , Ijaz UZ , et al. Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data . BMC Bioinformatics 2016 ; 17 ( 1 ): 125 . Google Scholar Crossref Search ADS PubMed 95 Peng Y , Leung HC , Yiu S-M , et al. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth . Bioinformatics 2012 ; 28 ( 11 ): 1420 – 8 . http://dx.doi.org/10.1093/bioinformatics/bts174 Google Scholar Crossref Search ADS PubMed 96 Jeraldo P , Kalari K , Chen X , et al. IM-TORNADO: a tool for comparison of 16S reads from paired-end libraries . PLoS One 2014 ; 9 ( 12 ): e114804 . Google Scholar Crossref Search ADS PubMed 97 Lai B , Wang F , Wang X , et al. InteMAP: Integrated metagenomic assembly pipeline for NGS short reads . BMC Bioinformatics 2015 ; 16 ( 1 ): 244 . http://dx.doi.org/10.1186/s12859-015-0686-x Google Scholar Crossref Search ADS PubMed 98 Mysara M , Leys N , Raes J , et al. IPED: a highly efficient denoising tool for Illumina MiSeq Paired-end 16S rRNA gene amplicon sequencing data . BMC Bioinformatics 2016 ; 17 ( 1 ): 192 . http://dx.doi.org/10.1186/s12859-016-1061-2 Google Scholar Crossref Search ADS PubMed 99 Lai B , Ding R , Li Y , et al. A de novo metagenomic assembly program for shotgun DNA reads . Bioinformatics 2012 ; 28 ( 11 ): 1455 – 62 . http://dx.doi.org/10.1093/bioinformatics/bts162 Google Scholar Crossref Search ADS PubMed 100 Parikh HI , Koparde VN , Bradley SP , et al. MeFiT: merging and filtering tool for illumina paired-end reads for 16S rRNA amplicon sequencing . BMC Bioinformatics 2016 ; 17 ( 1 ): 491 . http://dx.doi.org/10.1186/s12859-016-1358-1 Google Scholar Crossref Search ADS PubMed 101 Li D , Liu C-M , Luo R , et al. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph . Bioinformatics 2015 ; 31 ( 10 ): 1674 – 6 . http://dx.doi.org/10.1093/bioinformatics/btv033 Google Scholar Crossref Search ADS PubMed 102 Unno T. Bioinformatic suggestions on MiSeq-based microbial community analysis . J Microbiol Biotechnol 2015 ; 25 ( 6 ): 765 – 70 . http://dx.doi.org/10.4014/jmb.1409.09057 Google Scholar Crossref Search ADS PubMed 103 Treangen TJ , Koren S , Sommer DD , et al. MetAMOS: a modular and open source metagenomic assembly and analysis pipeline . Genome Biol 2013 ; 14 ( 1 ): R2 . Google Scholar Crossref Search ADS PubMed 104 Nurk S , Meleshko D , Korobeynikov A , et al. metaSPAdes: a new versatile de novo metagenomics assembler . Genome Res 2017 ; 27 : 824 – 34 . Google Scholar Crossref Search ADS PubMed 105 Namiki T , Hachiya T , Tanaka H , et al. MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads . Nucleic Acids Res 2012 ; 40 ( 20 ): e155 . Google Scholar Crossref Search ADS PubMed 106 Schloss PD , Westcott SL , Ryabin T , et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities . Appl Environ Microbiol 2009 ; 75 ( 23 ): 7537 – 41 . http://dx.doi.org/10.1128/AEM.01541-09 Google Scholar Crossref Search ADS PubMed 107 Mysara M , Leys N , Raes J , et al. NoDe: a fast error-correction algorithm for pyrosequencing amplicon reads . BMC Bioinformatics 2015 ; 16 : 88.http://dx.doi.org/10.1186/s12859-015-0520-5 Google Scholar Crossref Search ADS PubMed 108 Mysara M , Njima M , Leys N , et al. From reads to operational taxonomic units: an ensemble processing pipeline for MiSeq amplicon sequencing data . Gigascience 2017 ; 6 ( 2 ): 1 – 10 . http://dx.doi.org/10.1093/gigascience/giw017 Google Scholar Crossref Search ADS PubMed 109 Cuccuru G , Orsini M , Pinna A , et al. Orione, a web-based framework for NGS analysis in microbiology . Bioinformatics 2014 ; 30 ( 13 ): 1928 – 9 . http://dx.doi.org/10.1093/bioinformatics/btu135 Google Scholar Crossref Search ADS PubMed 110 Ruby JG , Bellare P , DeRisi JL. PRICE: software for the targeted assembly of components of (Meta) genomic sequence data . G3 2013 ; 3 : 865 – 80 . http://dx.doi.org/10.1534/g3.113.005967 Google Scholar Crossref Search ADS PubMed 111 Caporaso JG , Kuczynski J , Stombaugh J , et al. QIIME allows analysis of high-throughput community sequencing data . Nat Methods 2010 ; 7 ( 5 ): 335 – 6 . http://dx.doi.org/10.1038/nmeth.f.303 Google Scholar Crossref Search ADS PubMed 112 Okonechnikov K , Conesa A , García-Alcalde F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data . Bioinformatics 2016 ; 32 : 292 – 4 . Google Scholar PubMed 113 Boisvert S , Raymond F , Godzaridis É , et al. Ray Meta: scalable de novo metagenome assembly and profiling . Genome Biol 2012 ; 13 ( 12 ): R122 . Google Scholar Crossref Search ADS PubMed 114 Mangul S , Yang HT , Strauli N , et al. Dumpster diving in RNA-sequencing to find the source of every last read . bioRxiv 2016 :053041. 115 Hardwick SA , Chen WY , Wong T , et al. Spliced synthetic genes as internal controls in RNA sequencing experiments . Nat Methods 2016 ; 13 ( 9 ): 792 – 8 . http://dx.doi.org/10.1038/nmeth.3958 Google Scholar Crossref Search ADS PubMed 116 Pimentel H , Bray N , Puente S , et al. . Supplementary materials for “Differential analysis of RNA-Seq incorporating quantification uncertainty” . bioRxiv 2016 . 117 Gregor I , Schönhuth A , McHardy AC. Snowball: strain aware gene assembly of metagenomes . Bioinformatics 2016 ; 32 ( 17 ): i649 – 57 . Google Scholar Crossref Search ADS PubMed 118 Bolger AM , Lohse M , Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data . Bioinformatics 2014 ; 30 : 2114 – 20 . http://dx.doi.org/10.1093/bioinformatics/btu170 Google Scholar Crossref Search ADS PubMed 119 Edgar RC , Haas BJ , Clemente JC , et al. UCHIME improves sensitivity and speed of chimera detection . Bioinformatics 2011 ; 27 ( 16 ): 2194 – 200 . http://dx.doi.org/10.1093/bioinformatics/btr381 Google Scholar Crossref Search ADS PubMed 120 Rognes T , Flouri T , Nichols B , et al. VSEARCH: a versatile open source tool for metagenomics . PeerJ 2016 ; 4 : e2584. Google Scholar Crossref Search ADS PubMed 121 Wang Q , Fish JA , Gilman M , et al. Xander: employing a novel method for efficient gene-targeted metagenomic assembly . Microbiome 2015 ; 3 ( 1 ): 32 . http://dx.doi.org/10.1186/s40168-015-0093-6 Google Scholar Crossref Search ADS PubMed 122 Nawrocki EP , Kolbe DL , Eddy SR , Infernal 1. 0: inference of RNA alignments . Bioinformatics 2009 ; 25 ( 10 ): 1335 – 7 . http://dx.doi.org/10.1093/bioinformatics/btp157 Google Scholar Crossref Search ADS PubMed 123 Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads . Nat Methods 2013 ; 10 ( 10 ): 996 – 8 . http://dx.doi.org/10.1038/nmeth.2604 Google Scholar Crossref Search ADS PubMed 124 Edgar RC. Search and clustering orders of magnitude faster than BLAST . Bioinformatics 2010 ; 26 ( 19 ): 2460 – 1 . http://dx.doi.org/10.1093/bioinformatics/btq461 Google Scholar Crossref Search ADS PubMed 125 Li W , Fu L , Niu B , et al. Ultrafast clustering algorithms for metagenomic sequence analysis . Brief Bioinform 2012 ; 13 : 656 – 68 . http://dx.doi.org/10.1093/bib/bbs035 Google Scholar Crossref Search ADS PubMed 126 Caporaso JG , Bittinger K , Bushman FD , et al. PyNAST: a flexible tool for aligning sequences to a template alignment . Bioinformatics 2010 ; 26 ( 2 ): 266 – 7 . http://dx.doi.org/10.1093/bioinformatics/btp636 Google Scholar Crossref Search ADS PubMed 127 Bengtsson‐Palme J , Hartmann M , Eriksson KM , et al. METAXA2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data . Mol Ecol Resour 2015 ; 15 ( 6 ): 1403 – 14 . Google Scholar Crossref Search ADS PubMed 128 Oh J , Choi C-H , Park M-K , et al. Clustom-cloud: In-memory data grid-based software for clustering 16s rrna sequence data in the cloud environment . PLoS One 2016 ; 11 ( 3 ): e0151064 . Google Scholar Crossref Search ADS PubMed 129 Mahé F , Rognes T , Quince C , et al. Swarm v2: highly-scalable and high-resolution amplicon clustering . PeerJ 2015 ; 3 : e1420. Google Scholar Crossref Search ADS PubMed 130 Westcott SL , Schloss PD. OptiClust, an improved method for assigning amplicon-based sequence data to operational taxonomic units . mSphere 2017 ; 2 :e00073-17. 131 Al-Ghalith GA , Montassier E , Ward HN , et al. NINJA-OPS: fast accurate marker gene alignment using concatenated ribosomes . PLoS Comput Biol 2016 ; 12 ( 1 ): e1004658 . Google Scholar Crossref Search ADS PubMed 132 Alneberg J , Bjarnason BS , De Bruijn I , et al. Binning metagenomic contigs by coverage and composition . Nat Methods 2014 ; 11 ( 11 ): 1144 – 46 . http://dx.doi.org/10.1038/nmeth.3103 Google Scholar Crossref Search ADS PubMed 133 Imelfort M , Parks D , Woodcroft BJ , et al. GroopM: an automated tool for the recovery of population genomes from related metagenomes . PeerJ 2014 ; 2 : e603. Google Scholar Crossref Search ADS PubMed 134 Ulyantsev VI , Kazakov SV , Dubinkina VB , et al. MetaFast: fast reference-free graph-based comparison of shotgun metagenomic data . Bioinformatics 2016 ; 32 : 2760 – 7 . http://dx.doi.org/10.1093/bioinformatics/btw312 Google Scholar Crossref Search ADS PubMed 135 Kang DD , Froula J , Egan R , et al. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities . PeerJ 2015 ; 3 : e1165. Google Scholar Crossref Search ADS PubMed 136 Wu Y-W , Simmons BA , Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets . Bioinformatics 2015 ; 32 : 605 – 7 . Google Scholar Crossref Search ADS PubMed 137 Laczny CC , Sternal T , Plugaru V , et al. VizBin-an application for reference-independent visualization and human-augmented binning of metagenomic data . Microbiome 2015 ; 3 ( 1 ): 1 . http://dx.doi.org/10.1186/s40168-014-0066-1 Google Scholar Crossref Search ADS PubMed 138 Lu YY , Chen T , Fuhrman JA , et al. COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment, and paired-end read LinkAge . Bioinformatics 2017 ; 33 : 791 – 8 . Google Scholar PubMed 139 Girotto S , Pizzi C , Comin M. MetaProb: accurate metagenomic reads binning based on probabilistic sequence signatures . Bioinformatics 2016 ; 32 ( 17 ): i567 – 75 . Google Scholar Crossref Search ADS PubMed 140 Cole JR , Wang Q , Fish JA , et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis . Nucleic Acids Res 2014 ; 42 : D633 – 42 . Google Scholar Crossref Search ADS PubMed 141 DeSantis TZ , Hugenholtz P , Larsen N , et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB . Appl Environ Microbiol 2006 ; 72 ( 7 ): 5069 – 72 . http://dx.doi.org/10.1128/AEM.03006-05 Google Scholar Crossref Search ADS PubMed 142 Pruesse E , Quast C , Knittel K , et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB . Nucleic Acids Res 2007 ; 35 ( 21 ): 7188 – 96 . http://dx.doi.org/10.1093/nar/gkm864 Google Scholar Crossref Search ADS PubMed 143 O'Leary NA , Wright MW , Brister JR , et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation . Nucleic Acids Res 2016 ; 44 : D733 – 45 . Google Scholar Crossref Search ADS PubMed 144 Forster SC , Browne HP , Kumar N , et al. HPMCD: the database of human microbial communities from metagenomic datasets and microbial reference genomes . Nucleic Acids Res 2016 ; 44 ( D1 ): D604 – 9 . Google Scholar Crossref Search ADS PubMed 145 Flygare S , Simmon K , Miller C , et al. Taxonomer: an interactive metagenomics analysis portal for universal pathogen detection and host mRNA expression profiling . Genome Biol 2016 ; 17 ( 1 ): 111 . http://dx.doi.org/10.1186/s13059-016-0969-1 Google Scholar Crossref Search ADS PubMed 146 Cox JW , Ballweg RA , Taft DH , et al. A fast and robust protocol for metataxonomic analysis using RNAseq data . Microbiome 2017 ; 5 ( 1 ): 7 . http://dx.doi.org/10.1186/s40168-016-0219-5 Google Scholar Crossref Search ADS PubMed 147 Gao X , Lin H , Revanna K , et al. A Bayesian taxonomic classification method for 16S rRNA gene sequences with improved species-level accuracy . BMC Bioinformatics 2017 ; 18 ( 1 ): 247 . http://dx.doi.org/10.1186/s12859-017-1670-4 Google Scholar Crossref Search ADS PubMed 148 Allard G , Ryan FJ , Jeffery IB , et al. SPINGO: a rapid species-classifier for microbial amplicon sequences . BMC Bioinformatics 2015 ; 16 : 324.http://dx.doi.org/10.1186/s12859-015-0747-1 Google Scholar Crossref Search ADS PubMed 149 Segata N , Waldron L , Ballarini A , et al. Metagenomic microbial community profiling using unique clade-specific marker genes . Nat Methods 2012 ; 9 ( 8 ): 811 – 14 . http://dx.doi.org/10.1038/nmeth.2066 Google Scholar Crossref Search ADS PubMed 150 Huson DH , Weber N. Microbial community analysis using MEGAN . Methods Enzymol 2013 ; 531 : 465 – 85 . Google Scholar Crossref Search ADS PubMed 151 Kim D , Song L , Breitwieser FP , et al. Centrifuge: rapid and sensitive classification of metagenomic sequences . Genome Res 2016 ; 26 : 1721 – 9 . http://dx.doi.org/10.1101/gr.210641.116 Google Scholar Crossref Search ADS PubMed 152 Petersen TN , Lukjancenko O , Thomsen MCF , et al. MGmapper: Reference based mapping and taxonomy annotation of metagenomics sequence reads . PLoS One 2017 ; 12 ( 6 ): e0176469 . Google Scholar Crossref Search ADS PubMed 153 Luo Y , Yu YW , Zeng J , et al. Metagenomic binning through low density hashing . bioRxiv 2017 : 133116 . 154 Henry VJ , Bandrowski AE , Pepin A-S , et al. OMICtools: an informative directory for multi-omic data analysis . Database 2014 ; 2014 : bau069. Google Scholar Crossref Search ADS PubMed 155 Comeau AM , Douglas GM , Langille MGI , Eisen J. Microbiome helper: a custom and streamlined workflow for microbiome research . mSystems 2017 ; 2 ( 1 ): e00127-16 . Google Scholar Crossref Search ADS PubMed 156 Kultima JR , Sunagawa S , Li J , et al. MOCAT: a metagenomics assembly and gene prediction toolkit . PLoS One 2012 ; 7 ( 10 ): e47656 . Google Scholar Crossref Search ADS PubMed 157 Narayanasamy S , Jarosz Y , Muller EE , et al. IMP: a pipeline for reproducible metagenomic and metatranscriptomic analyses . bioRxiv 2016 : 039263 . 158 Lin H-H , Liao Y-C. drVM: a new tool for efficient genome assembly of known eukaryotic viruses from metagenomes . Gigascience 2017 ; 6 ( 2 ): 1 – 10 . http://dx.doi.org/10.1093/gigascience/gix003 Google Scholar Crossref Search ADS 159 Broeksema B , Calusinska M , McGee F , et al. ICoVeR–an interactive visualization tool for verification and refinement of metagenomic bins . BMC Bioinformatics 2017 ; 18 ( 1 ): 233 . http://dx.doi.org/10.1186/s12859-017-1653-5 Google Scholar Crossref Search ADS PubMed 160 Kerepesi C , Bánky D , Grolmusz V. AmphoraNet: the webserver implementation of the AMPHORA2 metagenomic workflow suite . Gene 2014 ; 533 ( 2 ): 538 – 40 . Google Scholar Crossref Search ADS PubMed 161 Fosso B , Santamaria M , D’Antonio M , et al. MetaShot: an accurate workflow for taxon classification of host-associated microbiome from shotgun metagenomic data . Bioinformatics 2017 ; 33 : 1730 – 2 . Google Scholar PubMed 162 Giongo A , Crabb DB , Davis-Richardson AG , et al. PANGEA: pipeline for analysis of next generation amplicons . ISME J 2010 ; 4 ( 7 ): 852 – 61 . http://dx.doi.org/10.1038/ismej.2010.16 Google Scholar Crossref Search ADS PubMed 163 Office of Cyber Infrastructure and Computational Biology (OCICB) N . Nephele. http://nephele.niaid.nih.gov 2016 . 164 Hildebrand F , Tadeo R , Voigt AY , et al. LotuS: an efficient and user-friendly OTU processing pipeline . Microbiome 2014 ; 2 ( 1 ): 30 . http://dx.doi.org/10.1186/2049-2618-2-30 Google Scholar Crossref Search ADS PubMed 165 Turnbaugh PJ , Ley RE , Hamady M , et al. The human microbiome project: exploring the microbial part of ourselves in a changing world . Nature 2007 ; 449 ( 7164 ): 804 . http://dx.doi.org/10.1038/nature06244 Google Scholar Crossref Search ADS PubMed 166 Mitchell A , Bucchini F , Cochrane G , et al. EBI metagenomics in 2016-an expanding and evolving resource for the analysis and archiving of metagenomic data . Nucleic Acids Res 2016 ; 44 : D595 – 603 . Google Scholar Crossref Search ADS PubMed 167 Peterson J , Garges S , Giovanni M , et al. The NIH human microbiome project . Genome Res 2009 ; 19 ( 12 ): 2317 – 23 . http://dx.doi.org/10.1101/gr.096651.109 Google Scholar Crossref Search ADS PubMed 168 Markowitz VM , Chen I-MA , Palaniappan K , et al. IMG: the integrated microbial genomes database and comparative analysis system . Nucleic Acids Res 2012 ; 40 ( D1 ): D115 – 22 . Google Scholar Crossref Search ADS PubMed 169 Hurwitz B. iMicrobe: advancing clinical and environmental microbial research using the iPlant cyberinfrastructure. In: Plant and animal genome XXII conference . San Diego, CA , 2014 . 170 Meyer F , Paarmann D , D'Souza M , et al. The metagenomics RAST server–a public resource for the automatic phylogenetic and functional analysis of metagenomes . BMC Bioinformatics 2008 ; 9 : 386.http://dx.doi.org/10.1186/1471-2105-9-386 Google Scholar Crossref Search ADS PubMed 171 Hyde ER , Sanders J , Tripathi A , et al. Comparing 16S rRNA Marker Gene and Shotgun Metagenomics Datasets in the American Gut Project Using State of the Art Tools . 172 Kovalevskaya NV , Whicher C , Richardson TD , et al. DNAdigest and repositive: connecting the World of Genomic Data . PLoS Biol 2016 ; 14 : e1002418. Google Scholar Crossref Search ADS PubMed 173 Simberloff D. Properties of the rarefaction diversity measurement . Am Nat 1972 ; 106 ( 949 ): 414 – 18 . http://dx.doi.org/10.1086/282781 Google Scholar Crossref Search ADS 174 Lozupone C , Knight R. UniFrac: a new phylogenetic method for comparing microbial communities . Appl Environ Microbiol 2005 ; 71 ( 12 ): 8228 – 35 . http://dx.doi.org/10.1128/AEM.71.12.8228-8235.2005 Google Scholar Crossref Search ADS PubMed 175 Heltshe JF , Forrester NE. Estimating species richness using the jackknife procedure . Biometrics 1983 ; 39 ( 1 ): 1 – 11 . http://dx.doi.org/10.2307/2530802 Google Scholar Crossref Search ADS PubMed 176 Xiao J , Cao H , Chen J. False discovery rate control incorporating phylogenetic tree increases detection power in microbiome-wide multiple testing . Bioinformatics 2017 ; 33 : 2873 – 81 . http://dx.doi.org/10.1093/bioinformatics/btx311 Google Scholar Crossref Search ADS PubMed 177 Buttigieg PL , Ramette A. A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses . FEMS Microbiol Ecol 2014 ; 90 ( 3 ): 543 – 50 . http://dx.doi.org/10.1111/1574-6941.12437 Google Scholar Crossref Search ADS PubMed 178 Le Cao K-A , Costello M-E , Lakis VA , et al. mixMC: a multivariate statistical framework to gain insight into Microbial Communities . PLoS One 2016 ; 11 : e0160169 . Google Scholar Crossref Search ADS PubMed 179 Ramette A. Multivariate analyses in microbial ecology . FEMS Microbiol Ecol 2007 ; 62 ( 2 ): 142 – 60 . http://dx.doi.org/10.1111/j.1574-6941.2007.00375.x Google Scholar Crossref Search ADS PubMed 180 Yang Y , Chen N , Chen T. mLDM: a new hierarchical Bayesian statistical model for sparse microbioal association discovery . bioRxiv 2016 :042630. 181 Mendes-Soares H , Mundy M , Soares LM , et al. MMinte: an application for predicting metabolic interactions among the microbial species in a community . BMC Bioinformatics 2016 ; 17 : 343 . http://dx.doi.org/10.1186/s12859-016-1230-3 Google Scholar Crossref Search ADS PubMed 182 Shannon P , Markiel A , Ozier O , et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks . Genome Res 2003 ; 13 ( 11 ): 2498 – 504 . http://dx.doi.org/10.1101/gr.1239303 Google Scholar Crossref Search ADS PubMed 183 Bastian M , Heymann S , Jacomy M. Gephi: an open source software for exploring and manipulating networks . ICWSM 2009 ; 8 : 361 – 2 . 184 Vespignani A , Wasserman S , Wernert E , et al. Network Workbench Tool . 185 Segata N , Izard J , Waldron L , et al. Metagenomic biomarker discovery and explanation . Genome Biol 2011 ; 12 ( 6 ): R60 . Google Scholar Crossref Search ADS PubMed 186 Turnbaugh PJ , Ley RE , Mahowald MA , et al. An obesity-associated gut microbiome with increased capacity for energy harvest . Nature 2006 ; 444 ( 7122 ): 1027 – 31 . Google Scholar Crossref Search ADS PubMed 187 Connelly S , Bristol A , Hubert S , et al. Clinical-stage, oral β-lactamase enzyme to prevent clostridium difficile infection triggered by antibiotic-mediated gut microbiome disruption. In: Open Forum Infectious Diseases . Oxford University Press , 2016 , 2221 . 188 Alexander JL , Scott A , Mroz A , et al. 91 Mass spectrometry imaging (MSI) of microbiome-metabolome interactions in colorectal cancer . Gastroenterology 2016 ; 150 ( 4 ): S23 . Google Scholar Crossref Search ADS 189 Weir TL , Manter DK , Sheflin AM , et al. Stool microbiome and metabolome differences between colorectal cancer patients and healthy adults . PLoS One 2013 ; 8 ( 8 ): e70803 . Google Scholar Crossref Search ADS PubMed 190 Ward T , Larson J , Meulemans J , et al. BugBase predicts organism level microbiome phenotypes . bioRxiv 2017 :133462. 191 Zakrzewski M , Proietti C , Ellis JJ , et al. Calypso: a user-friendly web-server for mining and visualizing microbiome–environment interactions . Bioinformatics 2016 ; 33 : 782 – 3 . 192 Bose T , Haque MM , Reddy C , et al. COGNIZER: a framework for functional annotation of metagenomic datasets . PLoS One 2015 ; 10 ( 11 ): e0142102 . Google Scholar Crossref Search ADS PubMed 193 Vázquez-Baeza Y , Pirrung M , Gonzalez A , et al. EMPeror: a tool for visualizing high-throughput microbial community data . Gigascience 2013 ; 2 ( 1 ): 16 . Google Scholar Crossref Search ADS PubMed 194 Robertson CE , Harris JK , Wagner BD , et al. Explicet: Graphical user interface software for metadata-driven management, analysis, and visualization of microbiome data . Bioinformatics 2013 ; 29 : 3100 – 1 . http://dx.doi.org/10.1093/bioinformatics/btt526 Google Scholar Crossref Search ADS PubMed 195 Manor O , Borenstein E. Systematic characterization and analysis of the taxonomic drivers of functional shifts in the human microbiome . Cell Host Microbe 2017 ; 21 ( 2 ): 254 – 67 . http://dx.doi.org/10.1016/j.chom.2016.12.014 Google Scholar Crossref Search ADS PubMed 196 Kim J , Kim MS , Koh AY , et al. FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies . BMC Bioinformatics 2016 ; 17 ( 1 ): 420 . http://dx.doi.org/10.1186/s12859-016-1278-0 Google Scholar Crossref Search ADS PubMed 197 Rho M , Tang H , Ye Y. FragGeneScan: predicting genes in short and error-prone reads . Nucleic Acids Res 2010 ; 38 ( 20 ): e191 . Google Scholar Crossref Search ADS PubMed 198 Uchiyama T , Irie M , Mori H , et al. FuncTree: functional analysis and visualization for large-scale omics data . PLoS One 2015 ; 10 ( 5 ): e0126967 . Google Scholar Crossref Search ADS PubMed 199 Riehle K , Coarfa C , Jackson A , et al. The Genboree Microbiome Toolset and the analysis of 16S rRNA microbial sequences . BMC Bioinformatics 2012 ; 13(Suppl 13) : S11 . Google Scholar Crossref Search ADS PubMed 200 Kelley DR , Liu B , Delcher AL , et al. Gene prediction with Glimmer for metagenomic sequences augmented by classification and clustering . Nucleic Acids Res 2012 ; 40 ( 1 ): e9 . Google Scholar Crossref Search ADS PubMed 201 Asnicar F , Weingart G , Tickle TL , et al. Compact graphical representation of phylogenetic data and metadata with GraPhlAn . PeerJ 2015 ; 3 : e1029. Google Scholar Crossref Search ADS PubMed 202 Abubucker S , Segata N , Goll J , et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome . PLoS Comput Biol 2012 ; 8 ( 6 ): e1002358 . Google Scholar Crossref Search ADS PubMed 203 Narayanasamy S , Jarosz Y , Muller EE , et al. IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses . Genome Biol 2016 ; 17 : 260 . http://dx.doi.org/10.1186/s13059-016-1116-8 Google Scholar Crossref Search ADS PubMed 204 Ondov BD , Bergman NH , Phillippy AM. Interactive metagenomic visualization in a Web browser . BMC Bioinformatics 2011 ; 12 : 385.http://dx.doi.org/10.1186/1471-2105-12-385 Google Scholar Crossref Search ADS PubMed 205 Wang Y , Xu L , Gu YQ , et al. MetaCoMET: a web platform for discovery and visualization of the core microbiome . Bioinformatics 2016 ; 32 : 3469 – 70 . Google Scholar PubMed 206 Arndt D , Xia J , Liu Y , et al. METAGENassist: a comprehensive web server for comparative metagenomics . Nucleic Acids Res 2012 ; 40 : W88 – 95 . Google Scholar Crossref Search ADS PubMed 207 Wagner J , Chelaru F , Kancherla J , et al. Metaviz: interactive statistical and visual analysis of metagenomic data . bioRxiv 2017 :105205. 208 Dhariwal A , Chong J , Habib S , et al. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data . Nucleic Acids Res 2017 ; 45 : W180 – 8 . Google Scholar Crossref Search ADS PubMed 209 Kultima JR , Coelho LP , Forslund K , et al. MOCAT2: a metagenomic assembly, annotation and profiling framework . Bioinformatics 2016 ; 32 ( 16 ): 2520 – 3 . http://dx.doi.org/10.1093/bioinformatics/btw183 Google Scholar Crossref Search ADS PubMed 210 Jing G , Sun Z , Wang H , et al. Parallel-META 3: Comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities . Sci Rep 2017 ; 7 : 40371.http://dx.doi.org/10.1038/srep40371 Google Scholar Crossref Search ADS PubMed 211 Soh J , Dong X , Caffrey SM , et al. Phoenix 2: a locally installable large-scale 16S rRNA gene sequence analysis pipeline with Web interface . J Biotechnol 2013 ; 167 ( 4 ): 393 – 403 . http://dx.doi.org/10.1016/j.jbiotec.2013.07.004 Google Scholar Crossref Search ADS PubMed 212 Langille MG , Zaneveld J , Caporaso JG , et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences . Nat Biotechnol 2013 ; 31 ( 9 ): 814 – 21 . http://dx.doi.org/10.1038/nbt.2676 Google Scholar Crossref Search ADS PubMed 213 Hyatt D , Chen G-L , LoCascio PF , et al. Prodigal: prokaryotic gene recognition and translation initiation site identification . BMC Bioinformatics 2010 ; 11 : 119.http://dx.doi.org/10.1186/1471-2105-11-119 Google Scholar Crossref Search ADS PubMed 214 Lagkouvardos I , Fischer S , Kumar N , et al. Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons . PeerJ 2017 ; 5 : e2836. Google Scholar Crossref Search ADS PubMed 215 Westreich ST , Korf I , Mills DA , et al. SAMSA: a comprehensive metatranscriptome analysis pipeline . BMC Bioinformatics 2016 ; 17 ( 1 ): 399 . http://dx.doi.org/10.1186/s12859-016-1270-8 Google Scholar Crossref Search ADS PubMed 216 Kaminski J , Gibson MK , Franzosa EA , et al. High-specificity targeted functional profiling in microbial communities with ShortBRED . PLoS Comput Biol 2015 ; 11 ( 12 ): e1004557 . Google Scholar Crossref Search ADS PubMed 217 Parks DH , Tyson GW , Hugenholtz P , et al. STAMP: statistical analysis of taxonomic and functional profiles . Bioinformatics 2014 ; 30 ( 21 ): 3123 – 4 . http://dx.doi.org/10.1093/bioinformatics/btu494 Google Scholar Crossref Search ADS PubMed 218 Aßhauer KP , Wemheuer B , Daniel R , et al. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data . Bioinformatics 2015 ; 31 ( 17 ): 2882 – 4 . Google Scholar Crossref Search ADS PubMed 219 Huse SM , Welch DBM , Voorhis A , et al. VAMPS: a website for visualization and analysis of microbial population structures . BMC Bioinformatics 2014 ; 15 : 41.http://dx.doi.org/10.1186/1471-2105-15-41 Google Scholar Crossref Search ADS PubMed 220 Nagpal S , Haque MM , Mande SS , Ahmed N. Vikodak-A modular framework for inferring functional potential of microbial communities from 16S metagenomic datasets . PLoS One 2016 ; 11 ( 2 ): e0148347. Google Scholar Crossref Search ADS PubMed 221 Dray S , Dufour A-B. The ade4 package: implementing the duality diagram for ecologists . J Stat Softw 2007 ; 22 ( 4 ): 1 – 20 . Google Scholar Crossref Search ADS 222 Rodriguez-R LM , Konstantinidis KT. The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes . PeerJ Preprints 2016 ; 4 : e1900v1 . 223 Luo D , Ziebell S , An L. An informative approach on differential abundance analysis for time-course metagenomic sequencing data . Bioinformatics 2017 ; 33 : 1286 – 92 . Google Scholar Crossref Search ADS PubMed 224 Paulson JN , Stine OC , Bravo HC , Pop M. Differential abundance analysis for microbial marker-gene surveys . Nat Methods 2013 ; 10 ( 12 ): 1200 – 2 . http://dx.doi.org/10.1038/nmeth.2658 Google Scholar Crossref Search ADS PubMed 225 Zhan X , Tong X , Zhao N. A small‐sample multivariate kernel machine test for microbiome association studies . Genet Epidemiol 2017 ; 41 : 210 – 20 . Google Scholar Crossref Search ADS PubMed 226 Cao Y , Zheng X , Li F , et al. mmnet: an R package for metagenomics systems biology analysis . Biomed Res Int 2015 ; 2015 : 1 . 227 McMurdie PJ , Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data . PLoS One 2013 ; 8 ( 4 ): e61217. Google Scholar Crossref Search ADS PubMed 228 Sohn MB , Du R , An L. A robust approach for identifying differentially abundant features in metagenomic samples . Bioinformatics 2015 ; 31 : 2269 – 75 . http://dx.doi.org/10.1093/bioinformatics/btv165 Google Scholar Crossref Search ADS PubMed 229 Cao Y , Wang Y , Zheng X , et al. RevEcoR: an R package for the reverse ecology analysis of microbiomes . BMC Bioinformatics 2016 ; 17 ( 1 ): 294 . http://dx.doi.org/10.1186/s12859-016-1088-4 Google Scholar Crossref Search ADS PubMed 230 Kristiansson E , Hugenholtz P , Dalevi D. ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes . Bioinformatics 2009 ; 25 ( 20 ): 2737 – 8 . http://dx.doi.org/10.1093/bioinformatics/btp508 Google Scholar Crossref Search ADS PubMed 231 Oksanen J , Kindt R , Legendre P , et al. The vegan package . Commun Ecol Package 2007 ; 10 : 631 – 7 . 232 Cockrell C , Christley S , An G , Gabhann FM. Investigation of inflammation and tissue patterning in the gut using a spatially explicit general-purpose model of enteric tissue (SEGMEnT) . PLoS Comput Biol 2014 ; 10 ( 3 ): e1003507. Google Scholar Crossref Search ADS PubMed 233 Leber A , Viladomiu M , Hontecillas R , et al. Systems modeling of interactions between mucosal immunity and the gut microbiome during clostridium difficile infection . PLoS One 2015 ; 10 ( 7 ): e0134849 . Google Scholar Crossref Search ADS PubMed 234 Abedi V , Hontecillas R , Hoops S , et al. ENISI multiscale modeling of mucosal immune responses driven by high performance computing. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) . IEEE, 2015 , p. 680–4. 235 Collins FS , Varmus H. A new initiative on precision medicine . N Engl J Med 2015 ; 372 ( 9 ): 793 – 5 . http://dx.doi.org/10.1056/NEJMp1500523 Google Scholar Crossref Search ADS PubMed 236 Initiative PM. Working group, the precision medicine initiative cohort program: building the foundation for 21st century medicine. PMI Working Group Report to the Advisory Committee to the Director, 2015 . 237 Somberg JC. The Human Microbiome and Therapeutics . LWW , 2012 . 238 ElRakaiby M , Dutilh BE , Rizkallah MR , et al. Pharmacomicrobiomics: the impact of human microbiome variations on systems pharmacology and personalized therapeutics . Omics 2014 ; 18 ( 7 ): 402 – 14 . http://dx.doi.org/10.1089/omi.2014.0018 Google Scholar Crossref Search ADS PubMed 239 Kuntz TM , Gilbert JA. Introducing the microbiome into precision medicine . Trends Pharmacol Sci 2017 ; 38 ( 1 ): 81 – 91 . http://dx.doi.org/10.1016/j.tips.2016.10.001 Google Scholar Crossref Search ADS PubMed 240 Johnson KW , Shameer K , Glicksberg BS , et al. Enabling precision cardiology through multiscale biology and systems medicine . JACC Basic Transl Sci 2017 ; 2 ( 3 ): 311 – 27 . http://dx.doi.org/10.1016/j.jacbts.2016.11.010 Google Scholar Crossref Search ADS PubMed 241 Antman EM , Loscalzo J. Precision medicine in cardiology . Nat Rev Cardiol 2016 ; 13 ( 10 ): 591 – 602 . http://dx.doi.org/10.1038/nrcardio.2016.101 Google Scholar Crossref Search ADS PubMed 242 Hold GL. The gut microbiota, dietary extremes and exercise . Gut 2014 ; 63 ( 12 ): 1838 – 9 . http://dx.doi.org/10.1136/gutjnl-2014-307305 Google Scholar Crossref Search ADS PubMed 243 Kang SS , Jeraldo PR , Kurti A , et al. Diet and exercise orthogonally alter the gut microbiome and reveal independent associations with anxiety and cognition . Mol Neurodegener 2014 ; 9 ( 1 ): 36 . http://dx.doi.org/10.1186/1750-1326-9-36 Google Scholar Crossref Search ADS PubMed 244 Barton W , Penney NC , Cronin O , et al. The microbiome of professional athletes differs from that of more sedentary subjects in composition and particularly at the functional metabolic level . Gut 2017 , doi: 10.1136/gutjnl-2016-313627. 245 Sandhu KV , Sherwin E , Schellekens H , et al. Feeding the microbiota-gut-brain axis: diet, microbiome, and neuropsychiatry . Transl Res 2017 ; 179 : 223 – 44 . http://dx.doi.org/10.1016/j.trsl.2016.10.002 Google Scholar Crossref Search ADS PubMed 246 Bokulich NA , Chung J , Battaglia T , et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life . Sci Transl Med 2016 ; 8 ( 343 ): 343ra382 . Google Scholar Crossref Search ADS 247 Preidis GA , Versalovic J. Targeting the human microbiome with antibiotics, probiotics, and prebiotics: gastroenterology enters the metagenomics era . Gastroenterology 2009 ; 136 ( 6 ): 2015 – 31 . http://dx.doi.org/10.1053/j.gastro.2009.01.072 Google Scholar Crossref Search ADS PubMed 248 Petschow B , Doré J , Hibberd P , et al. Probiotics, prebiotics, and the host microbiome: the science of translation . Ann N Y Acad Sci 2013 ; 1306 : 1 – 17 . Google Scholar Crossref Search ADS PubMed 249 Damaskos D , Kolios G. Probiotics and prebiotics in inflammatory bowel disease: microflora ‘on the scope’ . Br J Clin Pharmacol 2008 ; 65 ( 4 ): 453 – 67 . Google Scholar Crossref Search ADS PubMed 250 Schrezenmeir J , de Vrese M. Probiotics, prebiotics, and synbiotics—approaching a definition . Am J Clin Nutr 2001 ; 73(2 Suppl) : 361s – 4s . Google Scholar Crossref Search ADS 251 Ghouri YA , Richards DM , Rahimi EF , et al. Systematic review of randomized controlled trials of probiotics, prebiotics, and synbiotics in inflammatory bowel disease . Clin Exp Gastroenterol 2014 ; 7 : 473 . Google Scholar PubMed 252 Frei R , Akdis M , O’Mahony L. Prebiotics, probiotics, synbiotics, and the immune system: experimental data and clinical evidence . Curr Opin Gastroenterol 2015 ; 31 ( 2 ): 153 – 8 . Google Scholar Crossref Search ADS PubMed 253 Mehta V , Bhatt K , Desai N , et al. Probiotics: an adjuvant therapy for D-galactose induced Alzheimer's disease . J Med Res Innov 2017 ; 1 : 30 – 3 . Google Scholar Crossref Search ADS 254 Dinan TG , Stanton C , Cryan JF. Psychobiotics: a novel class of psychotropic . Biol Psychiatry 2013 ; 74 ( 10 ): 720 – 6 . http://dx.doi.org/10.1016/j.biopsych.2013.05.001 Google Scholar Crossref Search ADS PubMed 255 Wall R , Cryan JF , Ross RP , et al. Bacterial neuroactive compounds produced by psychobiotics. Microbial endocrinology: The microbiota-gut-brain axis in health and disease . Springer , 2014 , 221 – 39 . 256 Borody TJ , Khoruts A. Fecal microbiota transplantation and emerging applications . Nat Rev Gastroenterol Hepatol 2012 ; 9 : 88 – 96 . Google Scholar Crossref Search ADS 257 Smits LP , Bouter KE , de Vos WM , et al. Therapeutic potential of fecal microbiota transplantation . Gastroenterology 2013 ; 145 ( 5 ): 946 – 53 . http://dx.doi.org/10.1053/j.gastro.2013.08.058 Google Scholar Crossref Search ADS PubMed 258 Khoruts A , Weingarden AR. Emergence of fecal microbiota transplantation as an approach to repair disrupted microbial gut ecology . Immunol Lett 2014 ; 162 ( 2 ): 77 – 81 . http://dx.doi.org/10.1016/j.imlet.2014.07.016 Google Scholar Crossref Search ADS PubMed 259 Paramsothy S , Borody TJ , Lin E , et al. Donor recruitment for fecal microbiota transplantation . Inflamm Bowel Dis 2015 ; 21 ( 7 ): 1600 – 6 . http://dx.doi.org/10.1097/MIB.0000000000000405 Google Scholar Crossref Search ADS PubMed 260 Wolf‐Meyer MJ. Normal, regular, and standard: scaling the body through fecal microbial transplants . Med Anthropol Q 2016 ; 31 : 297 – 314 . Google Scholar Crossref Search ADS 261 Kang D-W , Adams JB , Gregory AC , et al. Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study . Microbiome 2017 ; 5 ( 1 ): 10 . http://dx.doi.org/10.1186/s40168-016-0225-7 Google Scholar Crossref Search ADS PubMed 262 Modi SR , Collins JJ , Relman DA. Antibiotics and the gut microbiota . J Clin Investig 2014 ; 124 ( 10 ): 4212.http://dx.doi.org/10.1172/JCI72333 Google Scholar Crossref Search ADS PubMed 263 Andersson DI. Persistence of antibiotic resistant bacteria . Curr Opin Microbiol 2003 ; 6 ( 5 ): 452 – 6 . http://dx.doi.org/10.1016/j.mib.2003.09.001 Google Scholar Crossref Search ADS PubMed 264 Zeissig S , Blumberg RS. Life at the beginning: perturbation of the microbiota by antibiotics in early life and its role in health and disease . Nat Immunol 2014 ; 15 ( 4 ): 307 – 10 . http://dx.doi.org/10.1038/ni.2847 Google Scholar Crossref Search ADS PubMed 265 Dubreuil L , Mahieux S , Neut C. Antibiotic Susceptibility of Probiotic Strains. Is it Reasonable to Combine Probiotics with Antibiotics? Gastroenterology 2017 ; 152 ( 5 ): S821. Google Scholar Crossref Search ADS 266 Sharma J , Chauhan D , Goyal A. Enhancement of antimicrobial activity of antibiotics by probiotics against Escherichia coli-An in vitro study . Adv Appl Sci Res 2014 ; 5 : 14 – 18 . 267 Adnan B , Lutvo S , Sabina K , et al. P329 Advantages to taking antibiotics with probiotics in children with reduction of complications diarrhoea . BMJ 2017 ; 102 . 268 Garrett WS. Gut microbiota in 2016: a banner year for gut microbiota research . Nat Rev Gastroenterol Hepatol 2017 ; 14 : 78 – 80 . http://dx.doi.org/10.1038/nrgastro.2016.207 Google Scholar Crossref Search ADS PubMed 269 Kantae V , Krekels EH , Van Esdonk MJ , et al. Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy . Metabolomics 2017 ; 13 ( 1 ): 9 . http://dx.doi.org/10.1007/s11306-016-1143-1 Google Scholar Crossref Search ADS PubMed 270 Enright EF , Gahan CG , Joyce SA , et al. Focus: microbiome: the impact of the gut microbiota on drug metabolism and clinical outcome . Yale J Biol Med 2016 ; 89 ( 3 ): 375 . Google Scholar PubMed 271 Koch C , Müller S. Personalized microbiome dynamics-Cytometric fingerprints for routine diagnostics . Mol Aspects Med 2017 , in press. 272 Halfvarson J , Brislawn CJ , Lamendella R , et al. Dynamics of the human gut microbiome in inflammatory bowel disease . Nat Microbiol 2017 ; 2 : 17004.http://dx.doi.org/10.1038/nmicrobiol.2017.4 Google Scholar Crossref Search ADS PubMed 273 Smith AH , Łukasik P , O'Connor MP , et al. Patterns, causes and consequences of defensive microbiome dynamics across multiple scales . Mol Ecol 2015 ; 24 ( 5 ): 1135 – 49 . Google Scholar Crossref Search ADS PubMed 274 Dorrestein PC , Mazmanian SK , Knight R. From microbiomess to metabolomes to function during host-microbial interactions . Immunity 2014 ; 40 : 824. Google Scholar Crossref Search ADS PubMed 275 von Mutius E. The shape of the microbiome in early life . Nat Med 2017 ; 23 ( 3 ): 274 – 5 . http://dx.doi.org/10.1038/nm.4299 Google Scholar Crossref Search ADS PubMed 276 Dunlop AL , Mulle JG , Ferranti EP , et al. The maternal microbiome and pregnancy outcomes that impact infant health: a review . Adv Neonat Care 2015 ; 15 ( 6 ): 377 . http://dx.doi.org/10.1097/ANC.0000000000000218 Google Scholar Crossref Search ADS 277 Zhulin IB. Databases for microbiologists . J Bacteriol 2015 ; 197 ( 15 ): 2458 – 67 . http://dx.doi.org/10.1128/JB.00330-15 Google Scholar Crossref Search ADS PubMed © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

Briefings in BioinformaticsOxford University Press

Published: May 21, 2019

There are no references for this article.