RNA methylation and diseases: experimental results, databases, Web servers and computational models

RNA methylation and diseases: experimental results, databases, Web servers and computational models Abstract Ribonucleic acid (RNA) methylation is a type of posttranscriptional modifications occurring in all kingdoms of life. It is strongly related to important biological process, thus making it linked to a number of human diseases. Owing to the development of high-throughput sequencing technology, plenty of achievement had been obtained in RNA methylation research recently. Meanwhile, various computational models have been developed to analyze and mining increasing RNA methylation data. In this review, we first made a brief introduction about eight types of most popular RNA methylation, the biological functions of RNA methylation, the relationship between RNA methylation and disease and five important RNA methylation-related diseases. The research of RNA methylation is based on sequencing data processing, and effective bioinformatics techniques can benefit better understanding of RNA methylation. We further introduced seven publicly available RNA methylation-related databases, and some important publicly available RNA-methylation-related Web servers and software for RNA methylation site identification, differential analysis and so on. Furthermore, we provided detailed analysis of the state-of-the-art computational models used in these Web servers and software. We also analyzed the limitations of these models and discussed the future directions of developing computational models for RNA methylation research. RNA methylation, biological function, disease, database, Web server and software, computational model RNA methylation Methylation is a form of alkylation in chemistry, which adds a methyl group on a substrate or substitutes the original atom or group. In biological systems, methylation reaction is catalyzed by a set of methyltransferases [1]. It contributes to epigenetic alterations as structural modification that does not affect gene sequence but regulates its expression. Methylation can occur in varieties of biomolecules including deoxyribonucleic acid (DNA), ribonucleic acid (RNA) and proteins. DNA methylation occurs on the carbon 5 of the pyrimidine ring of cytosines. It is established by methyltransferases DNMT3A and DNMT3B, and is maintained by DNMT1. Global and gene-specific patterns of DNA methylation are often dynamic in many important biological processes, which are closely related to disease. Thus, it has been an intensive area of research for the past 30 years [2]. Protein methylation has been widely studied in the histones. Methylation of histones occurs on lysines, arginines and histidines, in which lysine methylations are by far the best characterized. The patterns of histone methyl marks are altered in disease development, especially malignancies. Mutations in specific histone methylatransferases, demethylase and associated factors also have been reported in many cancers [3]. RNA is the intermediate molecule, which links genetic information contained in genes to its expression in functional proteins. In the past decade, noncoding RNA has been added as new players, which is likely to be further extended by the improvement of sequencing technologies. Methylation is involved in many steps of RNA biology and occurs in diverse RNA species such as transfer RNA (tRNA), ribosomal RNA (rRNA), messenger RNA (mRNA), transfer–messenger RNA, small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), microRNA (miRNA) and viral RNA. It is thought that RNA methylation may have existed before DNA methylation in the early stages of life evolving on earth [4]. In most cases, the function and biological consequences of these methylations remain elusive. However, thanks to the development of new analysis tools, the field of RNA nucleotide methylation is emerging. In recent years, the idea that dynamic RNA methylation plays active roles in gene regulation has been intensively studied. The molecular function of enzymes involved in RNA methylation has also been uncovered. These observations point to an important role of RNA methylation in cellular process and call for this area to be further studied from both theory and application of biomedicine. RNA methylation classification RNA methylation occurs in all species of organisms. Methylated RNA nucleotides are ubiquitous in life, and roughly two-third of the >100 chemically distinct RNA modifications involve the addition of methyl groups [5, 6]. However, the distribution of the different types of methylated nucleotides in different species is not uniform. For example, the methylated nucleotides Am, m1I, m2G and m22G are shared among eukaryotes and archaea, m5Um and m3C have not yet been detected in archaea, while m26A, N6-methyladenosine (m6A), N1-methyladenosine (m1A), Cm, 5-methylcytosine (m5C), Gm, m1G, N1-methyladenosine (m7G), m5U and Um form a pool common to archaea, eukaryotes and prokaryotes [7]. N6-Methyladenosine m6A was initially discovered in 1974. It was the first internal mRNA modification discovered and most prevalent in eukaryotic mRNA [8]. Early studies showed that every mammalian mRNA on average contains three to five m6A within consensus sequence. The m6A is installed by a methyltransferase complex [9]. The identification of a subunit METTL3 of the complex allowed scientists to examine m6A in model organisms [10]. The m6A methylation or demethylation activities have been shown to affect stability of transcriptional regulators and provide a dynamic and rapid response to cellular signals, environmental stimuli or programmed biological transformations [11–13]. 5-Methylcytosine m5C is a well-known epigenetic modification in rRNA and tRNA. Recent transcriptome-wide mapping of m5C in human RNA has uncovered >10 000 candidate m5C sites in mRNA and other noncoding RNAs [14]. The m5C is involved in metabolic processes including energy and lipid metabolism. Several m5C methyltransferases were thought to work on rRNA and tRNA and have binding sites on mRNA, suggesting that they take additional roles that impact mRNA [15, 16]. 2’-O-methylation Ribose 2’-O-methylation occurs in rRNA, tRNA, mRNA, snoRNA and small interfering RNA, etc., at adenosine (A), guanosine (G), cytidine (C) and uridine (U) nucleobases [17] and is ubiquitous in viruses, archaebacteria, eubacteria, yeasts, protists, fungi and higher eukaryotes [18]. 2’-O-methylation is involved in discrimination of mRNA [19]. The function of 2’-O-methylation is also suggested to protect the 3′ end of miRNA to protect the 3′ end of miRNA against polyuridylation preventing miRNA from poly(U)-mediated degradation [20]. N7-methylguanine The N7-methylated G cap structure is found at the 5′ ends of mature eukaryotic mRNAs. It is linked by an inverted 5′-5′ triphosphate bridge to the first nucleotide of the nascent transcript. The 5′-m7G cap structure plays a critical role in the life cycle of eukaryotic mRNA and is necessary for efficient gene expression and cell viability from yeast to human. It serves as both a positive and negative element in mRNA recruitment to stimulate canonical translation initiation while preventing binding to the ribosome via an alternative pathway [21]. N1-Methyladenosine The methylation on the N1 atom of A to form 1-methyladenosine has been found in tRNA. In cytosolic tRNA, the m1A modification occurs at five different positions 9, 14, 22, 57 and 58. The most well-studied m1A modifications are those occurring at nucleotide positions 9 and 58. The mechanism for formation of m1A has not yet been determined but is known to rely on a number of residues such as aspartate and glutamine in all families. The m1A modification plays a number of biological roles, for example, enhancing structural stability and inducing correct folding of the tRNA [22]. Pseudouridine Pseudouridine (Ψ) was discovered over 60 years ago [23]. Ψ modification provides an additional hydrogen-bonding donor that can significantly affect the secondary structure of RNA. More recently, transcriptome-wide mapping has uncovered hundreds of naturally occurring Ψ sites in human mRNA [24]. These sites are responsive to nutrition starvation and heat shock, suggesting pseudouridylation as a potential mechanism to rapidly adapt the translation landscape to environmental stress [25, 26]. 5-Hydroxymethylcytosine In 2009, Rao and colleagues found that human ten-eleven translocation (TET) proteins can oxidize 5mC to generate 5-hydroxymethylcytosine (5hmC). Every mammalian cell seems to contain 5hmC, but the levels vary significantly depending on the cell type [27]. Though the exact function of 5hmC is not fully elucidated, it is thought that it may regulate gene expression. The 5hmC may be especially important in the central nervous system, as it is found in high levels there. Reduction in the 5hmC levels has been found to be associated with impaired self-renewal in embryonic stem cells (ESCs). It is also associated with unstable nucleosomes, which are frequently repositioned during cell differentiation [28]. Adenosine to inosine editing Adenosine to inosine editing (A-to-I editing) is a cotranscriptional process that contributes to transcriptome complexity by deamination of adenosines to inosines [29]. It is accomplished by adenosine deaminases acting on RNAs (ADARs) [30]. The most recent deep-sequencing study suggests that >100 million sites in the human transcriptome might be subjected to A-to-I editing [31]. A few hundred A-to-I editing events can recode mRNAs, thus resulting in different proteins translation from their genomically encoded versions [32]. The other millions of editing events are largely located in the noncoding RNAs [33]. The biological consequences of these editing events are only partly understood, which may include RNA destabilization, changes in the folding of RNA or inosine-dependent suppression of immune responses [34, 35]. RNA methylation function The different function of methyl groups in RNA include biophysical, biochemical and metabolic stabilization of RNA; quality control; resistance to antibiotics; mRNA reading frame maintenance; deciphering of normal and altered genetic code; selenocysteine incorporation; tRNA aminoacylation; ribotoxins; splicing; intracellular trafficking; immune response; gene regulation; DNA repair; stress response; and possibly histone acetylation [36, 37]. In what follows, we will review the most important aspects of RNA methylation with what is known of their function (Table 1). Table 1. Functions of the various types of RNA methylation RNA methylation type  Main functions  m6A  Stability of transcriptional regulators [12], RNA splicing [38], translation [11], cell differentiation [39], circadian clock [40], DNA repair [41], sex determination [42], viral infection regulation [43], response to cellular signals, environmental stimuli [44] or programmed biological transformations [13]  m5C  Metabolic processes [16], mRNA process [15], stress response [45]  2’-O-methylation  Discrimination of mRNA [19], miRNA stability [20], viral infection regulation  m7G  Life cycle of eukaryotic mRNA, translation initiation, mRNA transport, splicing and degradation [21]  m1A  Structural stability and correct folding of the tRNA [22], translation initiation [46]  Ψ  Translation [25], response to nutrition starvation and heat shock [47]  5hmC  Self-renewal in ESCs, cell differentiation [28]  A-to-I editing  RNA destabilization, changes in the folding of RNA [34], immune responses [35]  RNA methylation type  Main functions  m6A  Stability of transcriptional regulators [12], RNA splicing [38], translation [11], cell differentiation [39], circadian clock [40], DNA repair [41], sex determination [42], viral infection regulation [43], response to cellular signals, environmental stimuli [44] or programmed biological transformations [13]  m5C  Metabolic processes [16], mRNA process [15], stress response [45]  2’-O-methylation  Discrimination of mRNA [19], miRNA stability [20], viral infection regulation  m7G  Life cycle of eukaryotic mRNA, translation initiation, mRNA transport, splicing and degradation [21]  m1A  Structural stability and correct folding of the tRNA [22], translation initiation [46]  Ψ  Translation [25], response to nutrition starvation and heat shock [47]  5hmC  Self-renewal in ESCs, cell differentiation [28]  A-to-I editing  RNA destabilization, changes in the folding of RNA [34], immune responses [35]  Table 1. Functions of the various types of RNA methylation RNA methylation type  Main functions  m6A  Stability of transcriptional regulators [12], RNA splicing [38], translation [11], cell differentiation [39], circadian clock [40], DNA repair [41], sex determination [42], viral infection regulation [43], response to cellular signals, environmental stimuli [44] or programmed biological transformations [13]  m5C  Metabolic processes [16], mRNA process [15], stress response [45]  2’-O-methylation  Discrimination of mRNA [19], miRNA stability [20], viral infection regulation  m7G  Life cycle of eukaryotic mRNA, translation initiation, mRNA transport, splicing and degradation [21]  m1A  Structural stability and correct folding of the tRNA [22], translation initiation [46]  Ψ  Translation [25], response to nutrition starvation and heat shock [47]  5hmC  Self-renewal in ESCs, cell differentiation [28]  A-to-I editing  RNA destabilization, changes in the folding of RNA [34], immune responses [35]  RNA methylation type  Main functions  m6A  Stability of transcriptional regulators [12], RNA splicing [38], translation [11], cell differentiation [39], circadian clock [40], DNA repair [41], sex determination [42], viral infection regulation [43], response to cellular signals, environmental stimuli [44] or programmed biological transformations [13]  m5C  Metabolic processes [16], mRNA process [15], stress response [45]  2’-O-methylation  Discrimination of mRNA [19], miRNA stability [20], viral infection regulation  m7G  Life cycle of eukaryotic mRNA, translation initiation, mRNA transport, splicing and degradation [21]  m1A  Structural stability and correct folding of the tRNA [22], translation initiation [46]  Ψ  Translation [25], response to nutrition starvation and heat shock [47]  5hmC  Self-renewal in ESCs, cell differentiation [28]  A-to-I editing  RNA destabilization, changes in the folding of RNA [34], immune responses [35]  Transcription and RNA splicing m6A modification exists in the mRNAs of various kinds of viruses. Occurrence of m6A in viral mRNA was shown to enhance the priming efficiency of mRNA [48–50]. Besides transcription efficiency, transcription kinetics are also likely affected by m6A modification. In human, antibiotic-induced deafness is caused by pathogenic mutation A1555G in mitochondrial genomic, which is located in close proximity to the m6A modification site, which establishes a link between human disease, mitochondrial transcription and 12 S rRNA methylation [51]. Pre-mRNA splicing is an essential step in gene expression. It involves precise excision of introns and joining of exons from primary transcripts in the nucleus to generate mature mRNA [52]. Emerging evidences support the correlation of m6A with RNA splicing. The regulatory role of m6A in mRNA splicing was reported in the study of fat mass and obesity-associated protein (FTO)-depleted 3T3-L1 preadipocytes. The researchers found that enhanced m6A level in response to FTO depletion promotes RNA-binding ability of splicing regulatory protein SRSF2, leading to increased inclusion of target exons [53]. A recent study also demonstrated that m6A-mediated mRNA structure remodeling affected the binding to HNRNPC, which was an abundant nuclear RNA-binding protein responsible for pre-mRNA processing and alternative splicing [38]. These data provide strong evidence on a mechanistic relationship between the presence of m6A and splicing events. mRNA translation The enrichment of m6A in exons and around the stop codon regions makes it conceivable that m6A may regulate translation. In a recent study performed in mouse ESCs and embryonic bodies, m6A writer METTL3 ablation significantly increased translation efficiency, indicating a regulatory role of m6A in translation [39]. The m6A reader, YTHDF1, was reported to interact with initiation factors and ribosomes to increase translational output, presenting direct evidence for translational regulation functions of m6A [54]. One of the translation factors, eIF3, was also reported to directly bind 5′ untranlated region (UTR) m6A, which was sufficient to recruit the 43 S complex to initiate translation in the absence of the cap-binding factor eIF4E [55]. m1A is also a widespread and conserved posttranscriptional modification that is associated with translation initiation in thousands of mammalian transcripts characterized by structured 5′UTR [46, 56]. Extensive translation of circular RNAs Extensive pre-mRNA back-splicing generates numerous circular RNAs (circRNAs) in human transcriptome. Recently, Yang et al. reported that m6A promotes efficient initiation of protein translation from circRNAs in human cells. Further analyses through polysome profiling, computational prediction and mass spectrometry revealed that m6A-driven translation of circRNAs is wide spread, with hundreds of endogenous circRNAs having translation potential. This expands the coding landscape of human transcriptome, and suggests a role of circRNA-derived proteins in cellular responses to environmental stress [57]. Cell fate transition ESCs are pluripotent stem cells derived from the inner cell mass of a preimplantation embryo, exhibiting prolonged undifferentiated proliferation and stable developmental potential to form derivatives of all three embryonic germ layers [58]. The transition from naïve pluripotency to differentiation is tightly regulated by a plethora of pluripotency markers and developmental factors. Transcriptome-wide m6A profiling in mouse embryonic stem cells (mESCs) showed that the majority of these core pluripotent genes and developmental regulators have m6A modifications on their transcripts [59]. Recently, Geula et al. [39] demonstrated that the m6A modification plays a key role in facilitating transition of human embryonic stem cells (hESCs) from the naïve state to the primed state on differentiation. The maternal-to-zygotic transition (MZT) is one of the most profound and tightly orchestrated processes during the early life of embryos. Over one-third of zebrafish maternal mRNAs can be m6A modified. Removal of YTHDF2 in zebrafish embryos decelerates the decay of m6A-modified maternal mRNAs and impedes zygotic genome activation. These embryos fail to initiate timely MZT, undergo cell cycle pause and remain developmentally delayed throughout larval life [60]. Circadian clock The mechanism of the mammalian circadian clock involves a negative transcription–translation feedback loop in which the transcription of the clock genes is suppressed by their own encoded proteins. Recent work showed that inhibition of transmethylation reactions elongates the circadian period. RNA sequencing (RNA-seq) revealed methylation inhibition causes widespread changes in the transcription of the RNA processing machinery, associated with RNA m6A-methylation. Specific inhibition of m6A methylation by silencing of METTL3 is sufficient to elicit circadian period elongation and RNA processing delay [40]. DNA damage response Cell proliferation and survival require the faithful maintenance and propagation of genetic information, which are threatened by the ubiquitous sources of DNA damage present intracellularly and in the external environment. DNA damage response detects and repairs damaged DNA and prevents cell division until the repair is complete [61]. A recent study uncovered m6A in RNA is rapidly and transiently induced at DNA damage sites in response to ultraviolet irradiation. This modification occurs on numerous poly(A)+ transcripts and is regulated by METTL3 and FTO. m6A RNA serves as a beacon for the selective, rapid recruitment of Pol κ to DNA damage sites to facilitate repair and cell survival [41]. The recruitment of methyl-CpG-binding domain protein 2 (MBD2) to DNA damage sites after laser microirradiation also suggests that RNA methylation is related to laser-induced DNA damage response [62]. Heat shock response The researchers also found that diverse cellular stresses induced a transcriptome-wide redistribution of m6A, resulting in increased numbers of mRNAs with 5′UTR m6A, which thus presented a concept of dynamic m6A events in response to stress. A connection between tRNA methylation and stress response has been evidenced for Dnmt2 mediate formation of m5C38 in tRNAs in Drosophila melanogaster [45]. Recently, findings show that a few Ψ sites in yeast U2 snRNA are induced by nutrient deprivation or heat shock. Hundreds of mRNA Ψ are also induced by heat shock in yeast possibly affecting transcript stability [63]. In mammalian cells, m6A is preferentially deposited to the 5′UTR of newly transcribed mRNAs in response to heat shock stress. The increased 5′UTR methylation in the form of m6A promotes cap-independent translation initiation, providing a mechanism for selective mRNA translation under heat shock stress [44]. Neuronal functions Humans with a nonsynonymous mutation in the FTO enzymatic domain exhibit brain malformation and impaired brain function, and intronic FTO single-nucleotide polymorphisms have been associated with abnormal brain volumes in both adolescents and healthy elderly subjects. Analysis of mRNA methylation in dopaminergic neurons following FTO loss of function identified a subset of mRNAs whose m6A levels were influenced by FTO [64]. Many of these transcripts encode proteins involved in the response to dopamine, suggesting that FTO-mediated dynamic methylation of neuronal mRNAs is necessary for proper dopaminergic signaling. Loss of DNMT2-mediated m5C methylation increases tRNA stress-induced cleavage in flies and cleavage of tRNAs, and repression of protein translation is a conserved response to several stress stimuli in eukaryotes. Nuerodevelopmental disorders are commonly associated with oxidative stress, and increased tRNA cleavage has been recently directly linked to neurodevelopmental and neurodegenerative conditions [65]. Sex determination In Drosophila, fl(2)d and vir are required for sex-dependent regulation of alternative splicing of the sex determination factor sex lethal (Sxl). m6A is required for female-specific alternative splicing of Sxl, which determines female physiognomy, but also translationally represses male-specific lethal 2 (msl-2) to prevent dosage compensation in females [42, 66]. Virus infection Viral life cycles are usually regulated by precise mechanisms that act on their RNA [67]. The m6A was found on RNA of several viruses in 1970 s and hypothesized a new RNA regulatory control to viral infection [68]. Recently, a proviral role for m6A in HIV-1 infection has been found. The function of individual m6A sites in HIV-1 RNA can be varied from regulating HIV-1 RNA nuclear export to enhancing viral gene expression [43, 69, 70]. Using m6A-seq, m6A modifications were also mapped in several regions across the RNA of the Flaviviridae members hepatitis C virus (HCV), Zika virus (ZIKV), dengue virus, yellow fever virus and West Nile virus [71, 72]. In addition, the Kaposi’s sarcoma-associated herpesvirus (KSHV), mRNAs also undergo m6A modification. The blockage of m6A inhibited splicing of the pre-mRNA, a key KSHV lytic switch protein, replication transcription activator, and halted viral lytic replication [73]. The m6A on viral RNAs may prevent detection by host pattern recognition receptors that trigger antiviral innate immunity. It may serve as a shield on viral RNA to prevent induction of antiviral signaling pathways, which is important for the therapy of pathogen-associated diseases. RNA methylation and disease The study of RNA methylation has emerged as an exciting new research area over the past few years. It might represent an additional layer of gene regulation, leading to the coining of the terms, ‘RNA epigenetics’ and ‘epitranscriptomics’. The direct studies of the role of RNA methylation in disease have been rare; however, the methylases, demethylases and other related factors have been shown to have disease correlations. Here, we summarize our current knowledge about the genes directing these modifications in human disease (Table 2). Although RNA methylation research is still in its early stages, disruption of RNA methylation has been linked to a number of disease conditions. It suggests RNA methylation important pathogenesis and independent factor during the progression of diseases. Considering that the biological effects of RNA methylation in different diseases are vicarious, we also summarize the RNA methylation regulating genes in different diseases (Table 3). Here, we give examples for five important human diseases including obesity, neurodevelopmental disorders, cancer, dyskeratosis congenita and X-linked intellectual disability [82]. Table 2. Human disease associated with RNA methylation factors RNA methylation type  Factors  Human disease  m6A  WTAP (m6A writer)  Hypospadias [126]  Acute myelogennous leukemia [127]  Cholangiocarcinoma [128]  FTO (m6A eraser)  Obesity [75]  Coronary heart disease [129]  Type 2 diabetes [130]  Cancer [131]  ALKBH5 (m6A eraser)  Infertility [132]  Major depressive disorder [133]  m5C  NSUN2 (m5C RNA methyltransferase)  Breast cancer [134]  Autosomal recessive intellectual disability [135]  Amyotrophic lateral sclerosis [61]  Parkinson’s disease [78]  Ψ  DKC1 (RNAΨ Synthase)  Diskeratosis congenita [136]  Pituitary tumorigenesis [137]  Prostate cancer [138]  PUS1 (RNAΨ Synthase)  Mitochondrial myopathy, lactic acidosis and sideroblastic  Anemia [139]  A-to-I editing  ADAR1 (adenosine deaminase)  Chronic myeloid leukemia [140]  Metastatic melanoma [141]  Human hepatocellular carcinoma [142]  Esophageal squamous cell carcinoma [143]  ADAR2 (adenosine deaminase)  Glioblastoma multiforme [144]  Alzheimer’s disease [145]  RNA methylation type  Factors  Human disease  m6A  WTAP (m6A writer)  Hypospadias [126]  Acute myelogennous leukemia [127]  Cholangiocarcinoma [128]  FTO (m6A eraser)  Obesity [75]  Coronary heart disease [129]  Type 2 diabetes [130]  Cancer [131]  ALKBH5 (m6A eraser)  Infertility [132]  Major depressive disorder [133]  m5C  NSUN2 (m5C RNA methyltransferase)  Breast cancer [134]  Autosomal recessive intellectual disability [135]  Amyotrophic lateral sclerosis [61]  Parkinson’s disease [78]  Ψ  DKC1 (RNAΨ Synthase)  Diskeratosis congenita [136]  Pituitary tumorigenesis [137]  Prostate cancer [138]  PUS1 (RNAΨ Synthase)  Mitochondrial myopathy, lactic acidosis and sideroblastic  Anemia [139]  A-to-I editing  ADAR1 (adenosine deaminase)  Chronic myeloid leukemia [140]  Metastatic melanoma [141]  Human hepatocellular carcinoma [142]  Esophageal squamous cell carcinoma [143]  ADAR2 (adenosine deaminase)  Glioblastoma multiforme [144]  Alzheimer’s disease [145]  Table 2. Human disease associated with RNA methylation factors RNA methylation type  Factors  Human disease  m6A  WTAP (m6A writer)  Hypospadias [126]  Acute myelogennous leukemia [127]  Cholangiocarcinoma [128]  FTO (m6A eraser)  Obesity [75]  Coronary heart disease [129]  Type 2 diabetes [130]  Cancer [131]  ALKBH5 (m6A eraser)  Infertility [132]  Major depressive disorder [133]  m5C  NSUN2 (m5C RNA methyltransferase)  Breast cancer [134]  Autosomal recessive intellectual disability [135]  Amyotrophic lateral sclerosis [61]  Parkinson’s disease [78]  Ψ  DKC1 (RNAΨ Synthase)  Diskeratosis congenita [136]  Pituitary tumorigenesis [137]  Prostate cancer [138]  PUS1 (RNAΨ Synthase)  Mitochondrial myopathy, lactic acidosis and sideroblastic  Anemia [139]  A-to-I editing  ADAR1 (adenosine deaminase)  Chronic myeloid leukemia [140]  Metastatic melanoma [141]  Human hepatocellular carcinoma [142]  Esophageal squamous cell carcinoma [143]  ADAR2 (adenosine deaminase)  Glioblastoma multiforme [144]  Alzheimer’s disease [145]  RNA methylation type  Factors  Human disease  m6A  WTAP (m6A writer)  Hypospadias [126]  Acute myelogennous leukemia [127]  Cholangiocarcinoma [128]  FTO (m6A eraser)  Obesity [75]  Coronary heart disease [129]  Type 2 diabetes [130]  Cancer [131]  ALKBH5 (m6A eraser)  Infertility [132]  Major depressive disorder [133]  m5C  NSUN2 (m5C RNA methyltransferase)  Breast cancer [134]  Autosomal recessive intellectual disability [135]  Amyotrophic lateral sclerosis [61]  Parkinson’s disease [78]  Ψ  DKC1 (RNAΨ Synthase)  Diskeratosis congenita [136]  Pituitary tumorigenesis [137]  Prostate cancer [138]  PUS1 (RNAΨ Synthase)  Mitochondrial myopathy, lactic acidosis and sideroblastic  Anemia [139]  A-to-I editing  ADAR1 (adenosine deaminase)  Chronic myeloid leukemia [140]  Metastatic melanoma [141]  Human hepatocellular carcinoma [142]  Esophageal squamous cell carcinoma [143]  ADAR2 (adenosine deaminase)  Glioblastoma multiforme [144]  Alzheimer’s disease [145]  Table 3. The role of RNA methylation in different genes with heterogeneity of disease RNA methylation type  Related genes  Relative level in disease  Role in disease  Human disease  m6A  RUNX1T1  Hypo  Disease progression  Obesity [74]  miR-125b  Hyper  Tumor progression  Cancer [75]  IDH1/2  Hyper  Tumor progression  Acute myeloid leukemia [76]  m5C  tRNA  Hypo  Disease progression  Amyotrophic lateral sclerosis [64]  Parkinson’s disease [77]  Ψ  TERC  Hypo  Disease diagnosis  Diskeratosis congenita [25]  2’-O-methylation  FTSJ1  Hypo  Disease progression  X-linked intellectual disability [78]  A-to-I editing  PU.1  Hyper  Tumor progression  Chronic myeloid leukemia [79]  AZIN1  Hyper  Tumor progression  Human hepatocellular carcinoma [80]  GluA2  Hypo  Disease diagnosis  Alzheimer’s disease [81]  RNA methylation type  Related genes  Relative level in disease  Role in disease  Human disease  m6A  RUNX1T1  Hypo  Disease progression  Obesity [74]  miR-125b  Hyper  Tumor progression  Cancer [75]  IDH1/2  Hyper  Tumor progression  Acute myeloid leukemia [76]  m5C  tRNA  Hypo  Disease progression  Amyotrophic lateral sclerosis [64]  Parkinson’s disease [77]  Ψ  TERC  Hypo  Disease diagnosis  Diskeratosis congenita [25]  2’-O-methylation  FTSJ1  Hypo  Disease progression  X-linked intellectual disability [78]  A-to-I editing  PU.1  Hyper  Tumor progression  Chronic myeloid leukemia [79]  AZIN1  Hyper  Tumor progression  Human hepatocellular carcinoma [80]  GluA2  Hypo  Disease diagnosis  Alzheimer’s disease [81]  Table 3. The role of RNA methylation in different genes with heterogeneity of disease RNA methylation type  Related genes  Relative level in disease  Role in disease  Human disease  m6A  RUNX1T1  Hypo  Disease progression  Obesity [74]  miR-125b  Hyper  Tumor progression  Cancer [75]  IDH1/2  Hyper  Tumor progression  Acute myeloid leukemia [76]  m5C  tRNA  Hypo  Disease progression  Amyotrophic lateral sclerosis [64]  Parkinson’s disease [77]  Ψ  TERC  Hypo  Disease diagnosis  Diskeratosis congenita [25]  2’-O-methylation  FTSJ1  Hypo  Disease progression  X-linked intellectual disability [78]  A-to-I editing  PU.1  Hyper  Tumor progression  Chronic myeloid leukemia [79]  AZIN1  Hyper  Tumor progression  Human hepatocellular carcinoma [80]  GluA2  Hypo  Disease diagnosis  Alzheimer’s disease [81]  RNA methylation type  Related genes  Relative level in disease  Role in disease  Human disease  m6A  RUNX1T1  Hypo  Disease progression  Obesity [74]  miR-125b  Hyper  Tumor progression  Cancer [75]  IDH1/2  Hyper  Tumor progression  Acute myeloid leukemia [76]  m5C  tRNA  Hypo  Disease progression  Amyotrophic lateral sclerosis [64]  Parkinson’s disease [77]  Ψ  TERC  Hypo  Disease diagnosis  Diskeratosis congenita [25]  2’-O-methylation  FTSJ1  Hypo  Disease progression  X-linked intellectual disability [78]  A-to-I editing  PU.1  Hyper  Tumor progression  Chronic myeloid leukemia [79]  AZIN1  Hyper  Tumor progression  Human hepatocellular carcinoma [80]  GluA2  Hypo  Disease diagnosis  Alzheimer’s disease [81]  Obesity Genome-wide association studies linked common variants of FTO gene with childhood and adult obesity in 2007 [74, 83, 84]. The finding that FTO-mediated m6A demethylation controls exonic splicing of adipogenic regulatory factor RUNX1T1 emphasized the regulatory role of FTO in adipogenesis [74, 84]. Neurodevelopmental disorders Hereditary forms of intellectual disability are neurodevelopmental disorders [85]. Loss of cytosine-5 RNA methylation increases the angiogenin-mediated endonucleolytic cleavage of tRNA leading to an accumulation of 5′ tRNA-derived small RNA fragments. Accumulation of 5′ tRNA fragments in the absence of methyltransferase NSun2 reduces protein cell size and increased apoptosis of cortical, hippocampal and striatal neurons. Cytosine-5 methylation at the variable loop of tRNAs act as the main upstream regulator of angiogenin-dependent tRNA binding and cleavage. tRNAs lacking cytosine-5 methylation are prone to be cleaved by angiogenin, and altered tRNA cleavage because of mutation is also linked to neurodegenerative disease, such as amyotrophic lateral sclerosis and Parkinson’s [64, 77, 86]. Cancer Proteinase-activated receptor 2 (PAR2) participates in cancer metastasis promoted by serine proteinases. The PAR2 activation also represses miR-125 b expression, while miR-125 b mimic successfully blocks PAR2-induced cell migration. PAR2 activation increases the level of m6A-containing pre-miR-125 b in NSun2-dependent manner. NSun2-dependent RNA methylation contributes to the downregulation of miR-125 b to regulate cancer cell migration by altering miRNA expression [875]. Dyskeratosis congenita Dyskeratosis congenita can be caused by mutations in the non-coding RNA (ncRNA) telomerase component TERC. The reads distribution across TERC revealed a putative Ψ site at position 307. Ψ-seq of the hybrid-captured RNA confirmed substantial pseudouridylation of position 307, a highly conserved uridine in a region essential for telomerase activity and TERT binding, and showed that it is modified at significantly higher levels in the control sample than in the patient sample. This suggests that TERC pseudouridylation may be compromised in dyskeratosis congenita, and provides a general way to quantify Ψ in lowly expressed genes [25]. X-linked intellectual disability Mutations in human FTSJ1 can cause nonsyndromic X-linked intellectual disability (NSXLID). The tRNAPhe from two genetically independent cell lines of NSXLID patients with loss of function FTSJ1 mutations nearly completely lacks 2’-O-methylated C32 (Cm32) and 2’-O-methylated G34 (Gm34), and has reduced peroxywybutosine. These directly link defective 2’-O-methylation of the tRNA anticodon loop to FTSJ1 mutations, suggesting that the modification defects cause NSXLID, and may implicate Gm34 of tRNAPhe as the critical modification [78]. Databases RNAMDB The RNA Modification Database (RNAMDB) has served as a focal point for information pertaining to naturally occurring RNA modifications (http://rna-mdb.cas.albany.edu/RNAmods/) [6]. In its current state, the database uses an easy-to-use, searchable interface to obtain detailed data on the 109 currently known RNA modifications. Each entry provides the chemical structure, common name and symbol, elemental composition and mass, CA registry numbers and index name, phylogenetic source, type of RNA species in which it is found and references to the first reported structure determination and synthesis. MODOMICS MODOMICS is a database of RNA modifications that provides comprehensive information concerning the chemical structures of modified ribonucleotides, their biosynthetic pathways, RNA-modifying enzymes and location of modified residues in RNA sequences (http://modomics.genesilico.pl) [5]. It integrates information about the chemical structure of modified nucleotides, their localization in RNA sequences, pathways of their biosynthesis and enzymes that carry out the respective reactions. MODOMICS also provides literature information, and links to other databases, including the available protein sequence and structure data. RADAR RADAR includes a comprehensive collection of A-to-I RNA editing sites identified in humans (Homo sapiens), mice (Mus musculus) and flies (D.melanogaster), together with extensive manually curated annotations for each editing site (http://RNAedit.com) [87]. RADAR also includes an expandable listing of tissue-specific editing levels for each editing site, which will facilitate the assignment of biological functions to specific editing sites. MeT-DB The MethylTranscriptome DataBase (MeT-DB) is the first comprehensive resource for m6A in mammalian transcriptome (http://compgenomics.utsa.edu/methylation/) [88]. It includes a database that records publicly available data sets from methylated RNA immunoprecipitation sequencing (MeRIP-Seq), a recently developed technology for interrogating m6A methyltranscriptome. MeT-DB includes ∼300k m6A methylation sites in 74 MeRIP-Seq samples from 22 different experimental conditions predicted by exomePeak and MACS2 algorithms. To explore this rich information, MeT-DB also provides a genome browser to query and visualize context-specific m6A methylation under different conditions. MeT-DB also includes the binding site data of miRNA, splicing factor and RNA-binding proteins in the browser window for comparison with m6A sites and for exploring the potential functions of m6A. RMBase RMBase (RNA Modification Base) is developed to decode the genome-wide landscape of RNA modifications identified from high-throughput modification data generated by 18 independent studies (http://mirlab.sysu.edu.cn/rmbase/) [89]. The current release of RMBase includes ∼9500 Ψ modifications generated from Pseudo-seq and CeU-seq sequencing data, ∼1000 m5C predicted from Aza-IP data, ∼124 200 m6A modifications discovered from m6A-seq and ∼1210 2'-O-methylations identified from RiboMeth-seq data and public resources. Moreover, RMBase provides a comprehensive listing of other experimentally supported types of RNA modifications by integrating various resources. REDIportal REDIportal is the largest and comprehensive collection of RNA editing in humans including >4.5 million of A-to-I events detected in 55 body sites from thousands of RNA-seq experiments (http://srv00.recas.ba.infn.it/atlas/) [90]. REDIportal embeds RADAR database and represents the first editing resource designed to answer functional questions, enabling the inspection and browsing of editing levels in a variety of human samples, tissues and body sites. In contrast with previous RNA editing databases, REDIportal comprises its own browser (JBrowse) that allows users to explore A-to-I changes in their genomic context, empathizing repetitive elements in which RNA editing is prominent. Web server and software Sequence-based site prediction Web server or software HAMR HAMR is a high-throughput method to map RNA modifications within all classes of RNAs by identifying mis-incorporation of nucleotides by reverse transcriptase (RT) during production of complementary DNA (cDNA) products (http://wanglab.pcbi.upenn.edu/hamr) [91]. Users may submit a link to a remote indexed BAM (read alignment) file to the online version of HAMR. HAMR detects candidate modification sites either transcriptome-wide or at selected loci specified by transcript ID or genomic coordinates. Users may also opt to filter out known dbSNP sites for human data and select various options affecting the stringency of the analysis, including P-value or false discovery rate (FDR) thresholds, minimum coverage and which null hypothesis to use. M6Apred M6Apred is a support vector machine (SVM)-based model to identify m6A sites in the Saccharomyces cerevisiae transcriptome by using the nucleotide chemical property and nucleotide density information (http://lin.uestc.edu.cn/server/m6Apred.php) [92]. In this model, RNA sequences are encoded by their nucleotide chemical property and accumulated nucleotide frequency information. iRNA-Methyl iRNA-Methyl formulates RNA sequences with the ‘pseudo dinucleotide composition’ (PseDNC), which incorporates three RNA physiochemical properties (http://lin.uestc.edu.cn/server/iRNA-Methyl) [93]. It was observed by the rigorous cross-validation test on the benchmark data set that the accuracy achieved by the predictor in identifying m6A was 65.59%. All benchmark data can be downloaded from the Data window of this Web server. PPUS PPUS is the first Web server to predict pseudo uridine synthase (PUS)-specific Ψ sites (http://lyh.pkmu.cn/ppus/) [94]. PPUS used SVM as the classifier and used nucleotides around Ψ sites as the features. Currently, PPUS could accurately predict new Ψ sites for PUS1, PUS4 and PUS7 in yeast and PUS4 in human. AthMethPre AthMethPre is a method to predict the m6A sites for Arabidopsis thaliana mRNA sequence(s) (http://bioinfo.tsinghua.edu.cn/AthMethPre/index.html) [95]. To predict the m6A sites of an mRNA sequence, the SVM was used to build a classifier using the features of the positional flanking nucleotide sequence and position-independent k-mer nucleotide spectrum. The server also provides a comprehensive database of predicted transcriptome-wide m6A sites and curated m6A-seq peaks from literatures for query and visualization. RNAMethPre RNAMethPre integrated multiple features of mRNA (flanking sequences, local secondary structure information and relative position information) and trained a SVM classifier to predict m6A sites in mammalian mRNA sequences (http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html) [96]. Given an mRNA as well as its corresponding species information, the server returns all predicted m6A sites to users. The results are also downloadable for further analysis. The SVM model was also applied to predict transcriptome-wide m6A sites. Experimental m6A-seq peaks were collected from literatures. The Web server was built to provide both prediction and query services for m6A sites. A genome browser was also built based on JBrowse to visualize the query results. M6ATH M6ATH is an SVM-based method proposed to identify m6A sites in A. thaliana transcriptome (http://lin.uestc.edu.cn/server/M6ATH) [97]. The proposed method was validated on a benchmark data set using jackknife test and was also validated by identifying strain-specific m6A sites in A. thaliana. For the convenience of scientific community, a freely accessible online Web server was established. PRNAm-PC In pRNAm-PC, RNA sequence samples are expressed by a novel mode of PseDNC whose components were derived from a physical–chemical matrix via a series of auto-covariance and cross-covariance transformations (http://www.jci-bioinfo.cn/pRNAm-PC) [98]. SRAMP To depict the sequence context around m6A sites, SRAMP combines three Random Forest classifiers that exploit the positional nucleotide sequence pattern, the k-nearest neighbor (kNN) information and the position-independent nucleotide pair spectrum features, respectively (http://www.cuilab.cn/sramp/) [99]. SRAMP accepts either genomic sequences or cDNA sequences as its input. It only requires nucleotide sequences for prediction. Users can select either the full transcript mode or the mature mRNA mode, depending on whether they have the genomic or the cDNA sequence at hand, and whether they are interested in the intronic m6A sites. Users can also decide whether the RNA secondary structure should be considered. Analysis of RNA secondary structures provides text and graphical representation of the local structure around the predicted m6A site. RAMPred RAMPred is proposed to identify m1A sites in H.sapiens, M.musculus as well as S.cerevisiae genomes for the first time (http://lin.uestc.edu.cn/server/RAMPred) [100]. In this method, RNA sequences are encoded by using nucleotide chemical property and nucleotide compositions. iRNA-PseU The Web server iRNA-PseU was developed to identify the Ψ sites in H.sapiens, M.musculus and S.cerevisiae (http://lin.uestc.edu.cn/server/iRNA-PseU) [101]. It incorporated the chemical properties of nucleotides and their occurrence frequency density distributions into the general form of pseudo K-tuple nucleotide composition (PseKNC). MethyRNA MethyRNA is an SVM-based model to identify m6A sites by encoding RNA sequence using nucleotide chemical property and frequency based on the high-resolution experimental data of H.sapiens and M.musculus (http://lin.uestc.edu.cn/server/methyrna) [102]. It was observed by the rigorous cross-validation test with accuracy of 90.38 and 88.89% for identifying m6A in former mentioned species, respectively. RAM-ESVM RAM-ESVM was developed for detecting m6A sites from S.cerevisiae transcriptome, which used ensemble SVM classifiers and novel sequence features (http://server.malab.cn/RAM-ESVM/) [103]. RAM-ESVM combined three basic classifiers, namely, SVM-PseKNC, SVM-motif and GkmSVM, which were constructed by using PseKNC, motif features and optimized k-mer as discriminal features, respectively. RAM-NPPS RAM-NPPS is a sequence-based predictor for identifying m6A sites within RNA sequences (http://server.malab.cn/RAM-NPPS/) [104]. Users can submit uncharacterized RNA sequences to identify the potential m6A sites. In particular, the online predictor provides m6A site identification specific for three species, such as S.cerevisiae, H.sapiens and A.thaliana. iRNA-AI iRNA-AI is a predictor to identify A-to-I editing sites based on the RNA sequence information alone (http://lin.uestc.edu.cn/server/iRNA-AI/) [105]. It has been proposed by incorporating the chemical properties of nucleotides and their sliding occurrence density distribution along a RNA sequence into the general form of pseudo nucleotide composition (PseKNC). iRNA-PseColl The iRNA-PseColl was formed by incorporating both the individual and collective features of the sequence elements into the general PseKNC of RNA via the chemicophysical properties and density distribution of its constituent nucleotides (http://lin.uestc.edu.cn/server/iRNA-PseColl) [106]. It was developed to identify RNA modifications in H.sapiens transcriptome. At present, the m1A, m6A and m5C can be identified based on the current platform. Next-generation sequencing (NGS) data-based site detection Web server or software MeRIP-PF MeRIP-PF is a novel high-efficiency and user-friendly analysis pipeline for the signal identification of MeRIP-Seq data in reference to controls (http://software.big.ac.cn/MeRIP-PF.html) [107]. MeRIP-PF provides a statistical P-value for each identified m6A region based on the difference of read distribution when compared with the controls and also calculates FDR as a cutoff to differentiate reliable m6A regions from the background. Furthermore, MeRIP-PF also achieves gene annotation of m6A signals or peaks and produces outputs in both XLS and graphical format, which are useful for further study. exomePeak R/Bioconductor package The ‘exomePeak’ is an open-source R package for detecting RNA methylation sites under a specific experimental condition or identifying the differential RNA methylation sites in a case-control study from MeRIP-Seq data (http://www.bioconductor.org/packages/release/bioc/html/exomePeak.html) [108]. Using exomePeak R/Bioconductor package along with other software programs for analysis of MeRIP-Seq data, it can conduct raw reads alignment, RNA methylation site detection, motif discovery, differential RNA methylation analysis and functional analysis. meRanTK The meRanTK is the first publicly available tool kit, which addresses the special demands of high-throughput RNA cytosine methylation data analysis (http://icbi.at/software/meRanTK/) [109]. It provides fast and easy-to-use splice-aware bisulfite sequencing read mapping, comprehensive methylation calling and identification of differentially methylated cytosines by statistical analysis of single- and multi-replicate experiments. Application of meRanTK to RNA-BSseq or Aza-IP data produces accurate results in standard compliant formats.meRanTK includes five multithreaded programs, which enable complete analysis and comparison of m5C transcriptome data sets. The tools, meRanT and meRanG, use well-established RNAseq-specific short-read mappers as core aligning engines and extend them to facilitate mapping of either single- or paired-end sequence reads from strand-specific RNA-BSseq libraries to a given reference sequence. The meRanCall methylation caller uses aligned reads to precisely identify and statistically evaluate the positions of methylated cytosines. The experimental comparison tool meRanCompare is designed to detect differentially methylated m5Cs of two experimental conditions with single- or multi-replicate RNA methylation data sets. The annotation tool meRanAnnotate helps to annotate candidate m5Cs with genomic features such as gene or transcript names and positional metrics. DRME DRME is designed for differential RNA methylation analysis from the MeRIP-Seq data set at small sample size scenario using the negative binomial model (https://github.com/lzcyzm/DRME) [110]. The model not only captures within-group biological variability among replicates but also addresses the changes in RNA expression level and its impact on RNA methylation, and thus can be applied to MeRIP-Seq, particularly for differential RNA methylation analysis. The algorithm is also fast to execute and in theory can be applicable to other data types related to RNA such as RNA bisulfite sequencing and photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) without reads count rescaling or normalization. MeTPeak MeTPeak is a novel, graphical model-based peak-calling method for transcriptome-wide detection of m6A sites from MeRIP-seq data (https://github.com/compgenomics/MeTPeak) [111]. MeTPeak explicitly models read count of an m6A site and introduces a hierarchical layer of beta variables to capture the variances and a hidden Markov model (HMM) to characterize the reads dependency across a site. In addition, a constrained Newton’s method and a log-barrier function are developed to estimate analytically intractable, positively constrained beta parameters. MeTPeak deploys a hierarchical beta-binomial model to depict the variance of reads enrichment and an HMM to account for the dependency of neighboring enrichment. MeTPeak is an open-source R package, where core heavy computation part of the algorithm is written in C  ++. txCoords txCoords is a novel and easy-to-use Web application for transcriptomic peak remapping (http://www.bioinfo.tsinghua.edu.cn/txCoords) [112]. txCoords can be used to correct the incorrectly reported transcriptomic peaks and retrieve the true sequences. It also supports visualization of the remapped peaks in a schematic figure or from the UCSC Genome Browser. Annotation and visualization of RNA modification CAn Hauenschild et al. [113] developed a RNA modification visualization tool called CoverageAnalyzer (CAn), which allows the visualization and assisted inspection of RNA-seq profiles for RT signatures of modifications intuitively (https://sourceforge.net/projects/coverageanalyzer/). CAn takes SAM input data files from N user-specified samples as input file. Build in pipeline will create Pileup format and further convert to Profile files, which provide pair-wise information including position; reference base; coverage; mismatch rate M; number of (#) As, #Gs, #Ts and #Cs; and arrest rate A. Afterward, statistics are gathered for reference sequences including ID, file path, length, sequence, coverage peak, number of high-arrest sites, high mismatch sites, heterogeneous mismatch sites and mapped reads. Based on this information, users can manually sort or set threshold to filtering and visualize RNA modification RT signature on graphical user interface (GUI) software. CAn is highly conductive to the extraction of complete RT signatures, by providing full control of all thresholds for visualization, identification and discrimination to the user. MetaPlotR MetaPlotR is a Perl and R pipeline, to easily generate metagenes for any organism for which a genome and transcript annotation is available through the UCSC Genome Browser database (https://github.com/olarerin/metaPlotR) [114]. RCAS The RNA Centric Annotation System (RCAS) is an R package to ease the process of creating gene-centric annotations and analysis for the genomic regions of interest obtained from various RNA-based omics technologies (http://bioconductor.org/packages/release/bioc/html/RCAS) [115]. The RCAS R package uses different R functions to perform annotation summarization, GO term and gene set enrichment analysis and de novo sequence motif discovery. The Web interface allows users to upload a single BED file, which is used as the main input to RCAS and to select analysis module. Users can select one of four reference genome assemblies and select one annotation database for the gene set enrichment analysis module. The intervals in the BED file can optionally be down-sampled. On submission, the job is enqueued to run RCAS in the background and generate the specified HTML report. Once RCAS has generated the report, the requester can access it online or download it in a bundle along with any produced output files. Computational models RNA methylation has been found for decades of years, which occur at different RNA types of numerous species. As more and more research evidences have indicated that RNA methylation plays an important role in RNA splicing, posttranscriptional gene expression regulation, extensive translation of circRNA, neuronal functions and many different stages of RNA life cycle [116, 117]. This reversible RNA methylation adds a new dimension to the developing picture of posttranscriptional regulation of gene expression [118]. However, the experimental technologies are cost-ineffective for RNA methylation site prediction, RNA methylation function analysis directly. As complements to experimental techniques, computational models could facilitate the analysis based on RNA sequences or RNA-seq data. Here, we conclude the well-established methods for detecting potential RNA methylation sites. RNA methylation sites could be predicted based on powerful computational models in the following two ways. We could construct sequence-based models to predict potential RNA methylation sites based on training samples (known methylation sites versus nonmethylation sites) and unlabeled samples (genomic sequences or cDNA sequences). We can also predict the RNA methylation sites based on sequencing data, such as RNA-seq, MeRIP-sequencing, BS-sequencing, etc. Then, we can make further analysis based on the predicted sites, such as differential analysis, annotation, visualization, as well as functional analysis, etc. Sequence-based site prediction models HAMR During RT, modifications may lead to RT signatures, including RT arrestor mis-incorporation. RT signatures can manifest in the cDNA as either abortive or modification, respectively, which can be captured by RNA-seq. Ryvkin et al. [91] developed a statistical method to identify RNA modification sites based on nucleotide mis-incorporation by RT. The method detects modification by two hypotheses. The simplest null hypothesis assumes the site is homozygous with the reference allele. Taking this as the null hypothesis results in any nonreference nucleotide above the base-calling error rate being called as a candidate modification. A more conservative null hypothesis assumes only that the genotype is biallelic. Taking this as the null hypothesis results in site with three or more nucleotides that are sequenced at a rate higher than base-call errors being called as a candidate modification site. Besides, authors developed a kNN-based classifier to predict the modification type. Using small RNA-seq data, HAMR was able to detect 92% of all known human tRNA modification sites that are predicted to affect RT activity, and authors can distinguish two classes of A and two classes of G modifications with 98 and 79% accuracy, respectively. However, HAMR cannot distinguish single-nucleotide polymorphism (SNP) and RNA modification. Besides, HAMR is mainly built based on small RNA-seq and tRNA modification data. Therefore, the performance of HAMR in RNA modification detection for other data need to be further validated. PPUS Ψ is known to be catalyzed by PUS, and is found to present in different categories of noncoding RNAs such as tRNAs, rRNAs and snRNA. Li et al. [94] proposed a new platform called PPUS to identify PUS-specific Ψ sites. A sliding window strategy is used to get nucleotides around the Ψsites as classification features. Then, SVM classifier is followed to make the prediction of Ψ sites. However, PPUS can only identify Ψ sites in human and S.cerevisiae. iRNAMethyl Chen et al. [93] proposed PseDNC to incorporate both the local and global sequence pattern information of the queried RNA sequence, which is defined as:   D=[d1d2⋯d16d16+1⋯d16+λ]T (1) with   du={fu∑i=116fi+w∑j=1λθj1≤u≤16wθu−16∑i=116fi+w∑j=1λθj16<u≤16+λ (2) where fu(u=1,2,…,16) is the normalized occurrence frequency of the u-th nonoverlapping dinucleotides. λ is the number of the total counted ranks of the correlations along a RNA sequence, while w is the weight factor. The correlation factor θj represents the j-tier structural correlation factor between all the most contiguous dinucleotides:   θj=1L−j−1∑i=1L−j−1Ci,i+j   (j=1,2,…,λ;λ<L), (3) With Θi,i+j being the coupling factor given by:   Θi,i+j=1v∑u=1v[Pu(Di)−Pu(Di+j)]2, (4) where v is the number of RNA physicochemical properties considered. Finally, the feature vectors are fed into SVM for site prediction. Through jackknife test, iRNA-Methyl shows better prediction performance than traditional BLAST approach. m6Apred Chen et al. [92] developed a SVM-based computational model of m6Apred to identify m6A site in the S.cerevisiae transcriptome. To the best of our knowledge, m6Apred is the first sequence-based m6A site prediction model. m6Apred developed a sequence encoding method to depict the nucleotide chemical properties as well as the density information of each nucleotide in RNA sequences. The four different kinds of nucleotides found in RNA, adenine (A), guanine (G), cytosine (C) and uracil (U), are classified into three different groups in terms of chemical properties, such as chemical structure, chemical binding and chemical functionality, as shown in Table 4. Thus, each nucleotide Ni is defined by three coordinates (xi,yi,zi) with:   xi={1ifNi∈{A,G}0ifNi∈{C,U},yi={1ifNi∈{A,C}0ifNi∈{G,U},zi={1ifNi∈{A,U}0ifNi∈{C,G}. (5) Table 4. Chemical property of nucleotide in RNA sequence Chemical property  Class  Nucleotides  Ring structure  Purine  A, G  Pyrimidine  C, U  Functional group  Amino  A, C  Keto  G, U  Hydrogen bond  Strong  C, G  Weak  A, U  Chemical property  Class  Nucleotides  Ring structure  Purine  A, G  Pyrimidine  C, U  Functional group  Amino  A, C  Keto  G, U  Hydrogen bond  Strong  C, G  Weak  A, U  Table 4. Chemical property of nucleotide in RNA sequence Chemical property  Class  Nucleotides  Ring structure  Purine  A, G  Pyrimidine  C, U  Functional group  Amino  A, C  Keto  G, U  Hydrogen bond  Strong  C, G  Weak  A, U  Chemical property  Class  Nucleotides  Ring structure  Purine  A, G  Pyrimidine  C, U  Functional group  Amino  A, C  Keto  G, U  Hydrogen bond  Strong  C, G  Weak  A, U  Then, the density di of any nucleotide si at position i in RNA sequence is also included by:   di=1|si|∑j=1lf(sj),f(q)={1if sj=q0other cases. (6) Finally, the encoded nucleotide chemical property and nucleotide densities are fed into SVM for prediction. m6Apred obtains an area under the curve (AUC) of 0.84 in the jackknife test, showing the considerable accuracy in predicting m6A sites in yeast. More importantly, m6Apred is not sensitive to the selection of negative data, which are really difficult to obtain in practical problems. However, whether m6Apred can be used to predict mammalian m6A sites has not been tested. RAM-ESVM Chen et al. [103] developed an ensemble classifier, called RAM-ESVM, for detecting m6A sites in the S.cerevisiae genome. RAM-ESVM used PseDNC together with SVM (SVM-PseKNC), motif features together with SVM (SVM-motif) and GkmSVM as basic classifiers. PreDNC represents the RNA sequences, which is defined as:   D=[d1d2⋯d16d16+1⋯d16+λ]T (7) with   du={fk∑i=116fi+w∑j=1λθj1≤u≤16wθu−16∑i=116fi+w∑j=1λθj16<u≤16+λ, (8) where fk(k=1,2,…,16) is the normalized occurrence frequency of the nonoverlapping dinucleotides. λ is the number of the total counted ranks of the correlations along a RNA sequence, while w is the weight factor. The correlation factor θj represents the j-tier structural correlation factor between all the j-th most contiguous dinucleotides Di=RiRi+1,   θj=1L−j−k+1∑i=1L−j−k+1Θ(Di,Di+1)   (j=1,2,…,λ;λ<L), (9) with Θ() being the correlation function. In term of motif feature, each sequence is represented as a boolean vector. If the substring selected as motif feature appears in one sequence, the feature value is 1. Otherwise, the value is 0. GkmSVM is used in the next for gapped k-mer-based classification. Classification results of all the three classifiers vote for final prediction score, as shown in Figure 1. Figure 1. View largeDownload slide The flowchart of RAM-ESVM which have described the basic steps to predict m6A methylation site from S. cerevisiae transcriptome. It uses ensemble SVM classifiers as well as some novel sequence features. Figure 1. View largeDownload slide The flowchart of RAM-ESVM which have described the basic steps to predict m6A methylation site from S. cerevisiae transcriptome. It uses ensemble SVM classifiers as well as some novel sequence features. RNAMethPre Considering different input sequences, i.e. genomic sequences and cDNA sequences, Xiang et al. [96] developed an SVM-based model to predict m6A sites in human, mouse and mammal. The frame structure is shown in Figure 2. Features, such as nucleotide sequence position, nucleotide k-mer frequency, a relative position value calculated from the absolute distance from the transcript start site as well as the stability of the local structure, are combined and added to SVM classifier for predicting the m6A sites. Just like SRAMP, RNAMethPre provides the full transcript mode and mature mRNA mode. For performance enhancement, RNAMethPre integrates all the abovementioned four features for mature mRNA mode, but only include the former two features in the full transcript mode. The validation results show that the performance of RNAMethyPre is superior to that of SRAMP. Figure 2. View largeDownload slide The flowchart shows the basic idea of RNAMethPre, which is used to predict m6A sites in human, mouse and mammal. RNAMethPre uses multiple features of mRNA, such as flanking sequences, local secondary structure information and relative position information, and feeds them into a SVM classifier for m6A site prediction. Figure 2. View largeDownload slide The flowchart shows the basic idea of RNAMethPre, which is used to predict m6A sites in human, mouse and mammal. RNAMethPre uses multiple features of mRNA, such as flanking sequences, local secondary structure information and relative position information, and feeds them into a SVM classifier for m6A site prediction. SRAMP Zhou et al. [99] established a mammalian m6A sites predictor named SRAMP (sequence-based RNA adenosine methylation site predictor) under the Random Forest framework, which is shown in Figure 3. SRAMP considers the positional binary encoding of nucleotide sequence, the kNN encoding and the nucleotide pair spectrum encoding. In the positional binary encoding, four different kinds of nucleotides found in RNA, adenine (A), guanine (G), cytosine (C) and uracil (U), are translated as binary vectors of (1, 0, 0, 0), (0, 1, 0, 0), (0, 0, 1, 0) and (0, 0, 0, 1). Then, kNN encoding depicts how much the 21 nt flanking window of one query sample resembles those of other m6A sites. The flanking window of the query sample was first compared with all reference samples to obtain pair-wise similarity scores:   Pair-wise similarity=∑i=1WNUC44(qi,ri), (10) where qi and ri are the nucleotides at the ith position of the query sample and the reference sample‘s flanking windows, respectively. In term of third type of feature, the frequency of a spaced nucleotide pair npi is defined as:   Frequency(npi)=C(npi)W−d−1, (11) Figure 3. View largeDownload slide The flowchart shows the basic steps of SRAMP. SRAMP combines three Random Forest classifiers that exploit the positional nucleotide sequence pattern, the kNN information and the position-independent nucleotide pair spectrum features, respectively. Figure 3. View largeDownload slide The flowchart shows the basic steps of SRAMP. SRAMP combines three Random Forest classifiers that exploit the positional nucleotide sequence pattern, the kNN information and the position-independent nucleotide pair spectrum features, respectively. Where C(npi) is the count of npi inside a flanking window, W is the window size and d is the space between two nucleotides. SRAMP also considers secondary structure predicted by RNAfold as the classification feature. The secondary structures are classified into hairpin loop, multiple loop, interior loop, paired and bulged loop, which are encoded as binary vectors, respectively. Random Forest classifiers are then trained with each feature. Finally, the prediction scores of the Random Forest classifiers trained with different feature encodings were combined using the weighted summing formula shown below:   Scombined=∑i=1nαiSi. (12) Where the Si and αi are the prediction score and the weight for the classifier trained with the i-th encoding, respectively. n is the total number of classifiers taken into account. The overall AUROC for the full transcript mode from 5-fold cross validation (CV) is 0.891, showing that it achieves good performance in full transcript mode. However, the prediction performance in mature mRNA mode can be further improved. RNAMethylPred Jia et al. [119] proposed a new bioinformatics model, named RNAMethylPred for the large-scale, rapid identification of m6A site. It was developed by incorporating Bi-profile Bayes (BPB), dinucleotide composition (DNC) and kNN scores as selected features, deploying SVM as classifier to perform the predictions, shown in Figure 4. First, with BPB, the queried sequence s is encoded into a probability vector V=(p1,p2,…,pn,pn+1,…,p2n), where pi(i=1,2,…,n) denotes the posterior probability of each nucleic acid at i-th position in the positive samples, and pi(i=n+1,n+2,…,2n) denotes the posterior probability of each nucleic acid at the i-th position in the negative samples, with n being the length of queried sequences. Then, DNC were defined as:   Pab=NabNa•P′ab=Nabn−1, (13) Figure 4. View largeDownload slide The flowchart of RNAMethylPred, which have described the basic steps to identify m6A site. RNAMethylPred adopts BPB, dinucleotides composition and kNN scores for feature extractions, and then follows SVM for classification. Figure 4. View largeDownload slide The flowchart of RNAMethylPred, which have described the basic steps to identify m6A site. RNAMethylPred adopts BPB, dinucleotides composition and kNN scores for feature extractions, and then follows SVM for classification. Where ab stands for the adjoining dinucleotides, Nab stands for the number of the adjoining dinucleotides in an RNA segment sample, a• stands for the adjoining dinucleotides, • stands for any nucleotide and n is the length of RNA sample. Thus, DNC encoding can contain features from both Pab and Pab′. In the next, the kNNs of the queried sequence in both positive and negative sets are picked out according to RNA local sequence similarity to get the kNN score, which can be formulated by following:   S(A,B)=∑1≤i≤nScore(A[i],B[i]), (14) Where A[i] and B[i] represent for the nucleotide at position i in both RNA sequence fragments. The similarity score for two nucleotides a and b is defined as:   Score={+2,if a=b−1,others. (15) The kNN score is achieved by calculating the percentage of the positive neighbors in its kNNs. As a result, RNAMethylPred achieves an accuracy of 76.51% and an MCC of 0.5302, showing the considerable performance in predicting m6A sites. iRNA-PseU Chen et al. [101] developed a new predictor called iRNA-PseU to identify Ψ sites. iRNA-PseU follows iRNA-PseColl to encode the nucleotide chemical property and nucleotide density, which are deployed as classification features in the following SVM classifier to make the prediction of Ψ sites. iRNA-PseU shows to achieve considerable accuracy in jackknife test on the benchmark data sets of human, mice and S.cerevisiae. RAMPred m1A has been found to have major influences on the structure and function of tRNA and rRNA. Chen et al. [100] further developed a new platform called RAMPred to identify the occurrence sites of m1A modifications across species of human, mice and S.cerevisiae transcriptome. RAMPred uses almost the same structure of m6Apred, and the validation results show that RAMPred can get satisfactory performance in predicting m1A modifications. iRNA-PseColl Feng et al. [106] developed a new platform called iRNA-PseColl to identify the occurrence sites for several different types of RNA modifications. iRNA-PseColl is the first method designed for multiple RNA modifications. iRNA-PseColl incorporates both individual and collective features of the sequence elements as features, as shown in Figure 5. Local features of the ith nucleotides Ni=(xi,yi,zi) are the same as that defined in m6Apred method. The occurrence frequency of a nucleotide for its distribution along the sequence is defined as:   Di=1||Li||∑j=1ℓf(Nj), (16) Figure 5. View largeDownload slide The flowchart of iRNA-PseColl. It aims at identification of occurrence sites for multiple RNA modification. It incorporates both local and collective features for classification, and SVM is adopted as the final classifier. Figure 5. View largeDownload slide The flowchart of iRNA-PseColl. It aims at identification of occurrence sites for multiple RNA modification. It incorporates both local and collective features for classification, and SVM is adopted as the final classifier. Where Di is the density of the nucleotide Ni at the site i of a RNA sequence, ||Li|| the length of the sliding substring concerned, ℓ denotes each of the site locations counted in the substring and   f(Nj)={1,if Nj=the nucleotide concerned0,otherwise. (17) Then, we combine the local feature and collective feature for the i-th nucleotide, which is defined by a set of four variables:   Ni=(xi,yi,zi,Di). (18) Finally, the prediction is achieved by SVM based on the features defined in (20). Validation results show that iRNA-PseColl can get considerable performance in predicting different types of RNA modifications. RAM-NPPS Xing et al. [104] proposed a sequence-based predictor called RAM-NPPS for identifying m6A sites within RNA sequences of different species. RAM-NPPS first encodes the input sequences by nucleotide pair position specificity (NPPS) algorithm. Then, the resulting feature vectors are joined together as the input for the following SVM classifier to make prediction. Figure 6 shows the NPPS feature encoding process. For the queried RNA sequence, it can be encoded by:   P=P+−P−, (19) Figure 6. View largeDownload slide The workflow of RAM-NPPS. It is based on multi-interval NPPS for feature extraction, and SVM classifier for prediction of m6A sites within RNA sequences. Figure 6. View largeDownload slide The workflow of RAM-NPPS. It is based on multi-interval NPPS for feature extraction, and SVM classifier for prediction of m6A sites within RNA sequences. Where P+ is formulated as:   P+=p1+p2+⋯pk+⋯pl−1+pl+, (20) with pk represents the k-th nucleotide, and l is the length of the sequence. Two matrices Ts+ and Td+ are defined to calculate pk+. Ts+ has size of 4×l representing the single-nucleotide occurrence probability. Rows represent {A,C,G,U}, respectively, and columns represent the length of the sequence. Td+ has size of 16×l representing the occurrence probability of nucleotide pair, with rows representing {A,C,G,U}×{A,C,G,U}. Suppose the dinucleotide between the k-th nucleotide and ( k+ξ)-th nucleotide is ‘AB’, then pk+=P(A∩B)P(B)=Fab,k+fb,k+ξ+, where ab is the index of ‘AB’ in {A,C,G,U}×{A,C,G,U}, and b is the index of ‘B’ in the {A,C,G,U}. The evaluation on three data sets shows that RAM-UPPS is effective and robust for the identification of m6A sites cross-different species. However, running of RAM-NPPS results in really heavy computation load. NGS data-based site detection models exomePeak Meng et.al. 120] proposed a pipeline for the analysis of MeRIP-seq data by combining several existing tools with a novel exome-based peak-calling and differential analysis approach, shown in Figure 7. exomePeak first extracts and connects all the exons of a specific gene and then detects peaks using a sliding window with C-test to determine the methylation site. Thus, it can be considered as projecting transcriptome onto the genome, and thus avoid the transcriptome heterogeneity. The test results show that it can achieve fairly robust m6A peak detection. However, it has two major limitations. First, exomePeak does not model the reads variance within transcripts and across replicates. Second, exomePeak ignores the dependency of reads enrichment, and thus may miss the true peaks with low enrichment. Finally, exomePeak tries to call peaks by bin-based method, which makes it difficult to get close to base solution. Figure 7. View largeDownload slide The basic steps of exomePeak, which provides a general pipeline for MeRIP-Seq data processing, predict m6A sites for samples from single condition, or identify differentially methylated sites for samples from multiple conditions. Figure 7. View largeDownload slide The basic steps of exomePeak, which provides a general pipeline for MeRIP-Seq data processing, predict m6A sites for samples from single condition, or identify differentially methylated sites for samples from multiple conditions. MeTPeak Cui et al. [111] further developed a graphic model-based peak-calling algorithm, MeTPeak, to detect m6A sites from MeRIP-seq data. It detects m6A peaks on each gene separately by dividing the particular gene into N bins with length of sequencing fragment L. The mixture of beta-binomial distribution is set up to describe the reads count in each bin, while an HMM is adopted to depict the reads dependency between the continuous bins. To be more specific, the reads count in the m-th pair of IP and input samples in bin n are denoted as Xmn and Ymn, which both follow Poisson distribution with parameters SIP,m, Sinput,m, λIP,m and λinput,m. SIP,m and Sinput,mare total reads in the m-th IP and input samples, respectively, and λIP,m and λinput,mare the normalized Poisson rates. With a priori of beta distribution for pn, which represents the methylation percentage at n-th bin, Xmn follows the beta-binomial distribution:   P(Xmn|Zn;α,β)=∏k=12(CΓ(Xmn+αk)Γ(Ymn+βk)Γ(αk+βk)Γ(Tmn+αk+βk)Γ(αk)Γ(βk)), (21) where Zn∈[1,2] denotes the unknown hidden methylation status with 1 representing methylated and 2 otherwise. Tmn=Xmn+Ymn. C=Γ(Tmn+1)Γ(Xmn+1)Γ(Ymn+1) is the normalization constant. α=[α1α2]T, β=[β1β2]T are the unknown parameters in the model, and they are shared for all bins across replications, and thus can somehow depict the variance of reads count across replicates. Then, an iterative Expectation Maximization (EM) algorithm is conducted to predict methylation sites Zn and model parameters. As a result, MeTPeak is shown to be more robust against data variance, small replicates and data outlier, and is more sensitive to lowly enriched peaks than exomePeak. However, limitations still existed in MeTPeak. For example, the search of m6A site is only limited to annotated genes. MeTPeak is also based on bin method, which makes it hard to get base solution. meRanTK Rieder etal. [109] developed a tool kit for the analysis of RNA-BSseq or Aza-IP data to detect m5C methylation site. To our knowledge, it is the first specialized software for RNA-BSseq data analysis. meRanTK includes five multithreaded programs, such as meRanT, meRanG, meRanCall, meRanCompare and meRanAnnotate, as shown in Figure 8. Among these tools, both meRanT and meRanG are RNA-BSseq alignment tools. The difference between these two alignment tools is that the former one maps reads to a preassembled set of transcripts, while the later one maps to a bisulfite-converted genome. meRanCall then extracts the methylation state of individual cytosine from the previous alignment. Based on the detected m5C sites, meRanCompare can help to identify whether the site is differentially methylation in two experimental conditions, while meRanAnnotate can assign genomic annotations and distance measurements to each individual candidate m5Cs. Figure 8. View largeDownload slide The workflow of meRanTK, which is specialized for RNA-BSseq data mapping, alignment, m5C site detection and annotation. Figure 8. View largeDownload slide The workflow of meRanTK, which is specialized for RNA-BSseq data mapping, alignment, m5C site detection and annotation. m6aViewer Antanaviciute etal. [121] developed a cross-platform, m6AViewer, for the detection, analysis and visualization of m6A peaks from MeRIP-seq data. The workflow is shown in Figure 9, where sorted and index BAM files are fed into m6aViewer as inputs. Then, sequencing adapter contamination and poor-quality bases are removed by Cutadapt software, followed by sequence alignment to reference genomes. The resulting alignments are sorted and indexed using SAMtools to obtain BAM files subsequent peak-calling module. For the preprocess step, the fragment coverage distribution is counted to compensate for the bias of reads coverage distribution; thus, it can achieve precise determination of sequenced RNA fragment coverage. Then, base-level candidate peak position is identified with local maxima as well as Fisher’s exact test. High-confidence peaks are called by combination of P-value and FDR-based cutoffs, as well as IP enrichment and c overage filters. In terms of the situation that peaks arising from multiple m6A sites in close proximity, which are visually indistinguishable cannot be identified accurately, m6aViewer proposed a mixture distribution-based approach to deconvolute overlapping peaks and pinpoint m6A methylation sites with increased precision. It considers the fragment coverage distribution in an enriched region as a mixture of coverage distributions. EM algorithm is adopted to establish the combination of mixtures best depict the observed RNA fragment distribution. To avoid overfitting, Bayesian information criterion is used to account for both the likelihood and the model complexity. M6aViewer combined the above sequence-based model with a feature-based approach to achieve practicable precision and recall rates. Features including transcript information, sequence composition, sequencing data features surrounding the peak or conservation information are obtained and fed into the subsequent Random Forest classifiers, whose results vote for the final classification score. Validation on multiple published m6A-seq data sets shows that m6aViewer can identify high-confidence methylated residues with more precision than other current existed approaches. Figure 9. View largeDownload slide The basic steps of m6aViewer, which can identify high-confidence methylated residues more precisely. It also provides a GUI for convenience. Figure 9. View largeDownload slide The basic steps of m6aViewer, which can identify high-confidence methylated residues more precisely. It also provides a GUI for convenience. Discussion and conclusion RNA is the intermediate molecule between DNA and proteins in the chain that links genetic information contained in genes to its expression in functional proteins, by either carrying this information in the form of mRNA or participating in mRNA expression, splicing, stability and translation in the form of noncoding RNAs. RNA methylation is a reversible posttranslational modification to RNA that adds a new dimension to the developing picture of gene expression regulation. It has been known to play critical roles in multiple biological processes by the advances in RNA detection and sequencing technologies. However, the sequencing protocol of RNA methylation is highly different from previous sequencing technologies. Although some databases, Web server and software as well as computational models have been established for RNA methylation, the function of most RNA methylations and their alterations in biological processes and human diseases are largely unknown for the lack of effective and precise processing methods [122]. A significant proportion of m6A methylation sites are enriched in the 5′UTR, around stop codon and the proximal region of 3′UTR of transcripts, while miRNA-targeted sites at the 5′ end and 3′ end of 3′ UTR suggested a potential link between m6A methylation and miRNA targeting sites, thus may regulate miRNA-related pathways [123–125]. To be more specific, miRNA miR-145 has been suggested by bioinformatics analysis that it might target the 3′UTR region of YTHDF2 MRNA, which is an m6A reader protein helping to recognize mRNA m6A sites to mediate mRNA degradation [140]. Another research showed that manipulation of miRNA expression or sequences altered m6A modification levels through modulating the binding of METTL3 methyltransferase to mRNA containing miRNA targeting sites, thus regulating m6A formation of mRNAs [142]. A knockdown of m6A demethylase FTO has been found to affect the steady-state levels of certain miRNAs [145]. Therefore, the prediction of associations between RNA methylation and miRNAs has great interest in its biogenesis and other fields. It will help better understand the complex regulation effect of both miRNA and RNA methylation. Furthermore, most studies focused on the association between m6A methylation and miRNAs. It can be further expanded to the other types of RNA methylation as well. Aberrant m6A modification patterns have been linked to diverse human diseases, including infertility, various forms of cancer, obesity, diabetes, depression and neurodevelopmental disorders, etc. For example, the m6A hyper methylation of IDH1/2 is found to play an important role in tumor progression, which can finally result in acute myeloid leukemia [76]. The m6A hypo methylation of RUNX1T1 is also found to participate in the disease progression in obesity. Thus, different methylation status in different genes may result in different phenotype. But it is really difficult and expensive to find the pathology of different types of RNA methylation in different diseases from experimental aspect only. Therefore, the prediction of RNA methylation–disease association, which can strongly guide the biological experiments, is of great significance in biological, medical and other fields. Based on network or machine learning models, the association probability between RNA methylation sites and diseases could be quantified and RNA methylation site–disease pairs with high confidence could be selected for further biological experimental validation. Thus, it will help understand the biogenesis, regulation and function of RNA methylation and human disease molecular mechanism at transcriptomic level, discover biomarkers and drugs for human disease diagnosis, treatment, prognosis and prevention with less time and cost of biological experiments. The first step to predict associations between RNA methylation and diseases or miRNAs is to identify the RNA methylation site precisely. Then, some new databases that annotate RNA methylation sequences provide the comprehensive information of RNA methylation sites or display and collect the experimentally confirmed RNA methylation–disease associations should be built for further analysis. To be more specific, some network-based models or machine learning models can be built to predict the association between diseases and those predicted sites based on those databases. Recently, scientists focused on building computational models to predict RNA methylation sites based on either sequence or high-throughput sequencing data, which is proved to be more time and cost-effective than biological methods. In this article, we summarized the known types of RNA methylation, the biological functions of RNA methylation, five RNA methylation-related diseases, seven publicly available RNA methylation-related databases, RNA methylation annotation, visualization tools, etc. Then, we introduced some state-of-art Web server and software as well as computational models for RNA methylation site prediction as well as differential analysis (Table 5). Most Web servers aim at identifying methylation sites based on sequence, and most Web servers are constructed following the five-step guidelines: (1) How to construct a valid benchmark data set to train and test the subsequent predictor? (2) How to represent the biological sequence with an valid mathematical formulation? (3) How to develop an effective prediction algorithm? (4) How to conduct the cross-validation to objectively estimate the performance of the predictor? (5) How to establish a user-friendly interface? These Web servers first build training data and testing data referring to RNA methylation motif DRACH (where D = A, G or U; R = A or G; H = A, C or U). Then, they follow different feature encoding scheme for discriminable features and fed them into classifiers for RNA methylation site prediction. Most sequence-based methods used SVM or SVM-based model as classifier. Some models, such as SRAMP adopted Random Forest and kNN for site prediction. With more discriminable features discovered in the future, prediction accuracy could be further improved. Table 5. Comparison list of the databases, Web servers and software Name  Type  Year of first version  RNA methylation type  Objection  Species  Maintained  Link  Databases  RNAMDB  Database  2011  N/A  A database for RNA modifications  N/A  No  http://rna-mdb.cas.albany.edu/RNAmods/  MODOMICS  Database  2013  N/A  A database of RNA modifications  N/A  Yes  http://modomics.genesilico.pl  RADAR  Database  2014  A to I  Collection of A-to-I RNA editing sites with annotation  Homo sapiens, M. musculus, D. melanogaster  Yes  http://RNAedit.com  MeT-DB  Database  2015  m6A  A database for publicaly available m6A data sets  N/A  Yes  http://compgenomics.utsa.edu/methylation/  RMBase  Database  2015  m6A, m5C, 2'-O- methylation, Ψ  A database for RNA modifications  N/A  System maintaining  http://mirlab.sysu.edu.cn/rmbase/  REDIportal  Database  2016  A to I  Collection of A-to-I RNA editing sites with annotation  N/A  Yes  http://srv00.recas.ba.infn.it/atlas/  Sequence-based site prediction tools  HAMR  Web server  2013  N/A  To predict RNA modification site (location and methylation class)  N/A  Yes  http://wanglab.pcbi.upenn.edu/hamr  M6Apred  Web server  2015  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://lin.uestc.edu.cn/server/m6Apred.php  iRNA-Methyl  Web server  2015  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-Methyl  PPUS  Web server  2015  Ψ  To predict Ψ site  N/A  Yes  http://lyh.pkmu.cn/ppus/  AthMethPre  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  No  http://bioinfo.tsinghua.edu.cn/AthMethPre/index.html  RNAMethPre  Web server  2016  m6A  To predict m6A site  Homo sapiens, M. musculus  No  http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html  m6ATH  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  Yes  http://lin.uestc.edu.cn/server/M6ATH  PRNAm-PC  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.jci-bioinfo.cn/pRNAm-PC  SRAMP  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.cuilab.cn/sramp/  RAMPred  Web server  2016  m1A  To predict m1A site  N/A  Yes  http://lin.uestc.edu.cn/server/RAMPred  iRNA-PseU  Web server  2016  Ψ  To predict Ψ site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseU  MethyRNA  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/methyrna  RAM-ESVM  Web server  2017  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://server.malab.cn/RAM-ESVM/  RAM-NPPS  Web server  2017  m6A  To predict m6A site  N/A  Yes  http://server.malab.cn/RAM-NPPS/  iRNA-AI  Web server  2017  A to I  To predict A-to-I site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-AI/  iRNA-PseColl  Web server  2017  m1A, m6A, m5C  To predict the occurrence sites of RNA modifications  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseColl  NGS data-based site detection tools  MeRIP-PF  Software  2013  m6A  To predict m6A-modified peaks  N/A  Yes  http://software.big.ac.cn/MeRIP-PF.html  exomePeak R/Bioconductor package  Software  2013  m6A  Alignment, site detection, motif discovery, differential analysis based on MeRIP-Seq data  N/A  Yes  http://www.bioconductor.org/packages/release/bioc /html/exomePeak.html  meRanTK  Software  2016  N/A  Alignment, site detection, differential analysis based on BS-seq data  N/A  Yes  http://icbi.at/software/meRanTK/  DRME  Software  2016  N/A  Differential analysis  N/A  Yes  https://github.com/lzcyzm/DRME  MeTPeak  Software  2016  m6A  To predict m6A-modified peaks  N/A  Yes  https://github.com/compgenomics/MeTPeak  txCoords  Web server  2016  m6A  To predict m6A-modified peaks  N/A  No  http://www.bioinfo.tsinghua.edu.cn/txCoords  Annotation and visualization of RNA modification tools  CAn  Software  2016  N/A  Visualization and analysis of modification signature in deep-sequencing data  N/A  Yes  https://sourceforge.net/projects/coverageanalyzer/  MetaPlotR  Software  2017  N/A  Create metagene plot  N/A  Yes  https://github.com/olarerin/metaPlotR  RCAS  Software  2017  N/A  Annotation  N/A  Yes  http://bioconductor.org/packages/release/bioc/html/RCAS  Name  Type  Year of first version  RNA methylation type  Objection  Species  Maintained  Link  Databases  RNAMDB  Database  2011  N/A  A database for RNA modifications  N/A  No  http://rna-mdb.cas.albany.edu/RNAmods/  MODOMICS  Database  2013  N/A  A database of RNA modifications  N/A  Yes  http://modomics.genesilico.pl  RADAR  Database  2014  A to I  Collection of A-to-I RNA editing sites with annotation  Homo sapiens, M. musculus, D. melanogaster  Yes  http://RNAedit.com  MeT-DB  Database  2015  m6A  A database for publicaly available m6A data sets  N/A  Yes  http://compgenomics.utsa.edu/methylation/  RMBase  Database  2015  m6A, m5C, 2'-O- methylation, Ψ  A database for RNA modifications  N/A  System maintaining  http://mirlab.sysu.edu.cn/rmbase/  REDIportal  Database  2016  A to I  Collection of A-to-I RNA editing sites with annotation  N/A  Yes  http://srv00.recas.ba.infn.it/atlas/  Sequence-based site prediction tools  HAMR  Web server  2013  N/A  To predict RNA modification site (location and methylation class)  N/A  Yes  http://wanglab.pcbi.upenn.edu/hamr  M6Apred  Web server  2015  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://lin.uestc.edu.cn/server/m6Apred.php  iRNA-Methyl  Web server  2015  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-Methyl  PPUS  Web server  2015  Ψ  To predict Ψ site  N/A  Yes  http://lyh.pkmu.cn/ppus/  AthMethPre  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  No  http://bioinfo.tsinghua.edu.cn/AthMethPre/index.html  RNAMethPre  Web server  2016  m6A  To predict m6A site  Homo sapiens, M. musculus  No  http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html  m6ATH  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  Yes  http://lin.uestc.edu.cn/server/M6ATH  PRNAm-PC  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.jci-bioinfo.cn/pRNAm-PC  SRAMP  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.cuilab.cn/sramp/  RAMPred  Web server  2016  m1A  To predict m1A site  N/A  Yes  http://lin.uestc.edu.cn/server/RAMPred  iRNA-PseU  Web server  2016  Ψ  To predict Ψ site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseU  MethyRNA  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/methyrna  RAM-ESVM  Web server  2017  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://server.malab.cn/RAM-ESVM/  RAM-NPPS  Web server  2017  m6A  To predict m6A site  N/A  Yes  http://server.malab.cn/RAM-NPPS/  iRNA-AI  Web server  2017  A to I  To predict A-to-I site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-AI/  iRNA-PseColl  Web server  2017  m1A, m6A, m5C  To predict the occurrence sites of RNA modifications  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseColl  NGS data-based site detection tools  MeRIP-PF  Software  2013  m6A  To predict m6A-modified peaks  N/A  Yes  http://software.big.ac.cn/MeRIP-PF.html  exomePeak R/Bioconductor package  Software  2013  m6A  Alignment, site detection, motif discovery, differential analysis based on MeRIP-Seq data  N/A  Yes  http://www.bioconductor.org/packages/release/bioc /html/exomePeak.html  meRanTK  Software  2016  N/A  Alignment, site detection, differential analysis based on BS-seq data  N/A  Yes  http://icbi.at/software/meRanTK/  DRME  Software  2016  N/A  Differential analysis  N/A  Yes  https://github.com/lzcyzm/DRME  MeTPeak  Software  2016  m6A  To predict m6A-modified peaks  N/A  Yes  https://github.com/compgenomics/MeTPeak  txCoords  Web server  2016  m6A  To predict m6A-modified peaks  N/A  No  http://www.bioinfo.tsinghua.edu.cn/txCoords  Annotation and visualization of RNA modification tools  CAn  Software  2016  N/A  Visualization and analysis of modification signature in deep-sequencing data  N/A  Yes  https://sourceforge.net/projects/coverageanalyzer/  MetaPlotR  Software  2017  N/A  Create metagene plot  N/A  Yes  https://github.com/olarerin/metaPlotR  RCAS  Software  2017  N/A  Annotation  N/A  Yes  http://bioconductor.org/packages/release/bioc/html/RCAS  Table 5. Comparison list of the databases, Web servers and software Name  Type  Year of first version  RNA methylation type  Objection  Species  Maintained  Link  Databases  RNAMDB  Database  2011  N/A  A database for RNA modifications  N/A  No  http://rna-mdb.cas.albany.edu/RNAmods/  MODOMICS  Database  2013  N/A  A database of RNA modifications  N/A  Yes  http://modomics.genesilico.pl  RADAR  Database  2014  A to I  Collection of A-to-I RNA editing sites with annotation  Homo sapiens, M. musculus, D. melanogaster  Yes  http://RNAedit.com  MeT-DB  Database  2015  m6A  A database for publicaly available m6A data sets  N/A  Yes  http://compgenomics.utsa.edu/methylation/  RMBase  Database  2015  m6A, m5C, 2'-O- methylation, Ψ  A database for RNA modifications  N/A  System maintaining  http://mirlab.sysu.edu.cn/rmbase/  REDIportal  Database  2016  A to I  Collection of A-to-I RNA editing sites with annotation  N/A  Yes  http://srv00.recas.ba.infn.it/atlas/  Sequence-based site prediction tools  HAMR  Web server  2013  N/A  To predict RNA modification site (location and methylation class)  N/A  Yes  http://wanglab.pcbi.upenn.edu/hamr  M6Apred  Web server  2015  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://lin.uestc.edu.cn/server/m6Apred.php  iRNA-Methyl  Web server  2015  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-Methyl  PPUS  Web server  2015  Ψ  To predict Ψ site  N/A  Yes  http://lyh.pkmu.cn/ppus/  AthMethPre  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  No  http://bioinfo.tsinghua.edu.cn/AthMethPre/index.html  RNAMethPre  Web server  2016  m6A  To predict m6A site  Homo sapiens, M. musculus  No  http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html  m6ATH  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  Yes  http://lin.uestc.edu.cn/server/M6ATH  PRNAm-PC  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.jci-bioinfo.cn/pRNAm-PC  SRAMP  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.cuilab.cn/sramp/  RAMPred  Web server  2016  m1A  To predict m1A site  N/A  Yes  http://lin.uestc.edu.cn/server/RAMPred  iRNA-PseU  Web server  2016  Ψ  To predict Ψ site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseU  MethyRNA  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/methyrna  RAM-ESVM  Web server  2017  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://server.malab.cn/RAM-ESVM/  RAM-NPPS  Web server  2017  m6A  To predict m6A site  N/A  Yes  http://server.malab.cn/RAM-NPPS/  iRNA-AI  Web server  2017  A to I  To predict A-to-I site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-AI/  iRNA-PseColl  Web server  2017  m1A, m6A, m5C  To predict the occurrence sites of RNA modifications  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseColl  NGS data-based site detection tools  MeRIP-PF  Software  2013  m6A  To predict m6A-modified peaks  N/A  Yes  http://software.big.ac.cn/MeRIP-PF.html  exomePeak R/Bioconductor package  Software  2013  m6A  Alignment, site detection, motif discovery, differential analysis based on MeRIP-Seq data  N/A  Yes  http://www.bioconductor.org/packages/release/bioc /html/exomePeak.html  meRanTK  Software  2016  N/A  Alignment, site detection, differential analysis based on BS-seq data  N/A  Yes  http://icbi.at/software/meRanTK/  DRME  Software  2016  N/A  Differential analysis  N/A  Yes  https://github.com/lzcyzm/DRME  MeTPeak  Software  2016  m6A  To predict m6A-modified peaks  N/A  Yes  https://github.com/compgenomics/MeTPeak  txCoords  Web server  2016  m6A  To predict m6A-modified peaks  N/A  No  http://www.bioinfo.tsinghua.edu.cn/txCoords  Annotation and visualization of RNA modification tools  CAn  Software  2016  N/A  Visualization and analysis of modification signature in deep-sequencing data  N/A  Yes  https://sourceforge.net/projects/coverageanalyzer/  MetaPlotR  Software  2017  N/A  Create metagene plot  N/A  Yes  https://github.com/olarerin/metaPlotR  RCAS  Software  2017  N/A  Annotation  N/A  Yes  http://bioconductor.org/packages/release/bioc/html/RCAS  Name  Type  Year of first version  RNA methylation type  Objection  Species  Maintained  Link  Databases  RNAMDB  Database  2011  N/A  A database for RNA modifications  N/A  No  http://rna-mdb.cas.albany.edu/RNAmods/  MODOMICS  Database  2013  N/A  A database of RNA modifications  N/A  Yes  http://modomics.genesilico.pl  RADAR  Database  2014  A to I  Collection of A-to-I RNA editing sites with annotation  Homo sapiens, M. musculus, D. melanogaster  Yes  http://RNAedit.com  MeT-DB  Database  2015  m6A  A database for publicaly available m6A data sets  N/A  Yes  http://compgenomics.utsa.edu/methylation/  RMBase  Database  2015  m6A, m5C, 2'-O- methylation, Ψ  A database for RNA modifications  N/A  System maintaining  http://mirlab.sysu.edu.cn/rmbase/  REDIportal  Database  2016  A to I  Collection of A-to-I RNA editing sites with annotation  N/A  Yes  http://srv00.recas.ba.infn.it/atlas/  Sequence-based site prediction tools  HAMR  Web server  2013  N/A  To predict RNA modification site (location and methylation class)  N/A  Yes  http://wanglab.pcbi.upenn.edu/hamr  M6Apred  Web server  2015  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://lin.uestc.edu.cn/server/m6Apred.php  iRNA-Methyl  Web server  2015  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-Methyl  PPUS  Web server  2015  Ψ  To predict Ψ site  N/A  Yes  http://lyh.pkmu.cn/ppus/  AthMethPre  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  No  http://bioinfo.tsinghua.edu.cn/AthMethPre/index.html  RNAMethPre  Web server  2016  m6A  To predict m6A site  Homo sapiens, M. musculus  No  http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html  m6ATH  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  Yes  http://lin.uestc.edu.cn/server/M6ATH  PRNAm-PC  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.jci-bioinfo.cn/pRNAm-PC  SRAMP  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.cuilab.cn/sramp/  RAMPred  Web server  2016  m1A  To predict m1A site  N/A  Yes  http://lin.uestc.edu.cn/server/RAMPred  iRNA-PseU  Web server  2016  Ψ  To predict Ψ site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseU  MethyRNA  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/methyrna  RAM-ESVM  Web server  2017  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://server.malab.cn/RAM-ESVM/  RAM-NPPS  Web server  2017  m6A  To predict m6A site  N/A  Yes  http://server.malab.cn/RAM-NPPS/  iRNA-AI  Web server  2017  A to I  To predict A-to-I site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-AI/  iRNA-PseColl  Web server  2017  m1A, m6A, m5C  To predict the occurrence sites of RNA modifications  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseColl  NGS data-based site detection tools  MeRIP-PF  Software  2013  m6A  To predict m6A-modified peaks  N/A  Yes  http://software.big.ac.cn/MeRIP-PF.html  exomePeak R/Bioconductor package  Software  2013  m6A  Alignment, site detection, motif discovery, differential analysis based on MeRIP-Seq data  N/A  Yes  http://www.bioconductor.org/packages/release/bioc /html/exomePeak.html  meRanTK  Software  2016  N/A  Alignment, site detection, differential analysis based on BS-seq data  N/A  Yes  http://icbi.at/software/meRanTK/  DRME  Software  2016  N/A  Differential analysis  N/A  Yes  https://github.com/lzcyzm/DRME  MeTPeak  Software  2016  m6A  To predict m6A-modified peaks  N/A  Yes  https://github.com/compgenomics/MeTPeak  txCoords  Web server  2016  m6A  To predict m6A-modified peaks  N/A  No  http://www.bioinfo.tsinghua.edu.cn/txCoords  Annotation and visualization of RNA modification tools  CAn  Software  2016  N/A  Visualization and analysis of modification signature in deep-sequencing data  N/A  Yes  https://sourceforge.net/projects/coverageanalyzer/  MetaPlotR  Software  2017  N/A  Create metagene plot  N/A  Yes  https://github.com/olarerin/metaPlotR  RCAS  Software  2017  N/A  Annotation  N/A  Yes  http://bioconductor.org/packages/release/bioc/html/RCAS  Furthermore, some high-throughput sequencing data-based computational models, such as exomePeak, MeTPeak, m6aViewer, are developed to predict RNA methylation site, which can be further extended for differential analysis. exomePeak and MeTPeak are based on R environment, which rely on a bin-based method. exomePeak predicts a methylation site by testing the small, equally sized regions divided from the transcriptome. If the number of reads of an exact bin in the immunoprecipitated sample is higher than that in the input sample, the bin is predicted as a significantly enriched bin. Then, those consecutive significantly enriched bins are merged together to form a larger region as peak. MeTPeak further model those consecutive significantly enriched bins by an HMM model to form a peak. However, significantly enriched regions predicted by the above two methods can span a large range, which can only achieve rough site identification. M6aViewer is developed on Java. It aims at detecting high-confidence m6A peaks by an EM-based deconvolution method, which helps to pinpoint m6A methylation sites with better precision. M6aViewer also combined the sequencing data-based computational models with sequence-based predictor to enhance the performance of false-positive rate. A supervised ensemble learning classifier is built to distinguish true-positive m6A sites from false-positive peaks. It is worth noting that the integration of sequence-based computational models with sequencing data-based models could help to further improve the accuracy of site prediction. Nowadays, a wide range of databases, Web servers about methylation site prediction have been built, providing a variety of methods for RNA methylation data processing. However, most methods focus on m6A methylation. It is expected to build such resources for the other types of methylation. Furthermore, RNA methylation–miRNA association, RNA methylation–disease association databases, Web servers and computational models should be constructed in the near future, which would benefit biologists to experimentally unveil the functions, mechanisms of RNA methylation. Key Points We made a brief introduction of the functions of RNA methylation, eight types of most popular RNA methylation, seven publicly available RNA methylation-related databases some important publicly available RNA-methylation-related Web server, software and computational tools for RNA methylation site identification, differential analysis and so on. Developing effective computational models to precisely identify methylation sites based on sequence or sequencing data could benefit better understanding of complex functions of RNA methylation. Making full use of different types of data sources, such as sequencing data with different technologies, sequences, etc., could benefit more effective discovery of new RNA methylation functions. RNA methylation–miRNA regulatory patterns could be predicted based on powerful computational models. Complex RNA methylation–disease associations could be predicted based on powerful computational models. Funding Fundamental Research Funds for the Central Universities (2017XKQY083 to X.C.). Xing Chen, PhD, is a professor of School of Information and Control Engineering, China University of Mining and Technology. He is also the founding director of Institute of Bioinformatics, China University of Mining and Technology. His research interests include disease, noncoding RNAs, network pharmacology, complex network and machine learning. Ya-Zhou Sun, PhD, is a postdoctor of College of Computer Science and Software Engineering, Shenzhen University. Her research interests include disease, noncoding RNAs, genomics, DNA damage repair and precise medicine. Hui Liu, PhD, is an associate professor of School of Information and Control Engineering, China University of Mining and Technology. His interests include noncoding RNAs, computational biology and machine learning. Lin Zhang, PhD, is an associate professor of School of Information and Control Engineering, China University of Mining and Technology. Her interests include computational biology, statistical signal processing and Bayesian methods. Jian-Qiang Li, PhD, is an associate professor of College of Computer Science and Software Engineering, Shenzhen University. He is also the executive director of Institute of Network and Information Security, Shenzhen University. His research interests include bioinformatics, artificial intelligence, mobile medical and complex systems. Jia Meng, PhD, is an associate professor of Department of Biological Sciences, Xi’an Jiaotong-Liverpool University. His interests include epitranscriptome bioinformatics, statistical modeling and NGS data mining. References 1 Thauer RK. Biochemistry of methanogenesis: a tribute to Marjory Stephenson. 1998 Marjory Stephenson Prize Lecture. Microbiology  1998; 144( 9): 2377– 406. 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RNA methylation and diseases: experimental results, databases, Web servers and computational models

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© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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Abstract

Abstract Ribonucleic acid (RNA) methylation is a type of posttranscriptional modifications occurring in all kingdoms of life. It is strongly related to important biological process, thus making it linked to a number of human diseases. Owing to the development of high-throughput sequencing technology, plenty of achievement had been obtained in RNA methylation research recently. Meanwhile, various computational models have been developed to analyze and mining increasing RNA methylation data. In this review, we first made a brief introduction about eight types of most popular RNA methylation, the biological functions of RNA methylation, the relationship between RNA methylation and disease and five important RNA methylation-related diseases. The research of RNA methylation is based on sequencing data processing, and effective bioinformatics techniques can benefit better understanding of RNA methylation. We further introduced seven publicly available RNA methylation-related databases, and some important publicly available RNA-methylation-related Web servers and software for RNA methylation site identification, differential analysis and so on. Furthermore, we provided detailed analysis of the state-of-the-art computational models used in these Web servers and software. We also analyzed the limitations of these models and discussed the future directions of developing computational models for RNA methylation research. RNA methylation, biological function, disease, database, Web server and software, computational model RNA methylation Methylation is a form of alkylation in chemistry, which adds a methyl group on a substrate or substitutes the original atom or group. In biological systems, methylation reaction is catalyzed by a set of methyltransferases [1]. It contributes to epigenetic alterations as structural modification that does not affect gene sequence but regulates its expression. Methylation can occur in varieties of biomolecules including deoxyribonucleic acid (DNA), ribonucleic acid (RNA) and proteins. DNA methylation occurs on the carbon 5 of the pyrimidine ring of cytosines. It is established by methyltransferases DNMT3A and DNMT3B, and is maintained by DNMT1. Global and gene-specific patterns of DNA methylation are often dynamic in many important biological processes, which are closely related to disease. Thus, it has been an intensive area of research for the past 30 years [2]. Protein methylation has been widely studied in the histones. Methylation of histones occurs on lysines, arginines and histidines, in which lysine methylations are by far the best characterized. The patterns of histone methyl marks are altered in disease development, especially malignancies. Mutations in specific histone methylatransferases, demethylase and associated factors also have been reported in many cancers [3]. RNA is the intermediate molecule, which links genetic information contained in genes to its expression in functional proteins. In the past decade, noncoding RNA has been added as new players, which is likely to be further extended by the improvement of sequencing technologies. Methylation is involved in many steps of RNA biology and occurs in diverse RNA species such as transfer RNA (tRNA), ribosomal RNA (rRNA), messenger RNA (mRNA), transfer–messenger RNA, small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), microRNA (miRNA) and viral RNA. It is thought that RNA methylation may have existed before DNA methylation in the early stages of life evolving on earth [4]. In most cases, the function and biological consequences of these methylations remain elusive. However, thanks to the development of new analysis tools, the field of RNA nucleotide methylation is emerging. In recent years, the idea that dynamic RNA methylation plays active roles in gene regulation has been intensively studied. The molecular function of enzymes involved in RNA methylation has also been uncovered. These observations point to an important role of RNA methylation in cellular process and call for this area to be further studied from both theory and application of biomedicine. RNA methylation classification RNA methylation occurs in all species of organisms. Methylated RNA nucleotides are ubiquitous in life, and roughly two-third of the >100 chemically distinct RNA modifications involve the addition of methyl groups [5, 6]. However, the distribution of the different types of methylated nucleotides in different species is not uniform. For example, the methylated nucleotides Am, m1I, m2G and m22G are shared among eukaryotes and archaea, m5Um and m3C have not yet been detected in archaea, while m26A, N6-methyladenosine (m6A), N1-methyladenosine (m1A), Cm, 5-methylcytosine (m5C), Gm, m1G, N1-methyladenosine (m7G), m5U and Um form a pool common to archaea, eukaryotes and prokaryotes [7]. N6-Methyladenosine m6A was initially discovered in 1974. It was the first internal mRNA modification discovered and most prevalent in eukaryotic mRNA [8]. Early studies showed that every mammalian mRNA on average contains three to five m6A within consensus sequence. The m6A is installed by a methyltransferase complex [9]. The identification of a subunit METTL3 of the complex allowed scientists to examine m6A in model organisms [10]. The m6A methylation or demethylation activities have been shown to affect stability of transcriptional regulators and provide a dynamic and rapid response to cellular signals, environmental stimuli or programmed biological transformations [11–13]. 5-Methylcytosine m5C is a well-known epigenetic modification in rRNA and tRNA. Recent transcriptome-wide mapping of m5C in human RNA has uncovered >10 000 candidate m5C sites in mRNA and other noncoding RNAs [14]. The m5C is involved in metabolic processes including energy and lipid metabolism. Several m5C methyltransferases were thought to work on rRNA and tRNA and have binding sites on mRNA, suggesting that they take additional roles that impact mRNA [15, 16]. 2’-O-methylation Ribose 2’-O-methylation occurs in rRNA, tRNA, mRNA, snoRNA and small interfering RNA, etc., at adenosine (A), guanosine (G), cytidine (C) and uridine (U) nucleobases [17] and is ubiquitous in viruses, archaebacteria, eubacteria, yeasts, protists, fungi and higher eukaryotes [18]. 2’-O-methylation is involved in discrimination of mRNA [19]. The function of 2’-O-methylation is also suggested to protect the 3′ end of miRNA to protect the 3′ end of miRNA against polyuridylation preventing miRNA from poly(U)-mediated degradation [20]. N7-methylguanine The N7-methylated G cap structure is found at the 5′ ends of mature eukaryotic mRNAs. It is linked by an inverted 5′-5′ triphosphate bridge to the first nucleotide of the nascent transcript. The 5′-m7G cap structure plays a critical role in the life cycle of eukaryotic mRNA and is necessary for efficient gene expression and cell viability from yeast to human. It serves as both a positive and negative element in mRNA recruitment to stimulate canonical translation initiation while preventing binding to the ribosome via an alternative pathway [21]. N1-Methyladenosine The methylation on the N1 atom of A to form 1-methyladenosine has been found in tRNA. In cytosolic tRNA, the m1A modification occurs at five different positions 9, 14, 22, 57 and 58. The most well-studied m1A modifications are those occurring at nucleotide positions 9 and 58. The mechanism for formation of m1A has not yet been determined but is known to rely on a number of residues such as aspartate and glutamine in all families. The m1A modification plays a number of biological roles, for example, enhancing structural stability and inducing correct folding of the tRNA [22]. Pseudouridine Pseudouridine (Ψ) was discovered over 60 years ago [23]. Ψ modification provides an additional hydrogen-bonding donor that can significantly affect the secondary structure of RNA. More recently, transcriptome-wide mapping has uncovered hundreds of naturally occurring Ψ sites in human mRNA [24]. These sites are responsive to nutrition starvation and heat shock, suggesting pseudouridylation as a potential mechanism to rapidly adapt the translation landscape to environmental stress [25, 26]. 5-Hydroxymethylcytosine In 2009, Rao and colleagues found that human ten-eleven translocation (TET) proteins can oxidize 5mC to generate 5-hydroxymethylcytosine (5hmC). Every mammalian cell seems to contain 5hmC, but the levels vary significantly depending on the cell type [27]. Though the exact function of 5hmC is not fully elucidated, it is thought that it may regulate gene expression. The 5hmC may be especially important in the central nervous system, as it is found in high levels there. Reduction in the 5hmC levels has been found to be associated with impaired self-renewal in embryonic stem cells (ESCs). It is also associated with unstable nucleosomes, which are frequently repositioned during cell differentiation [28]. Adenosine to inosine editing Adenosine to inosine editing (A-to-I editing) is a cotranscriptional process that contributes to transcriptome complexity by deamination of adenosines to inosines [29]. It is accomplished by adenosine deaminases acting on RNAs (ADARs) [30]. The most recent deep-sequencing study suggests that >100 million sites in the human transcriptome might be subjected to A-to-I editing [31]. A few hundred A-to-I editing events can recode mRNAs, thus resulting in different proteins translation from their genomically encoded versions [32]. The other millions of editing events are largely located in the noncoding RNAs [33]. The biological consequences of these editing events are only partly understood, which may include RNA destabilization, changes in the folding of RNA or inosine-dependent suppression of immune responses [34, 35]. RNA methylation function The different function of methyl groups in RNA include biophysical, biochemical and metabolic stabilization of RNA; quality control; resistance to antibiotics; mRNA reading frame maintenance; deciphering of normal and altered genetic code; selenocysteine incorporation; tRNA aminoacylation; ribotoxins; splicing; intracellular trafficking; immune response; gene regulation; DNA repair; stress response; and possibly histone acetylation [36, 37]. In what follows, we will review the most important aspects of RNA methylation with what is known of their function (Table 1). Table 1. Functions of the various types of RNA methylation RNA methylation type  Main functions  m6A  Stability of transcriptional regulators [12], RNA splicing [38], translation [11], cell differentiation [39], circadian clock [40], DNA repair [41], sex determination [42], viral infection regulation [43], response to cellular signals, environmental stimuli [44] or programmed biological transformations [13]  m5C  Metabolic processes [16], mRNA process [15], stress response [45]  2’-O-methylation  Discrimination of mRNA [19], miRNA stability [20], viral infection regulation  m7G  Life cycle of eukaryotic mRNA, translation initiation, mRNA transport, splicing and degradation [21]  m1A  Structural stability and correct folding of the tRNA [22], translation initiation [46]  Ψ  Translation [25], response to nutrition starvation and heat shock [47]  5hmC  Self-renewal in ESCs, cell differentiation [28]  A-to-I editing  RNA destabilization, changes in the folding of RNA [34], immune responses [35]  RNA methylation type  Main functions  m6A  Stability of transcriptional regulators [12], RNA splicing [38], translation [11], cell differentiation [39], circadian clock [40], DNA repair [41], sex determination [42], viral infection regulation [43], response to cellular signals, environmental stimuli [44] or programmed biological transformations [13]  m5C  Metabolic processes [16], mRNA process [15], stress response [45]  2’-O-methylation  Discrimination of mRNA [19], miRNA stability [20], viral infection regulation  m7G  Life cycle of eukaryotic mRNA, translation initiation, mRNA transport, splicing and degradation [21]  m1A  Structural stability and correct folding of the tRNA [22], translation initiation [46]  Ψ  Translation [25], response to nutrition starvation and heat shock [47]  5hmC  Self-renewal in ESCs, cell differentiation [28]  A-to-I editing  RNA destabilization, changes in the folding of RNA [34], immune responses [35]  Table 1. Functions of the various types of RNA methylation RNA methylation type  Main functions  m6A  Stability of transcriptional regulators [12], RNA splicing [38], translation [11], cell differentiation [39], circadian clock [40], DNA repair [41], sex determination [42], viral infection regulation [43], response to cellular signals, environmental stimuli [44] or programmed biological transformations [13]  m5C  Metabolic processes [16], mRNA process [15], stress response [45]  2’-O-methylation  Discrimination of mRNA [19], miRNA stability [20], viral infection regulation  m7G  Life cycle of eukaryotic mRNA, translation initiation, mRNA transport, splicing and degradation [21]  m1A  Structural stability and correct folding of the tRNA [22], translation initiation [46]  Ψ  Translation [25], response to nutrition starvation and heat shock [47]  5hmC  Self-renewal in ESCs, cell differentiation [28]  A-to-I editing  RNA destabilization, changes in the folding of RNA [34], immune responses [35]  RNA methylation type  Main functions  m6A  Stability of transcriptional regulators [12], RNA splicing [38], translation [11], cell differentiation [39], circadian clock [40], DNA repair [41], sex determination [42], viral infection regulation [43], response to cellular signals, environmental stimuli [44] or programmed biological transformations [13]  m5C  Metabolic processes [16], mRNA process [15], stress response [45]  2’-O-methylation  Discrimination of mRNA [19], miRNA stability [20], viral infection regulation  m7G  Life cycle of eukaryotic mRNA, translation initiation, mRNA transport, splicing and degradation [21]  m1A  Structural stability and correct folding of the tRNA [22], translation initiation [46]  Ψ  Translation [25], response to nutrition starvation and heat shock [47]  5hmC  Self-renewal in ESCs, cell differentiation [28]  A-to-I editing  RNA destabilization, changes in the folding of RNA [34], immune responses [35]  Transcription and RNA splicing m6A modification exists in the mRNAs of various kinds of viruses. Occurrence of m6A in viral mRNA was shown to enhance the priming efficiency of mRNA [48–50]. Besides transcription efficiency, transcription kinetics are also likely affected by m6A modification. In human, antibiotic-induced deafness is caused by pathogenic mutation A1555G in mitochondrial genomic, which is located in close proximity to the m6A modification site, which establishes a link between human disease, mitochondrial transcription and 12 S rRNA methylation [51]. Pre-mRNA splicing is an essential step in gene expression. It involves precise excision of introns and joining of exons from primary transcripts in the nucleus to generate mature mRNA [52]. Emerging evidences support the correlation of m6A with RNA splicing. The regulatory role of m6A in mRNA splicing was reported in the study of fat mass and obesity-associated protein (FTO)-depleted 3T3-L1 preadipocytes. The researchers found that enhanced m6A level in response to FTO depletion promotes RNA-binding ability of splicing regulatory protein SRSF2, leading to increased inclusion of target exons [53]. A recent study also demonstrated that m6A-mediated mRNA structure remodeling affected the binding to HNRNPC, which was an abundant nuclear RNA-binding protein responsible for pre-mRNA processing and alternative splicing [38]. These data provide strong evidence on a mechanistic relationship between the presence of m6A and splicing events. mRNA translation The enrichment of m6A in exons and around the stop codon regions makes it conceivable that m6A may regulate translation. In a recent study performed in mouse ESCs and embryonic bodies, m6A writer METTL3 ablation significantly increased translation efficiency, indicating a regulatory role of m6A in translation [39]. The m6A reader, YTHDF1, was reported to interact with initiation factors and ribosomes to increase translational output, presenting direct evidence for translational regulation functions of m6A [54]. One of the translation factors, eIF3, was also reported to directly bind 5′ untranlated region (UTR) m6A, which was sufficient to recruit the 43 S complex to initiate translation in the absence of the cap-binding factor eIF4E [55]. m1A is also a widespread and conserved posttranscriptional modification that is associated with translation initiation in thousands of mammalian transcripts characterized by structured 5′UTR [46, 56]. Extensive translation of circular RNAs Extensive pre-mRNA back-splicing generates numerous circular RNAs (circRNAs) in human transcriptome. Recently, Yang et al. reported that m6A promotes efficient initiation of protein translation from circRNAs in human cells. Further analyses through polysome profiling, computational prediction and mass spectrometry revealed that m6A-driven translation of circRNAs is wide spread, with hundreds of endogenous circRNAs having translation potential. This expands the coding landscape of human transcriptome, and suggests a role of circRNA-derived proteins in cellular responses to environmental stress [57]. Cell fate transition ESCs are pluripotent stem cells derived from the inner cell mass of a preimplantation embryo, exhibiting prolonged undifferentiated proliferation and stable developmental potential to form derivatives of all three embryonic germ layers [58]. The transition from naïve pluripotency to differentiation is tightly regulated by a plethora of pluripotency markers and developmental factors. Transcriptome-wide m6A profiling in mouse embryonic stem cells (mESCs) showed that the majority of these core pluripotent genes and developmental regulators have m6A modifications on their transcripts [59]. Recently, Geula et al. [39] demonstrated that the m6A modification plays a key role in facilitating transition of human embryonic stem cells (hESCs) from the naïve state to the primed state on differentiation. The maternal-to-zygotic transition (MZT) is one of the most profound and tightly orchestrated processes during the early life of embryos. Over one-third of zebrafish maternal mRNAs can be m6A modified. Removal of YTHDF2 in zebrafish embryos decelerates the decay of m6A-modified maternal mRNAs and impedes zygotic genome activation. These embryos fail to initiate timely MZT, undergo cell cycle pause and remain developmentally delayed throughout larval life [60]. Circadian clock The mechanism of the mammalian circadian clock involves a negative transcription–translation feedback loop in which the transcription of the clock genes is suppressed by their own encoded proteins. Recent work showed that inhibition of transmethylation reactions elongates the circadian period. RNA sequencing (RNA-seq) revealed methylation inhibition causes widespread changes in the transcription of the RNA processing machinery, associated with RNA m6A-methylation. Specific inhibition of m6A methylation by silencing of METTL3 is sufficient to elicit circadian period elongation and RNA processing delay [40]. DNA damage response Cell proliferation and survival require the faithful maintenance and propagation of genetic information, which are threatened by the ubiquitous sources of DNA damage present intracellularly and in the external environment. DNA damage response detects and repairs damaged DNA and prevents cell division until the repair is complete [61]. A recent study uncovered m6A in RNA is rapidly and transiently induced at DNA damage sites in response to ultraviolet irradiation. This modification occurs on numerous poly(A)+ transcripts and is regulated by METTL3 and FTO. m6A RNA serves as a beacon for the selective, rapid recruitment of Pol κ to DNA damage sites to facilitate repair and cell survival [41]. The recruitment of methyl-CpG-binding domain protein 2 (MBD2) to DNA damage sites after laser microirradiation also suggests that RNA methylation is related to laser-induced DNA damage response [62]. Heat shock response The researchers also found that diverse cellular stresses induced a transcriptome-wide redistribution of m6A, resulting in increased numbers of mRNAs with 5′UTR m6A, which thus presented a concept of dynamic m6A events in response to stress. A connection between tRNA methylation and stress response has been evidenced for Dnmt2 mediate formation of m5C38 in tRNAs in Drosophila melanogaster [45]. Recently, findings show that a few Ψ sites in yeast U2 snRNA are induced by nutrient deprivation or heat shock. Hundreds of mRNA Ψ are also induced by heat shock in yeast possibly affecting transcript stability [63]. In mammalian cells, m6A is preferentially deposited to the 5′UTR of newly transcribed mRNAs in response to heat shock stress. The increased 5′UTR methylation in the form of m6A promotes cap-independent translation initiation, providing a mechanism for selective mRNA translation under heat shock stress [44]. Neuronal functions Humans with a nonsynonymous mutation in the FTO enzymatic domain exhibit brain malformation and impaired brain function, and intronic FTO single-nucleotide polymorphisms have been associated with abnormal brain volumes in both adolescents and healthy elderly subjects. Analysis of mRNA methylation in dopaminergic neurons following FTO loss of function identified a subset of mRNAs whose m6A levels were influenced by FTO [64]. Many of these transcripts encode proteins involved in the response to dopamine, suggesting that FTO-mediated dynamic methylation of neuronal mRNAs is necessary for proper dopaminergic signaling. Loss of DNMT2-mediated m5C methylation increases tRNA stress-induced cleavage in flies and cleavage of tRNAs, and repression of protein translation is a conserved response to several stress stimuli in eukaryotes. Nuerodevelopmental disorders are commonly associated with oxidative stress, and increased tRNA cleavage has been recently directly linked to neurodevelopmental and neurodegenerative conditions [65]. Sex determination In Drosophila, fl(2)d and vir are required for sex-dependent regulation of alternative splicing of the sex determination factor sex lethal (Sxl). m6A is required for female-specific alternative splicing of Sxl, which determines female physiognomy, but also translationally represses male-specific lethal 2 (msl-2) to prevent dosage compensation in females [42, 66]. Virus infection Viral life cycles are usually regulated by precise mechanisms that act on their RNA [67]. The m6A was found on RNA of several viruses in 1970 s and hypothesized a new RNA regulatory control to viral infection [68]. Recently, a proviral role for m6A in HIV-1 infection has been found. The function of individual m6A sites in HIV-1 RNA can be varied from regulating HIV-1 RNA nuclear export to enhancing viral gene expression [43, 69, 70]. Using m6A-seq, m6A modifications were also mapped in several regions across the RNA of the Flaviviridae members hepatitis C virus (HCV), Zika virus (ZIKV), dengue virus, yellow fever virus and West Nile virus [71, 72]. In addition, the Kaposi’s sarcoma-associated herpesvirus (KSHV), mRNAs also undergo m6A modification. The blockage of m6A inhibited splicing of the pre-mRNA, a key KSHV lytic switch protein, replication transcription activator, and halted viral lytic replication [73]. The m6A on viral RNAs may prevent detection by host pattern recognition receptors that trigger antiviral innate immunity. It may serve as a shield on viral RNA to prevent induction of antiviral signaling pathways, which is important for the therapy of pathogen-associated diseases. RNA methylation and disease The study of RNA methylation has emerged as an exciting new research area over the past few years. It might represent an additional layer of gene regulation, leading to the coining of the terms, ‘RNA epigenetics’ and ‘epitranscriptomics’. The direct studies of the role of RNA methylation in disease have been rare; however, the methylases, demethylases and other related factors have been shown to have disease correlations. Here, we summarize our current knowledge about the genes directing these modifications in human disease (Table 2). Although RNA methylation research is still in its early stages, disruption of RNA methylation has been linked to a number of disease conditions. It suggests RNA methylation important pathogenesis and independent factor during the progression of diseases. Considering that the biological effects of RNA methylation in different diseases are vicarious, we also summarize the RNA methylation regulating genes in different diseases (Table 3). Here, we give examples for five important human diseases including obesity, neurodevelopmental disorders, cancer, dyskeratosis congenita and X-linked intellectual disability [82]. Table 2. Human disease associated with RNA methylation factors RNA methylation type  Factors  Human disease  m6A  WTAP (m6A writer)  Hypospadias [126]  Acute myelogennous leukemia [127]  Cholangiocarcinoma [128]  FTO (m6A eraser)  Obesity [75]  Coronary heart disease [129]  Type 2 diabetes [130]  Cancer [131]  ALKBH5 (m6A eraser)  Infertility [132]  Major depressive disorder [133]  m5C  NSUN2 (m5C RNA methyltransferase)  Breast cancer [134]  Autosomal recessive intellectual disability [135]  Amyotrophic lateral sclerosis [61]  Parkinson’s disease [78]  Ψ  DKC1 (RNAΨ Synthase)  Diskeratosis congenita [136]  Pituitary tumorigenesis [137]  Prostate cancer [138]  PUS1 (RNAΨ Synthase)  Mitochondrial myopathy, lactic acidosis and sideroblastic  Anemia [139]  A-to-I editing  ADAR1 (adenosine deaminase)  Chronic myeloid leukemia [140]  Metastatic melanoma [141]  Human hepatocellular carcinoma [142]  Esophageal squamous cell carcinoma [143]  ADAR2 (adenosine deaminase)  Glioblastoma multiforme [144]  Alzheimer’s disease [145]  RNA methylation type  Factors  Human disease  m6A  WTAP (m6A writer)  Hypospadias [126]  Acute myelogennous leukemia [127]  Cholangiocarcinoma [128]  FTO (m6A eraser)  Obesity [75]  Coronary heart disease [129]  Type 2 diabetes [130]  Cancer [131]  ALKBH5 (m6A eraser)  Infertility [132]  Major depressive disorder [133]  m5C  NSUN2 (m5C RNA methyltransferase)  Breast cancer [134]  Autosomal recessive intellectual disability [135]  Amyotrophic lateral sclerosis [61]  Parkinson’s disease [78]  Ψ  DKC1 (RNAΨ Synthase)  Diskeratosis congenita [136]  Pituitary tumorigenesis [137]  Prostate cancer [138]  PUS1 (RNAΨ Synthase)  Mitochondrial myopathy, lactic acidosis and sideroblastic  Anemia [139]  A-to-I editing  ADAR1 (adenosine deaminase)  Chronic myeloid leukemia [140]  Metastatic melanoma [141]  Human hepatocellular carcinoma [142]  Esophageal squamous cell carcinoma [143]  ADAR2 (adenosine deaminase)  Glioblastoma multiforme [144]  Alzheimer’s disease [145]  Table 2. Human disease associated with RNA methylation factors RNA methylation type  Factors  Human disease  m6A  WTAP (m6A writer)  Hypospadias [126]  Acute myelogennous leukemia [127]  Cholangiocarcinoma [128]  FTO (m6A eraser)  Obesity [75]  Coronary heart disease [129]  Type 2 diabetes [130]  Cancer [131]  ALKBH5 (m6A eraser)  Infertility [132]  Major depressive disorder [133]  m5C  NSUN2 (m5C RNA methyltransferase)  Breast cancer [134]  Autosomal recessive intellectual disability [135]  Amyotrophic lateral sclerosis [61]  Parkinson’s disease [78]  Ψ  DKC1 (RNAΨ Synthase)  Diskeratosis congenita [136]  Pituitary tumorigenesis [137]  Prostate cancer [138]  PUS1 (RNAΨ Synthase)  Mitochondrial myopathy, lactic acidosis and sideroblastic  Anemia [139]  A-to-I editing  ADAR1 (adenosine deaminase)  Chronic myeloid leukemia [140]  Metastatic melanoma [141]  Human hepatocellular carcinoma [142]  Esophageal squamous cell carcinoma [143]  ADAR2 (adenosine deaminase)  Glioblastoma multiforme [144]  Alzheimer’s disease [145]  RNA methylation type  Factors  Human disease  m6A  WTAP (m6A writer)  Hypospadias [126]  Acute myelogennous leukemia [127]  Cholangiocarcinoma [128]  FTO (m6A eraser)  Obesity [75]  Coronary heart disease [129]  Type 2 diabetes [130]  Cancer [131]  ALKBH5 (m6A eraser)  Infertility [132]  Major depressive disorder [133]  m5C  NSUN2 (m5C RNA methyltransferase)  Breast cancer [134]  Autosomal recessive intellectual disability [135]  Amyotrophic lateral sclerosis [61]  Parkinson’s disease [78]  Ψ  DKC1 (RNAΨ Synthase)  Diskeratosis congenita [136]  Pituitary tumorigenesis [137]  Prostate cancer [138]  PUS1 (RNAΨ Synthase)  Mitochondrial myopathy, lactic acidosis and sideroblastic  Anemia [139]  A-to-I editing  ADAR1 (adenosine deaminase)  Chronic myeloid leukemia [140]  Metastatic melanoma [141]  Human hepatocellular carcinoma [142]  Esophageal squamous cell carcinoma [143]  ADAR2 (adenosine deaminase)  Glioblastoma multiforme [144]  Alzheimer’s disease [145]  Table 3. The role of RNA methylation in different genes with heterogeneity of disease RNA methylation type  Related genes  Relative level in disease  Role in disease  Human disease  m6A  RUNX1T1  Hypo  Disease progression  Obesity [74]  miR-125b  Hyper  Tumor progression  Cancer [75]  IDH1/2  Hyper  Tumor progression  Acute myeloid leukemia [76]  m5C  tRNA  Hypo  Disease progression  Amyotrophic lateral sclerosis [64]  Parkinson’s disease [77]  Ψ  TERC  Hypo  Disease diagnosis  Diskeratosis congenita [25]  2’-O-methylation  FTSJ1  Hypo  Disease progression  X-linked intellectual disability [78]  A-to-I editing  PU.1  Hyper  Tumor progression  Chronic myeloid leukemia [79]  AZIN1  Hyper  Tumor progression  Human hepatocellular carcinoma [80]  GluA2  Hypo  Disease diagnosis  Alzheimer’s disease [81]  RNA methylation type  Related genes  Relative level in disease  Role in disease  Human disease  m6A  RUNX1T1  Hypo  Disease progression  Obesity [74]  miR-125b  Hyper  Tumor progression  Cancer [75]  IDH1/2  Hyper  Tumor progression  Acute myeloid leukemia [76]  m5C  tRNA  Hypo  Disease progression  Amyotrophic lateral sclerosis [64]  Parkinson’s disease [77]  Ψ  TERC  Hypo  Disease diagnosis  Diskeratosis congenita [25]  2’-O-methylation  FTSJ1  Hypo  Disease progression  X-linked intellectual disability [78]  A-to-I editing  PU.1  Hyper  Tumor progression  Chronic myeloid leukemia [79]  AZIN1  Hyper  Tumor progression  Human hepatocellular carcinoma [80]  GluA2  Hypo  Disease diagnosis  Alzheimer’s disease [81]  Table 3. The role of RNA methylation in different genes with heterogeneity of disease RNA methylation type  Related genes  Relative level in disease  Role in disease  Human disease  m6A  RUNX1T1  Hypo  Disease progression  Obesity [74]  miR-125b  Hyper  Tumor progression  Cancer [75]  IDH1/2  Hyper  Tumor progression  Acute myeloid leukemia [76]  m5C  tRNA  Hypo  Disease progression  Amyotrophic lateral sclerosis [64]  Parkinson’s disease [77]  Ψ  TERC  Hypo  Disease diagnosis  Diskeratosis congenita [25]  2’-O-methylation  FTSJ1  Hypo  Disease progression  X-linked intellectual disability [78]  A-to-I editing  PU.1  Hyper  Tumor progression  Chronic myeloid leukemia [79]  AZIN1  Hyper  Tumor progression  Human hepatocellular carcinoma [80]  GluA2  Hypo  Disease diagnosis  Alzheimer’s disease [81]  RNA methylation type  Related genes  Relative level in disease  Role in disease  Human disease  m6A  RUNX1T1  Hypo  Disease progression  Obesity [74]  miR-125b  Hyper  Tumor progression  Cancer [75]  IDH1/2  Hyper  Tumor progression  Acute myeloid leukemia [76]  m5C  tRNA  Hypo  Disease progression  Amyotrophic lateral sclerosis [64]  Parkinson’s disease [77]  Ψ  TERC  Hypo  Disease diagnosis  Diskeratosis congenita [25]  2’-O-methylation  FTSJ1  Hypo  Disease progression  X-linked intellectual disability [78]  A-to-I editing  PU.1  Hyper  Tumor progression  Chronic myeloid leukemia [79]  AZIN1  Hyper  Tumor progression  Human hepatocellular carcinoma [80]  GluA2  Hypo  Disease diagnosis  Alzheimer’s disease [81]  Obesity Genome-wide association studies linked common variants of FTO gene with childhood and adult obesity in 2007 [74, 83, 84]. The finding that FTO-mediated m6A demethylation controls exonic splicing of adipogenic regulatory factor RUNX1T1 emphasized the regulatory role of FTO in adipogenesis [74, 84]. Neurodevelopmental disorders Hereditary forms of intellectual disability are neurodevelopmental disorders [85]. Loss of cytosine-5 RNA methylation increases the angiogenin-mediated endonucleolytic cleavage of tRNA leading to an accumulation of 5′ tRNA-derived small RNA fragments. Accumulation of 5′ tRNA fragments in the absence of methyltransferase NSun2 reduces protein cell size and increased apoptosis of cortical, hippocampal and striatal neurons. Cytosine-5 methylation at the variable loop of tRNAs act as the main upstream regulator of angiogenin-dependent tRNA binding and cleavage. tRNAs lacking cytosine-5 methylation are prone to be cleaved by angiogenin, and altered tRNA cleavage because of mutation is also linked to neurodegenerative disease, such as amyotrophic lateral sclerosis and Parkinson’s [64, 77, 86]. Cancer Proteinase-activated receptor 2 (PAR2) participates in cancer metastasis promoted by serine proteinases. The PAR2 activation also represses miR-125 b expression, while miR-125 b mimic successfully blocks PAR2-induced cell migration. PAR2 activation increases the level of m6A-containing pre-miR-125 b in NSun2-dependent manner. NSun2-dependent RNA methylation contributes to the downregulation of miR-125 b to regulate cancer cell migration by altering miRNA expression [875]. Dyskeratosis congenita Dyskeratosis congenita can be caused by mutations in the non-coding RNA (ncRNA) telomerase component TERC. The reads distribution across TERC revealed a putative Ψ site at position 307. Ψ-seq of the hybrid-captured RNA confirmed substantial pseudouridylation of position 307, a highly conserved uridine in a region essential for telomerase activity and TERT binding, and showed that it is modified at significantly higher levels in the control sample than in the patient sample. This suggests that TERC pseudouridylation may be compromised in dyskeratosis congenita, and provides a general way to quantify Ψ in lowly expressed genes [25]. X-linked intellectual disability Mutations in human FTSJ1 can cause nonsyndromic X-linked intellectual disability (NSXLID). The tRNAPhe from two genetically independent cell lines of NSXLID patients with loss of function FTSJ1 mutations nearly completely lacks 2’-O-methylated C32 (Cm32) and 2’-O-methylated G34 (Gm34), and has reduced peroxywybutosine. These directly link defective 2’-O-methylation of the tRNA anticodon loop to FTSJ1 mutations, suggesting that the modification defects cause NSXLID, and may implicate Gm34 of tRNAPhe as the critical modification [78]. Databases RNAMDB The RNA Modification Database (RNAMDB) has served as a focal point for information pertaining to naturally occurring RNA modifications (http://rna-mdb.cas.albany.edu/RNAmods/) [6]. In its current state, the database uses an easy-to-use, searchable interface to obtain detailed data on the 109 currently known RNA modifications. Each entry provides the chemical structure, common name and symbol, elemental composition and mass, CA registry numbers and index name, phylogenetic source, type of RNA species in which it is found and references to the first reported structure determination and synthesis. MODOMICS MODOMICS is a database of RNA modifications that provides comprehensive information concerning the chemical structures of modified ribonucleotides, their biosynthetic pathways, RNA-modifying enzymes and location of modified residues in RNA sequences (http://modomics.genesilico.pl) [5]. It integrates information about the chemical structure of modified nucleotides, their localization in RNA sequences, pathways of their biosynthesis and enzymes that carry out the respective reactions. MODOMICS also provides literature information, and links to other databases, including the available protein sequence and structure data. RADAR RADAR includes a comprehensive collection of A-to-I RNA editing sites identified in humans (Homo sapiens), mice (Mus musculus) and flies (D.melanogaster), together with extensive manually curated annotations for each editing site (http://RNAedit.com) [87]. RADAR also includes an expandable listing of tissue-specific editing levels for each editing site, which will facilitate the assignment of biological functions to specific editing sites. MeT-DB The MethylTranscriptome DataBase (MeT-DB) is the first comprehensive resource for m6A in mammalian transcriptome (http://compgenomics.utsa.edu/methylation/) [88]. It includes a database that records publicly available data sets from methylated RNA immunoprecipitation sequencing (MeRIP-Seq), a recently developed technology for interrogating m6A methyltranscriptome. MeT-DB includes ∼300k m6A methylation sites in 74 MeRIP-Seq samples from 22 different experimental conditions predicted by exomePeak and MACS2 algorithms. To explore this rich information, MeT-DB also provides a genome browser to query and visualize context-specific m6A methylation under different conditions. MeT-DB also includes the binding site data of miRNA, splicing factor and RNA-binding proteins in the browser window for comparison with m6A sites and for exploring the potential functions of m6A. RMBase RMBase (RNA Modification Base) is developed to decode the genome-wide landscape of RNA modifications identified from high-throughput modification data generated by 18 independent studies (http://mirlab.sysu.edu.cn/rmbase/) [89]. The current release of RMBase includes ∼9500 Ψ modifications generated from Pseudo-seq and CeU-seq sequencing data, ∼1000 m5C predicted from Aza-IP data, ∼124 200 m6A modifications discovered from m6A-seq and ∼1210 2'-O-methylations identified from RiboMeth-seq data and public resources. Moreover, RMBase provides a comprehensive listing of other experimentally supported types of RNA modifications by integrating various resources. REDIportal REDIportal is the largest and comprehensive collection of RNA editing in humans including >4.5 million of A-to-I events detected in 55 body sites from thousands of RNA-seq experiments (http://srv00.recas.ba.infn.it/atlas/) [90]. REDIportal embeds RADAR database and represents the first editing resource designed to answer functional questions, enabling the inspection and browsing of editing levels in a variety of human samples, tissues and body sites. In contrast with previous RNA editing databases, REDIportal comprises its own browser (JBrowse) that allows users to explore A-to-I changes in their genomic context, empathizing repetitive elements in which RNA editing is prominent. Web server and software Sequence-based site prediction Web server or software HAMR HAMR is a high-throughput method to map RNA modifications within all classes of RNAs by identifying mis-incorporation of nucleotides by reverse transcriptase (RT) during production of complementary DNA (cDNA) products (http://wanglab.pcbi.upenn.edu/hamr) [91]. Users may submit a link to a remote indexed BAM (read alignment) file to the online version of HAMR. HAMR detects candidate modification sites either transcriptome-wide or at selected loci specified by transcript ID or genomic coordinates. Users may also opt to filter out known dbSNP sites for human data and select various options affecting the stringency of the analysis, including P-value or false discovery rate (FDR) thresholds, minimum coverage and which null hypothesis to use. M6Apred M6Apred is a support vector machine (SVM)-based model to identify m6A sites in the Saccharomyces cerevisiae transcriptome by using the nucleotide chemical property and nucleotide density information (http://lin.uestc.edu.cn/server/m6Apred.php) [92]. In this model, RNA sequences are encoded by their nucleotide chemical property and accumulated nucleotide frequency information. iRNA-Methyl iRNA-Methyl formulates RNA sequences with the ‘pseudo dinucleotide composition’ (PseDNC), which incorporates three RNA physiochemical properties (http://lin.uestc.edu.cn/server/iRNA-Methyl) [93]. It was observed by the rigorous cross-validation test on the benchmark data set that the accuracy achieved by the predictor in identifying m6A was 65.59%. All benchmark data can be downloaded from the Data window of this Web server. PPUS PPUS is the first Web server to predict pseudo uridine synthase (PUS)-specific Ψ sites (http://lyh.pkmu.cn/ppus/) [94]. PPUS used SVM as the classifier and used nucleotides around Ψ sites as the features. Currently, PPUS could accurately predict new Ψ sites for PUS1, PUS4 and PUS7 in yeast and PUS4 in human. AthMethPre AthMethPre is a method to predict the m6A sites for Arabidopsis thaliana mRNA sequence(s) (http://bioinfo.tsinghua.edu.cn/AthMethPre/index.html) [95]. To predict the m6A sites of an mRNA sequence, the SVM was used to build a classifier using the features of the positional flanking nucleotide sequence and position-independent k-mer nucleotide spectrum. The server also provides a comprehensive database of predicted transcriptome-wide m6A sites and curated m6A-seq peaks from literatures for query and visualization. RNAMethPre RNAMethPre integrated multiple features of mRNA (flanking sequences, local secondary structure information and relative position information) and trained a SVM classifier to predict m6A sites in mammalian mRNA sequences (http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html) [96]. Given an mRNA as well as its corresponding species information, the server returns all predicted m6A sites to users. The results are also downloadable for further analysis. The SVM model was also applied to predict transcriptome-wide m6A sites. Experimental m6A-seq peaks were collected from literatures. The Web server was built to provide both prediction and query services for m6A sites. A genome browser was also built based on JBrowse to visualize the query results. M6ATH M6ATH is an SVM-based method proposed to identify m6A sites in A. thaliana transcriptome (http://lin.uestc.edu.cn/server/M6ATH) [97]. The proposed method was validated on a benchmark data set using jackknife test and was also validated by identifying strain-specific m6A sites in A. thaliana. For the convenience of scientific community, a freely accessible online Web server was established. PRNAm-PC In pRNAm-PC, RNA sequence samples are expressed by a novel mode of PseDNC whose components were derived from a physical–chemical matrix via a series of auto-covariance and cross-covariance transformations (http://www.jci-bioinfo.cn/pRNAm-PC) [98]. SRAMP To depict the sequence context around m6A sites, SRAMP combines three Random Forest classifiers that exploit the positional nucleotide sequence pattern, the k-nearest neighbor (kNN) information and the position-independent nucleotide pair spectrum features, respectively (http://www.cuilab.cn/sramp/) [99]. SRAMP accepts either genomic sequences or cDNA sequences as its input. It only requires nucleotide sequences for prediction. Users can select either the full transcript mode or the mature mRNA mode, depending on whether they have the genomic or the cDNA sequence at hand, and whether they are interested in the intronic m6A sites. Users can also decide whether the RNA secondary structure should be considered. Analysis of RNA secondary structures provides text and graphical representation of the local structure around the predicted m6A site. RAMPred RAMPred is proposed to identify m1A sites in H.sapiens, M.musculus as well as S.cerevisiae genomes for the first time (http://lin.uestc.edu.cn/server/RAMPred) [100]. In this method, RNA sequences are encoded by using nucleotide chemical property and nucleotide compositions. iRNA-PseU The Web server iRNA-PseU was developed to identify the Ψ sites in H.sapiens, M.musculus and S.cerevisiae (http://lin.uestc.edu.cn/server/iRNA-PseU) [101]. It incorporated the chemical properties of nucleotides and their occurrence frequency density distributions into the general form of pseudo K-tuple nucleotide composition (PseKNC). MethyRNA MethyRNA is an SVM-based model to identify m6A sites by encoding RNA sequence using nucleotide chemical property and frequency based on the high-resolution experimental data of H.sapiens and M.musculus (http://lin.uestc.edu.cn/server/methyrna) [102]. It was observed by the rigorous cross-validation test with accuracy of 90.38 and 88.89% for identifying m6A in former mentioned species, respectively. RAM-ESVM RAM-ESVM was developed for detecting m6A sites from S.cerevisiae transcriptome, which used ensemble SVM classifiers and novel sequence features (http://server.malab.cn/RAM-ESVM/) [103]. RAM-ESVM combined three basic classifiers, namely, SVM-PseKNC, SVM-motif and GkmSVM, which were constructed by using PseKNC, motif features and optimized k-mer as discriminal features, respectively. RAM-NPPS RAM-NPPS is a sequence-based predictor for identifying m6A sites within RNA sequences (http://server.malab.cn/RAM-NPPS/) [104]. Users can submit uncharacterized RNA sequences to identify the potential m6A sites. In particular, the online predictor provides m6A site identification specific for three species, such as S.cerevisiae, H.sapiens and A.thaliana. iRNA-AI iRNA-AI is a predictor to identify A-to-I editing sites based on the RNA sequence information alone (http://lin.uestc.edu.cn/server/iRNA-AI/) [105]. It has been proposed by incorporating the chemical properties of nucleotides and their sliding occurrence density distribution along a RNA sequence into the general form of pseudo nucleotide composition (PseKNC). iRNA-PseColl The iRNA-PseColl was formed by incorporating both the individual and collective features of the sequence elements into the general PseKNC of RNA via the chemicophysical properties and density distribution of its constituent nucleotides (http://lin.uestc.edu.cn/server/iRNA-PseColl) [106]. It was developed to identify RNA modifications in H.sapiens transcriptome. At present, the m1A, m6A and m5C can be identified based on the current platform. Next-generation sequencing (NGS) data-based site detection Web server or software MeRIP-PF MeRIP-PF is a novel high-efficiency and user-friendly analysis pipeline for the signal identification of MeRIP-Seq data in reference to controls (http://software.big.ac.cn/MeRIP-PF.html) [107]. MeRIP-PF provides a statistical P-value for each identified m6A region based on the difference of read distribution when compared with the controls and also calculates FDR as a cutoff to differentiate reliable m6A regions from the background. Furthermore, MeRIP-PF also achieves gene annotation of m6A signals or peaks and produces outputs in both XLS and graphical format, which are useful for further study. exomePeak R/Bioconductor package The ‘exomePeak’ is an open-source R package for detecting RNA methylation sites under a specific experimental condition or identifying the differential RNA methylation sites in a case-control study from MeRIP-Seq data (http://www.bioconductor.org/packages/release/bioc/html/exomePeak.html) [108]. Using exomePeak R/Bioconductor package along with other software programs for analysis of MeRIP-Seq data, it can conduct raw reads alignment, RNA methylation site detection, motif discovery, differential RNA methylation analysis and functional analysis. meRanTK The meRanTK is the first publicly available tool kit, which addresses the special demands of high-throughput RNA cytosine methylation data analysis (http://icbi.at/software/meRanTK/) [109]. It provides fast and easy-to-use splice-aware bisulfite sequencing read mapping, comprehensive methylation calling and identification of differentially methylated cytosines by statistical analysis of single- and multi-replicate experiments. Application of meRanTK to RNA-BSseq or Aza-IP data produces accurate results in standard compliant formats.meRanTK includes five multithreaded programs, which enable complete analysis and comparison of m5C transcriptome data sets. The tools, meRanT and meRanG, use well-established RNAseq-specific short-read mappers as core aligning engines and extend them to facilitate mapping of either single- or paired-end sequence reads from strand-specific RNA-BSseq libraries to a given reference sequence. The meRanCall methylation caller uses aligned reads to precisely identify and statistically evaluate the positions of methylated cytosines. The experimental comparison tool meRanCompare is designed to detect differentially methylated m5Cs of two experimental conditions with single- or multi-replicate RNA methylation data sets. The annotation tool meRanAnnotate helps to annotate candidate m5Cs with genomic features such as gene or transcript names and positional metrics. DRME DRME is designed for differential RNA methylation analysis from the MeRIP-Seq data set at small sample size scenario using the negative binomial model (https://github.com/lzcyzm/DRME) [110]. The model not only captures within-group biological variability among replicates but also addresses the changes in RNA expression level and its impact on RNA methylation, and thus can be applied to MeRIP-Seq, particularly for differential RNA methylation analysis. The algorithm is also fast to execute and in theory can be applicable to other data types related to RNA such as RNA bisulfite sequencing and photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) without reads count rescaling or normalization. MeTPeak MeTPeak is a novel, graphical model-based peak-calling method for transcriptome-wide detection of m6A sites from MeRIP-seq data (https://github.com/compgenomics/MeTPeak) [111]. MeTPeak explicitly models read count of an m6A site and introduces a hierarchical layer of beta variables to capture the variances and a hidden Markov model (HMM) to characterize the reads dependency across a site. In addition, a constrained Newton’s method and a log-barrier function are developed to estimate analytically intractable, positively constrained beta parameters. MeTPeak deploys a hierarchical beta-binomial model to depict the variance of reads enrichment and an HMM to account for the dependency of neighboring enrichment. MeTPeak is an open-source R package, where core heavy computation part of the algorithm is written in C  ++. txCoords txCoords is a novel and easy-to-use Web application for transcriptomic peak remapping (http://www.bioinfo.tsinghua.edu.cn/txCoords) [112]. txCoords can be used to correct the incorrectly reported transcriptomic peaks and retrieve the true sequences. It also supports visualization of the remapped peaks in a schematic figure or from the UCSC Genome Browser. Annotation and visualization of RNA modification CAn Hauenschild et al. [113] developed a RNA modification visualization tool called CoverageAnalyzer (CAn), which allows the visualization and assisted inspection of RNA-seq profiles for RT signatures of modifications intuitively (https://sourceforge.net/projects/coverageanalyzer/). CAn takes SAM input data files from N user-specified samples as input file. Build in pipeline will create Pileup format and further convert to Profile files, which provide pair-wise information including position; reference base; coverage; mismatch rate M; number of (#) As, #Gs, #Ts and #Cs; and arrest rate A. Afterward, statistics are gathered for reference sequences including ID, file path, length, sequence, coverage peak, number of high-arrest sites, high mismatch sites, heterogeneous mismatch sites and mapped reads. Based on this information, users can manually sort or set threshold to filtering and visualize RNA modification RT signature on graphical user interface (GUI) software. CAn is highly conductive to the extraction of complete RT signatures, by providing full control of all thresholds for visualization, identification and discrimination to the user. MetaPlotR MetaPlotR is a Perl and R pipeline, to easily generate metagenes for any organism for which a genome and transcript annotation is available through the UCSC Genome Browser database (https://github.com/olarerin/metaPlotR) [114]. RCAS The RNA Centric Annotation System (RCAS) is an R package to ease the process of creating gene-centric annotations and analysis for the genomic regions of interest obtained from various RNA-based omics technologies (http://bioconductor.org/packages/release/bioc/html/RCAS) [115]. The RCAS R package uses different R functions to perform annotation summarization, GO term and gene set enrichment analysis and de novo sequence motif discovery. The Web interface allows users to upload a single BED file, which is used as the main input to RCAS and to select analysis module. Users can select one of four reference genome assemblies and select one annotation database for the gene set enrichment analysis module. The intervals in the BED file can optionally be down-sampled. On submission, the job is enqueued to run RCAS in the background and generate the specified HTML report. Once RCAS has generated the report, the requester can access it online or download it in a bundle along with any produced output files. Computational models RNA methylation has been found for decades of years, which occur at different RNA types of numerous species. As more and more research evidences have indicated that RNA methylation plays an important role in RNA splicing, posttranscriptional gene expression regulation, extensive translation of circRNA, neuronal functions and many different stages of RNA life cycle [116, 117]. This reversible RNA methylation adds a new dimension to the developing picture of posttranscriptional regulation of gene expression [118]. However, the experimental technologies are cost-ineffective for RNA methylation site prediction, RNA methylation function analysis directly. As complements to experimental techniques, computational models could facilitate the analysis based on RNA sequences or RNA-seq data. Here, we conclude the well-established methods for detecting potential RNA methylation sites. RNA methylation sites could be predicted based on powerful computational models in the following two ways. We could construct sequence-based models to predict potential RNA methylation sites based on training samples (known methylation sites versus nonmethylation sites) and unlabeled samples (genomic sequences or cDNA sequences). We can also predict the RNA methylation sites based on sequencing data, such as RNA-seq, MeRIP-sequencing, BS-sequencing, etc. Then, we can make further analysis based on the predicted sites, such as differential analysis, annotation, visualization, as well as functional analysis, etc. Sequence-based site prediction models HAMR During RT, modifications may lead to RT signatures, including RT arrestor mis-incorporation. RT signatures can manifest in the cDNA as either abortive or modification, respectively, which can be captured by RNA-seq. Ryvkin et al. [91] developed a statistical method to identify RNA modification sites based on nucleotide mis-incorporation by RT. The method detects modification by two hypotheses. The simplest null hypothesis assumes the site is homozygous with the reference allele. Taking this as the null hypothesis results in any nonreference nucleotide above the base-calling error rate being called as a candidate modification. A more conservative null hypothesis assumes only that the genotype is biallelic. Taking this as the null hypothesis results in site with three or more nucleotides that are sequenced at a rate higher than base-call errors being called as a candidate modification site. Besides, authors developed a kNN-based classifier to predict the modification type. Using small RNA-seq data, HAMR was able to detect 92% of all known human tRNA modification sites that are predicted to affect RT activity, and authors can distinguish two classes of A and two classes of G modifications with 98 and 79% accuracy, respectively. However, HAMR cannot distinguish single-nucleotide polymorphism (SNP) and RNA modification. Besides, HAMR is mainly built based on small RNA-seq and tRNA modification data. Therefore, the performance of HAMR in RNA modification detection for other data need to be further validated. PPUS Ψ is known to be catalyzed by PUS, and is found to present in different categories of noncoding RNAs such as tRNAs, rRNAs and snRNA. Li et al. [94] proposed a new platform called PPUS to identify PUS-specific Ψ sites. A sliding window strategy is used to get nucleotides around the Ψsites as classification features. Then, SVM classifier is followed to make the prediction of Ψ sites. However, PPUS can only identify Ψ sites in human and S.cerevisiae. iRNAMethyl Chen et al. [93] proposed PseDNC to incorporate both the local and global sequence pattern information of the queried RNA sequence, which is defined as:   D=[d1d2⋯d16d16+1⋯d16+λ]T (1) with   du={fu∑i=116fi+w∑j=1λθj1≤u≤16wθu−16∑i=116fi+w∑j=1λθj16<u≤16+λ (2) where fu(u=1,2,…,16) is the normalized occurrence frequency of the u-th nonoverlapping dinucleotides. λ is the number of the total counted ranks of the correlations along a RNA sequence, while w is the weight factor. The correlation factor θj represents the j-tier structural correlation factor between all the most contiguous dinucleotides:   θj=1L−j−1∑i=1L−j−1Ci,i+j   (j=1,2,…,λ;λ<L), (3) With Θi,i+j being the coupling factor given by:   Θi,i+j=1v∑u=1v[Pu(Di)−Pu(Di+j)]2, (4) where v is the number of RNA physicochemical properties considered. Finally, the feature vectors are fed into SVM for site prediction. Through jackknife test, iRNA-Methyl shows better prediction performance than traditional BLAST approach. m6Apred Chen et al. [92] developed a SVM-based computational model of m6Apred to identify m6A site in the S.cerevisiae transcriptome. To the best of our knowledge, m6Apred is the first sequence-based m6A site prediction model. m6Apred developed a sequence encoding method to depict the nucleotide chemical properties as well as the density information of each nucleotide in RNA sequences. The four different kinds of nucleotides found in RNA, adenine (A), guanine (G), cytosine (C) and uracil (U), are classified into three different groups in terms of chemical properties, such as chemical structure, chemical binding and chemical functionality, as shown in Table 4. Thus, each nucleotide Ni is defined by three coordinates (xi,yi,zi) with:   xi={1ifNi∈{A,G}0ifNi∈{C,U},yi={1ifNi∈{A,C}0ifNi∈{G,U},zi={1ifNi∈{A,U}0ifNi∈{C,G}. (5) Table 4. Chemical property of nucleotide in RNA sequence Chemical property  Class  Nucleotides  Ring structure  Purine  A, G  Pyrimidine  C, U  Functional group  Amino  A, C  Keto  G, U  Hydrogen bond  Strong  C, G  Weak  A, U  Chemical property  Class  Nucleotides  Ring structure  Purine  A, G  Pyrimidine  C, U  Functional group  Amino  A, C  Keto  G, U  Hydrogen bond  Strong  C, G  Weak  A, U  Table 4. Chemical property of nucleotide in RNA sequence Chemical property  Class  Nucleotides  Ring structure  Purine  A, G  Pyrimidine  C, U  Functional group  Amino  A, C  Keto  G, U  Hydrogen bond  Strong  C, G  Weak  A, U  Chemical property  Class  Nucleotides  Ring structure  Purine  A, G  Pyrimidine  C, U  Functional group  Amino  A, C  Keto  G, U  Hydrogen bond  Strong  C, G  Weak  A, U  Then, the density di of any nucleotide si at position i in RNA sequence is also included by:   di=1|si|∑j=1lf(sj),f(q)={1if sj=q0other cases. (6) Finally, the encoded nucleotide chemical property and nucleotide densities are fed into SVM for prediction. m6Apred obtains an area under the curve (AUC) of 0.84 in the jackknife test, showing the considerable accuracy in predicting m6A sites in yeast. More importantly, m6Apred is not sensitive to the selection of negative data, which are really difficult to obtain in practical problems. However, whether m6Apred can be used to predict mammalian m6A sites has not been tested. RAM-ESVM Chen et al. [103] developed an ensemble classifier, called RAM-ESVM, for detecting m6A sites in the S.cerevisiae genome. RAM-ESVM used PseDNC together with SVM (SVM-PseKNC), motif features together with SVM (SVM-motif) and GkmSVM as basic classifiers. PreDNC represents the RNA sequences, which is defined as:   D=[d1d2⋯d16d16+1⋯d16+λ]T (7) with   du={fk∑i=116fi+w∑j=1λθj1≤u≤16wθu−16∑i=116fi+w∑j=1λθj16<u≤16+λ, (8) where fk(k=1,2,…,16) is the normalized occurrence frequency of the nonoverlapping dinucleotides. λ is the number of the total counted ranks of the correlations along a RNA sequence, while w is the weight factor. The correlation factor θj represents the j-tier structural correlation factor between all the j-th most contiguous dinucleotides Di=RiRi+1,   θj=1L−j−k+1∑i=1L−j−k+1Θ(Di,Di+1)   (j=1,2,…,λ;λ<L), (9) with Θ() being the correlation function. In term of motif feature, each sequence is represented as a boolean vector. If the substring selected as motif feature appears in one sequence, the feature value is 1. Otherwise, the value is 0. GkmSVM is used in the next for gapped k-mer-based classification. Classification results of all the three classifiers vote for final prediction score, as shown in Figure 1. Figure 1. View largeDownload slide The flowchart of RAM-ESVM which have described the basic steps to predict m6A methylation site from S. cerevisiae transcriptome. It uses ensemble SVM classifiers as well as some novel sequence features. Figure 1. View largeDownload slide The flowchart of RAM-ESVM which have described the basic steps to predict m6A methylation site from S. cerevisiae transcriptome. It uses ensemble SVM classifiers as well as some novel sequence features. RNAMethPre Considering different input sequences, i.e. genomic sequences and cDNA sequences, Xiang et al. [96] developed an SVM-based model to predict m6A sites in human, mouse and mammal. The frame structure is shown in Figure 2. Features, such as nucleotide sequence position, nucleotide k-mer frequency, a relative position value calculated from the absolute distance from the transcript start site as well as the stability of the local structure, are combined and added to SVM classifier for predicting the m6A sites. Just like SRAMP, RNAMethPre provides the full transcript mode and mature mRNA mode. For performance enhancement, RNAMethPre integrates all the abovementioned four features for mature mRNA mode, but only include the former two features in the full transcript mode. The validation results show that the performance of RNAMethyPre is superior to that of SRAMP. Figure 2. View largeDownload slide The flowchart shows the basic idea of RNAMethPre, which is used to predict m6A sites in human, mouse and mammal. RNAMethPre uses multiple features of mRNA, such as flanking sequences, local secondary structure information and relative position information, and feeds them into a SVM classifier for m6A site prediction. Figure 2. View largeDownload slide The flowchart shows the basic idea of RNAMethPre, which is used to predict m6A sites in human, mouse and mammal. RNAMethPre uses multiple features of mRNA, such as flanking sequences, local secondary structure information and relative position information, and feeds them into a SVM classifier for m6A site prediction. SRAMP Zhou et al. [99] established a mammalian m6A sites predictor named SRAMP (sequence-based RNA adenosine methylation site predictor) under the Random Forest framework, which is shown in Figure 3. SRAMP considers the positional binary encoding of nucleotide sequence, the kNN encoding and the nucleotide pair spectrum encoding. In the positional binary encoding, four different kinds of nucleotides found in RNA, adenine (A), guanine (G), cytosine (C) and uracil (U), are translated as binary vectors of (1, 0, 0, 0), (0, 1, 0, 0), (0, 0, 1, 0) and (0, 0, 0, 1). Then, kNN encoding depicts how much the 21 nt flanking window of one query sample resembles those of other m6A sites. The flanking window of the query sample was first compared with all reference samples to obtain pair-wise similarity scores:   Pair-wise similarity=∑i=1WNUC44(qi,ri), (10) where qi and ri are the nucleotides at the ith position of the query sample and the reference sample‘s flanking windows, respectively. In term of third type of feature, the frequency of a spaced nucleotide pair npi is defined as:   Frequency(npi)=C(npi)W−d−1, (11) Figure 3. View largeDownload slide The flowchart shows the basic steps of SRAMP. SRAMP combines three Random Forest classifiers that exploit the positional nucleotide sequence pattern, the kNN information and the position-independent nucleotide pair spectrum features, respectively. Figure 3. View largeDownload slide The flowchart shows the basic steps of SRAMP. SRAMP combines three Random Forest classifiers that exploit the positional nucleotide sequence pattern, the kNN information and the position-independent nucleotide pair spectrum features, respectively. Where C(npi) is the count of npi inside a flanking window, W is the window size and d is the space between two nucleotides. SRAMP also considers secondary structure predicted by RNAfold as the classification feature. The secondary structures are classified into hairpin loop, multiple loop, interior loop, paired and bulged loop, which are encoded as binary vectors, respectively. Random Forest classifiers are then trained with each feature. Finally, the prediction scores of the Random Forest classifiers trained with different feature encodings were combined using the weighted summing formula shown below:   Scombined=∑i=1nαiSi. (12) Where the Si and αi are the prediction score and the weight for the classifier trained with the i-th encoding, respectively. n is the total number of classifiers taken into account. The overall AUROC for the full transcript mode from 5-fold cross validation (CV) is 0.891, showing that it achieves good performance in full transcript mode. However, the prediction performance in mature mRNA mode can be further improved. RNAMethylPred Jia et al. [119] proposed a new bioinformatics model, named RNAMethylPred for the large-scale, rapid identification of m6A site. It was developed by incorporating Bi-profile Bayes (BPB), dinucleotide composition (DNC) and kNN scores as selected features, deploying SVM as classifier to perform the predictions, shown in Figure 4. First, with BPB, the queried sequence s is encoded into a probability vector V=(p1,p2,…,pn,pn+1,…,p2n), where pi(i=1,2,…,n) denotes the posterior probability of each nucleic acid at i-th position in the positive samples, and pi(i=n+1,n+2,…,2n) denotes the posterior probability of each nucleic acid at the i-th position in the negative samples, with n being the length of queried sequences. Then, DNC were defined as:   Pab=NabNa•P′ab=Nabn−1, (13) Figure 4. View largeDownload slide The flowchart of RNAMethylPred, which have described the basic steps to identify m6A site. RNAMethylPred adopts BPB, dinucleotides composition and kNN scores for feature extractions, and then follows SVM for classification. Figure 4. View largeDownload slide The flowchart of RNAMethylPred, which have described the basic steps to identify m6A site. RNAMethylPred adopts BPB, dinucleotides composition and kNN scores for feature extractions, and then follows SVM for classification. Where ab stands for the adjoining dinucleotides, Nab stands for the number of the adjoining dinucleotides in an RNA segment sample, a• stands for the adjoining dinucleotides, • stands for any nucleotide and n is the length of RNA sample. Thus, DNC encoding can contain features from both Pab and Pab′. In the next, the kNNs of the queried sequence in both positive and negative sets are picked out according to RNA local sequence similarity to get the kNN score, which can be formulated by following:   S(A,B)=∑1≤i≤nScore(A[i],B[i]), (14) Where A[i] and B[i] represent for the nucleotide at position i in both RNA sequence fragments. The similarity score for two nucleotides a and b is defined as:   Score={+2,if a=b−1,others. (15) The kNN score is achieved by calculating the percentage of the positive neighbors in its kNNs. As a result, RNAMethylPred achieves an accuracy of 76.51% and an MCC of 0.5302, showing the considerable performance in predicting m6A sites. iRNA-PseU Chen et al. [101] developed a new predictor called iRNA-PseU to identify Ψ sites. iRNA-PseU follows iRNA-PseColl to encode the nucleotide chemical property and nucleotide density, which are deployed as classification features in the following SVM classifier to make the prediction of Ψ sites. iRNA-PseU shows to achieve considerable accuracy in jackknife test on the benchmark data sets of human, mice and S.cerevisiae. RAMPred m1A has been found to have major influences on the structure and function of tRNA and rRNA. Chen et al. [100] further developed a new platform called RAMPred to identify the occurrence sites of m1A modifications across species of human, mice and S.cerevisiae transcriptome. RAMPred uses almost the same structure of m6Apred, and the validation results show that RAMPred can get satisfactory performance in predicting m1A modifications. iRNA-PseColl Feng et al. [106] developed a new platform called iRNA-PseColl to identify the occurrence sites for several different types of RNA modifications. iRNA-PseColl is the first method designed for multiple RNA modifications. iRNA-PseColl incorporates both individual and collective features of the sequence elements as features, as shown in Figure 5. Local features of the ith nucleotides Ni=(xi,yi,zi) are the same as that defined in m6Apred method. The occurrence frequency of a nucleotide for its distribution along the sequence is defined as:   Di=1||Li||∑j=1ℓf(Nj), (16) Figure 5. View largeDownload slide The flowchart of iRNA-PseColl. It aims at identification of occurrence sites for multiple RNA modification. It incorporates both local and collective features for classification, and SVM is adopted as the final classifier. Figure 5. View largeDownload slide The flowchart of iRNA-PseColl. It aims at identification of occurrence sites for multiple RNA modification. It incorporates both local and collective features for classification, and SVM is adopted as the final classifier. Where Di is the density of the nucleotide Ni at the site i of a RNA sequence, ||Li|| the length of the sliding substring concerned, ℓ denotes each of the site locations counted in the substring and   f(Nj)={1,if Nj=the nucleotide concerned0,otherwise. (17) Then, we combine the local feature and collective feature for the i-th nucleotide, which is defined by a set of four variables:   Ni=(xi,yi,zi,Di). (18) Finally, the prediction is achieved by SVM based on the features defined in (20). Validation results show that iRNA-PseColl can get considerable performance in predicting different types of RNA modifications. RAM-NPPS Xing et al. [104] proposed a sequence-based predictor called RAM-NPPS for identifying m6A sites within RNA sequences of different species. RAM-NPPS first encodes the input sequences by nucleotide pair position specificity (NPPS) algorithm. Then, the resulting feature vectors are joined together as the input for the following SVM classifier to make prediction. Figure 6 shows the NPPS feature encoding process. For the queried RNA sequence, it can be encoded by:   P=P+−P−, (19) Figure 6. View largeDownload slide The workflow of RAM-NPPS. It is based on multi-interval NPPS for feature extraction, and SVM classifier for prediction of m6A sites within RNA sequences. Figure 6. View largeDownload slide The workflow of RAM-NPPS. It is based on multi-interval NPPS for feature extraction, and SVM classifier for prediction of m6A sites within RNA sequences. Where P+ is formulated as:   P+=p1+p2+⋯pk+⋯pl−1+pl+, (20) with pk represents the k-th nucleotide, and l is the length of the sequence. Two matrices Ts+ and Td+ are defined to calculate pk+. Ts+ has size of 4×l representing the single-nucleotide occurrence probability. Rows represent {A,C,G,U}, respectively, and columns represent the length of the sequence. Td+ has size of 16×l representing the occurrence probability of nucleotide pair, with rows representing {A,C,G,U}×{A,C,G,U}. Suppose the dinucleotide between the k-th nucleotide and ( k+ξ)-th nucleotide is ‘AB’, then pk+=P(A∩B)P(B)=Fab,k+fb,k+ξ+, where ab is the index of ‘AB’ in {A,C,G,U}×{A,C,G,U}, and b is the index of ‘B’ in the {A,C,G,U}. The evaluation on three data sets shows that RAM-UPPS is effective and robust for the identification of m6A sites cross-different species. However, running of RAM-NPPS results in really heavy computation load. NGS data-based site detection models exomePeak Meng et.al. 120] proposed a pipeline for the analysis of MeRIP-seq data by combining several existing tools with a novel exome-based peak-calling and differential analysis approach, shown in Figure 7. exomePeak first extracts and connects all the exons of a specific gene and then detects peaks using a sliding window with C-test to determine the methylation site. Thus, it can be considered as projecting transcriptome onto the genome, and thus avoid the transcriptome heterogeneity. The test results show that it can achieve fairly robust m6A peak detection. However, it has two major limitations. First, exomePeak does not model the reads variance within transcripts and across replicates. Second, exomePeak ignores the dependency of reads enrichment, and thus may miss the true peaks with low enrichment. Finally, exomePeak tries to call peaks by bin-based method, which makes it difficult to get close to base solution. Figure 7. View largeDownload slide The basic steps of exomePeak, which provides a general pipeline for MeRIP-Seq data processing, predict m6A sites for samples from single condition, or identify differentially methylated sites for samples from multiple conditions. Figure 7. View largeDownload slide The basic steps of exomePeak, which provides a general pipeline for MeRIP-Seq data processing, predict m6A sites for samples from single condition, or identify differentially methylated sites for samples from multiple conditions. MeTPeak Cui et al. [111] further developed a graphic model-based peak-calling algorithm, MeTPeak, to detect m6A sites from MeRIP-seq data. It detects m6A peaks on each gene separately by dividing the particular gene into N bins with length of sequencing fragment L. The mixture of beta-binomial distribution is set up to describe the reads count in each bin, while an HMM is adopted to depict the reads dependency between the continuous bins. To be more specific, the reads count in the m-th pair of IP and input samples in bin n are denoted as Xmn and Ymn, which both follow Poisson distribution with parameters SIP,m, Sinput,m, λIP,m and λinput,m. SIP,m and Sinput,mare total reads in the m-th IP and input samples, respectively, and λIP,m and λinput,mare the normalized Poisson rates. With a priori of beta distribution for pn, which represents the methylation percentage at n-th bin, Xmn follows the beta-binomial distribution:   P(Xmn|Zn;α,β)=∏k=12(CΓ(Xmn+αk)Γ(Ymn+βk)Γ(αk+βk)Γ(Tmn+αk+βk)Γ(αk)Γ(βk)), (21) where Zn∈[1,2] denotes the unknown hidden methylation status with 1 representing methylated and 2 otherwise. Tmn=Xmn+Ymn. C=Γ(Tmn+1)Γ(Xmn+1)Γ(Ymn+1) is the normalization constant. α=[α1α2]T, β=[β1β2]T are the unknown parameters in the model, and they are shared for all bins across replications, and thus can somehow depict the variance of reads count across replicates. Then, an iterative Expectation Maximization (EM) algorithm is conducted to predict methylation sites Zn and model parameters. As a result, MeTPeak is shown to be more robust against data variance, small replicates and data outlier, and is more sensitive to lowly enriched peaks than exomePeak. However, limitations still existed in MeTPeak. For example, the search of m6A site is only limited to annotated genes. MeTPeak is also based on bin method, which makes it hard to get base solution. meRanTK Rieder etal. [109] developed a tool kit for the analysis of RNA-BSseq or Aza-IP data to detect m5C methylation site. To our knowledge, it is the first specialized software for RNA-BSseq data analysis. meRanTK includes five multithreaded programs, such as meRanT, meRanG, meRanCall, meRanCompare and meRanAnnotate, as shown in Figure 8. Among these tools, both meRanT and meRanG are RNA-BSseq alignment tools. The difference between these two alignment tools is that the former one maps reads to a preassembled set of transcripts, while the later one maps to a bisulfite-converted genome. meRanCall then extracts the methylation state of individual cytosine from the previous alignment. Based on the detected m5C sites, meRanCompare can help to identify whether the site is differentially methylation in two experimental conditions, while meRanAnnotate can assign genomic annotations and distance measurements to each individual candidate m5Cs. Figure 8. View largeDownload slide The workflow of meRanTK, which is specialized for RNA-BSseq data mapping, alignment, m5C site detection and annotation. Figure 8. View largeDownload slide The workflow of meRanTK, which is specialized for RNA-BSseq data mapping, alignment, m5C site detection and annotation. m6aViewer Antanaviciute etal. [121] developed a cross-platform, m6AViewer, for the detection, analysis and visualization of m6A peaks from MeRIP-seq data. The workflow is shown in Figure 9, where sorted and index BAM files are fed into m6aViewer as inputs. Then, sequencing adapter contamination and poor-quality bases are removed by Cutadapt software, followed by sequence alignment to reference genomes. The resulting alignments are sorted and indexed using SAMtools to obtain BAM files subsequent peak-calling module. For the preprocess step, the fragment coverage distribution is counted to compensate for the bias of reads coverage distribution; thus, it can achieve precise determination of sequenced RNA fragment coverage. Then, base-level candidate peak position is identified with local maxima as well as Fisher’s exact test. High-confidence peaks are called by combination of P-value and FDR-based cutoffs, as well as IP enrichment and c overage filters. In terms of the situation that peaks arising from multiple m6A sites in close proximity, which are visually indistinguishable cannot be identified accurately, m6aViewer proposed a mixture distribution-based approach to deconvolute overlapping peaks and pinpoint m6A methylation sites with increased precision. It considers the fragment coverage distribution in an enriched region as a mixture of coverage distributions. EM algorithm is adopted to establish the combination of mixtures best depict the observed RNA fragment distribution. To avoid overfitting, Bayesian information criterion is used to account for both the likelihood and the model complexity. M6aViewer combined the above sequence-based model with a feature-based approach to achieve practicable precision and recall rates. Features including transcript information, sequence composition, sequencing data features surrounding the peak or conservation information are obtained and fed into the subsequent Random Forest classifiers, whose results vote for the final classification score. Validation on multiple published m6A-seq data sets shows that m6aViewer can identify high-confidence methylated residues with more precision than other current existed approaches. Figure 9. View largeDownload slide The basic steps of m6aViewer, which can identify high-confidence methylated residues more precisely. It also provides a GUI for convenience. Figure 9. View largeDownload slide The basic steps of m6aViewer, which can identify high-confidence methylated residues more precisely. It also provides a GUI for convenience. Discussion and conclusion RNA is the intermediate molecule between DNA and proteins in the chain that links genetic information contained in genes to its expression in functional proteins, by either carrying this information in the form of mRNA or participating in mRNA expression, splicing, stability and translation in the form of noncoding RNAs. RNA methylation is a reversible posttranslational modification to RNA that adds a new dimension to the developing picture of gene expression regulation. It has been known to play critical roles in multiple biological processes by the advances in RNA detection and sequencing technologies. However, the sequencing protocol of RNA methylation is highly different from previous sequencing technologies. Although some databases, Web server and software as well as computational models have been established for RNA methylation, the function of most RNA methylations and their alterations in biological processes and human diseases are largely unknown for the lack of effective and precise processing methods [122]. A significant proportion of m6A methylation sites are enriched in the 5′UTR, around stop codon and the proximal region of 3′UTR of transcripts, while miRNA-targeted sites at the 5′ end and 3′ end of 3′ UTR suggested a potential link between m6A methylation and miRNA targeting sites, thus may regulate miRNA-related pathways [123–125]. To be more specific, miRNA miR-145 has been suggested by bioinformatics analysis that it might target the 3′UTR region of YTHDF2 MRNA, which is an m6A reader protein helping to recognize mRNA m6A sites to mediate mRNA degradation [140]. Another research showed that manipulation of miRNA expression or sequences altered m6A modification levels through modulating the binding of METTL3 methyltransferase to mRNA containing miRNA targeting sites, thus regulating m6A formation of mRNAs [142]. A knockdown of m6A demethylase FTO has been found to affect the steady-state levels of certain miRNAs [145]. Therefore, the prediction of associations between RNA methylation and miRNAs has great interest in its biogenesis and other fields. It will help better understand the complex regulation effect of both miRNA and RNA methylation. Furthermore, most studies focused on the association between m6A methylation and miRNAs. It can be further expanded to the other types of RNA methylation as well. Aberrant m6A modification patterns have been linked to diverse human diseases, including infertility, various forms of cancer, obesity, diabetes, depression and neurodevelopmental disorders, etc. For example, the m6A hyper methylation of IDH1/2 is found to play an important role in tumor progression, which can finally result in acute myeloid leukemia [76]. The m6A hypo methylation of RUNX1T1 is also found to participate in the disease progression in obesity. Thus, different methylation status in different genes may result in different phenotype. But it is really difficult and expensive to find the pathology of different types of RNA methylation in different diseases from experimental aspect only. Therefore, the prediction of RNA methylation–disease association, which can strongly guide the biological experiments, is of great significance in biological, medical and other fields. Based on network or machine learning models, the association probability between RNA methylation sites and diseases could be quantified and RNA methylation site–disease pairs with high confidence could be selected for further biological experimental validation. Thus, it will help understand the biogenesis, regulation and function of RNA methylation and human disease molecular mechanism at transcriptomic level, discover biomarkers and drugs for human disease diagnosis, treatment, prognosis and prevention with less time and cost of biological experiments. The first step to predict associations between RNA methylation and diseases or miRNAs is to identify the RNA methylation site precisely. Then, some new databases that annotate RNA methylation sequences provide the comprehensive information of RNA methylation sites or display and collect the experimentally confirmed RNA methylation–disease associations should be built for further analysis. To be more specific, some network-based models or machine learning models can be built to predict the association between diseases and those predicted sites based on those databases. Recently, scientists focused on building computational models to predict RNA methylation sites based on either sequence or high-throughput sequencing data, which is proved to be more time and cost-effective than biological methods. In this article, we summarized the known types of RNA methylation, the biological functions of RNA methylation, five RNA methylation-related diseases, seven publicly available RNA methylation-related databases, RNA methylation annotation, visualization tools, etc. Then, we introduced some state-of-art Web server and software as well as computational models for RNA methylation site prediction as well as differential analysis (Table 5). Most Web servers aim at identifying methylation sites based on sequence, and most Web servers are constructed following the five-step guidelines: (1) How to construct a valid benchmark data set to train and test the subsequent predictor? (2) How to represent the biological sequence with an valid mathematical formulation? (3) How to develop an effective prediction algorithm? (4) How to conduct the cross-validation to objectively estimate the performance of the predictor? (5) How to establish a user-friendly interface? These Web servers first build training data and testing data referring to RNA methylation motif DRACH (where D = A, G or U; R = A or G; H = A, C or U). Then, they follow different feature encoding scheme for discriminable features and fed them into classifiers for RNA methylation site prediction. Most sequence-based methods used SVM or SVM-based model as classifier. Some models, such as SRAMP adopted Random Forest and kNN for site prediction. With more discriminable features discovered in the future, prediction accuracy could be further improved. Table 5. Comparison list of the databases, Web servers and software Name  Type  Year of first version  RNA methylation type  Objection  Species  Maintained  Link  Databases  RNAMDB  Database  2011  N/A  A database for RNA modifications  N/A  No  http://rna-mdb.cas.albany.edu/RNAmods/  MODOMICS  Database  2013  N/A  A database of RNA modifications  N/A  Yes  http://modomics.genesilico.pl  RADAR  Database  2014  A to I  Collection of A-to-I RNA editing sites with annotation  Homo sapiens, M. musculus, D. melanogaster  Yes  http://RNAedit.com  MeT-DB  Database  2015  m6A  A database for publicaly available m6A data sets  N/A  Yes  http://compgenomics.utsa.edu/methylation/  RMBase  Database  2015  m6A, m5C, 2'-O- methylation, Ψ  A database for RNA modifications  N/A  System maintaining  http://mirlab.sysu.edu.cn/rmbase/  REDIportal  Database  2016  A to I  Collection of A-to-I RNA editing sites with annotation  N/A  Yes  http://srv00.recas.ba.infn.it/atlas/  Sequence-based site prediction tools  HAMR  Web server  2013  N/A  To predict RNA modification site (location and methylation class)  N/A  Yes  http://wanglab.pcbi.upenn.edu/hamr  M6Apred  Web server  2015  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://lin.uestc.edu.cn/server/m6Apred.php  iRNA-Methyl  Web server  2015  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-Methyl  PPUS  Web server  2015  Ψ  To predict Ψ site  N/A  Yes  http://lyh.pkmu.cn/ppus/  AthMethPre  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  No  http://bioinfo.tsinghua.edu.cn/AthMethPre/index.html  RNAMethPre  Web server  2016  m6A  To predict m6A site  Homo sapiens, M. musculus  No  http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html  m6ATH  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  Yes  http://lin.uestc.edu.cn/server/M6ATH  PRNAm-PC  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.jci-bioinfo.cn/pRNAm-PC  SRAMP  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.cuilab.cn/sramp/  RAMPred  Web server  2016  m1A  To predict m1A site  N/A  Yes  http://lin.uestc.edu.cn/server/RAMPred  iRNA-PseU  Web server  2016  Ψ  To predict Ψ site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseU  MethyRNA  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/methyrna  RAM-ESVM  Web server  2017  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://server.malab.cn/RAM-ESVM/  RAM-NPPS  Web server  2017  m6A  To predict m6A site  N/A  Yes  http://server.malab.cn/RAM-NPPS/  iRNA-AI  Web server  2017  A to I  To predict A-to-I site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-AI/  iRNA-PseColl  Web server  2017  m1A, m6A, m5C  To predict the occurrence sites of RNA modifications  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseColl  NGS data-based site detection tools  MeRIP-PF  Software  2013  m6A  To predict m6A-modified peaks  N/A  Yes  http://software.big.ac.cn/MeRIP-PF.html  exomePeak R/Bioconductor package  Software  2013  m6A  Alignment, site detection, motif discovery, differential analysis based on MeRIP-Seq data  N/A  Yes  http://www.bioconductor.org/packages/release/bioc /html/exomePeak.html  meRanTK  Software  2016  N/A  Alignment, site detection, differential analysis based on BS-seq data  N/A  Yes  http://icbi.at/software/meRanTK/  DRME  Software  2016  N/A  Differential analysis  N/A  Yes  https://github.com/lzcyzm/DRME  MeTPeak  Software  2016  m6A  To predict m6A-modified peaks  N/A  Yes  https://github.com/compgenomics/MeTPeak  txCoords  Web server  2016  m6A  To predict m6A-modified peaks  N/A  No  http://www.bioinfo.tsinghua.edu.cn/txCoords  Annotation and visualization of RNA modification tools  CAn  Software  2016  N/A  Visualization and analysis of modification signature in deep-sequencing data  N/A  Yes  https://sourceforge.net/projects/coverageanalyzer/  MetaPlotR  Software  2017  N/A  Create metagene plot  N/A  Yes  https://github.com/olarerin/metaPlotR  RCAS  Software  2017  N/A  Annotation  N/A  Yes  http://bioconductor.org/packages/release/bioc/html/RCAS  Name  Type  Year of first version  RNA methylation type  Objection  Species  Maintained  Link  Databases  RNAMDB  Database  2011  N/A  A database for RNA modifications  N/A  No  http://rna-mdb.cas.albany.edu/RNAmods/  MODOMICS  Database  2013  N/A  A database of RNA modifications  N/A  Yes  http://modomics.genesilico.pl  RADAR  Database  2014  A to I  Collection of A-to-I RNA editing sites with annotation  Homo sapiens, M. musculus, D. melanogaster  Yes  http://RNAedit.com  MeT-DB  Database  2015  m6A  A database for publicaly available m6A data sets  N/A  Yes  http://compgenomics.utsa.edu/methylation/  RMBase  Database  2015  m6A, m5C, 2'-O- methylation, Ψ  A database for RNA modifications  N/A  System maintaining  http://mirlab.sysu.edu.cn/rmbase/  REDIportal  Database  2016  A to I  Collection of A-to-I RNA editing sites with annotation  N/A  Yes  http://srv00.recas.ba.infn.it/atlas/  Sequence-based site prediction tools  HAMR  Web server  2013  N/A  To predict RNA modification site (location and methylation class)  N/A  Yes  http://wanglab.pcbi.upenn.edu/hamr  M6Apred  Web server  2015  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://lin.uestc.edu.cn/server/m6Apred.php  iRNA-Methyl  Web server  2015  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-Methyl  PPUS  Web server  2015  Ψ  To predict Ψ site  N/A  Yes  http://lyh.pkmu.cn/ppus/  AthMethPre  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  No  http://bioinfo.tsinghua.edu.cn/AthMethPre/index.html  RNAMethPre  Web server  2016  m6A  To predict m6A site  Homo sapiens, M. musculus  No  http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html  m6ATH  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  Yes  http://lin.uestc.edu.cn/server/M6ATH  PRNAm-PC  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.jci-bioinfo.cn/pRNAm-PC  SRAMP  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.cuilab.cn/sramp/  RAMPred  Web server  2016  m1A  To predict m1A site  N/A  Yes  http://lin.uestc.edu.cn/server/RAMPred  iRNA-PseU  Web server  2016  Ψ  To predict Ψ site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseU  MethyRNA  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/methyrna  RAM-ESVM  Web server  2017  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://server.malab.cn/RAM-ESVM/  RAM-NPPS  Web server  2017  m6A  To predict m6A site  N/A  Yes  http://server.malab.cn/RAM-NPPS/  iRNA-AI  Web server  2017  A to I  To predict A-to-I site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-AI/  iRNA-PseColl  Web server  2017  m1A, m6A, m5C  To predict the occurrence sites of RNA modifications  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseColl  NGS data-based site detection tools  MeRIP-PF  Software  2013  m6A  To predict m6A-modified peaks  N/A  Yes  http://software.big.ac.cn/MeRIP-PF.html  exomePeak R/Bioconductor package  Software  2013  m6A  Alignment, site detection, motif discovery, differential analysis based on MeRIP-Seq data  N/A  Yes  http://www.bioconductor.org/packages/release/bioc /html/exomePeak.html  meRanTK  Software  2016  N/A  Alignment, site detection, differential analysis based on BS-seq data  N/A  Yes  http://icbi.at/software/meRanTK/  DRME  Software  2016  N/A  Differential analysis  N/A  Yes  https://github.com/lzcyzm/DRME  MeTPeak  Software  2016  m6A  To predict m6A-modified peaks  N/A  Yes  https://github.com/compgenomics/MeTPeak  txCoords  Web server  2016  m6A  To predict m6A-modified peaks  N/A  No  http://www.bioinfo.tsinghua.edu.cn/txCoords  Annotation and visualization of RNA modification tools  CAn  Software  2016  N/A  Visualization and analysis of modification signature in deep-sequencing data  N/A  Yes  https://sourceforge.net/projects/coverageanalyzer/  MetaPlotR  Software  2017  N/A  Create metagene plot  N/A  Yes  https://github.com/olarerin/metaPlotR  RCAS  Software  2017  N/A  Annotation  N/A  Yes  http://bioconductor.org/packages/release/bioc/html/RCAS  Table 5. Comparison list of the databases, Web servers and software Name  Type  Year of first version  RNA methylation type  Objection  Species  Maintained  Link  Databases  RNAMDB  Database  2011  N/A  A database for RNA modifications  N/A  No  http://rna-mdb.cas.albany.edu/RNAmods/  MODOMICS  Database  2013  N/A  A database of RNA modifications  N/A  Yes  http://modomics.genesilico.pl  RADAR  Database  2014  A to I  Collection of A-to-I RNA editing sites with annotation  Homo sapiens, M. musculus, D. melanogaster  Yes  http://RNAedit.com  MeT-DB  Database  2015  m6A  A database for publicaly available m6A data sets  N/A  Yes  http://compgenomics.utsa.edu/methylation/  RMBase  Database  2015  m6A, m5C, 2'-O- methylation, Ψ  A database for RNA modifications  N/A  System maintaining  http://mirlab.sysu.edu.cn/rmbase/  REDIportal  Database  2016  A to I  Collection of A-to-I RNA editing sites with annotation  N/A  Yes  http://srv00.recas.ba.infn.it/atlas/  Sequence-based site prediction tools  HAMR  Web server  2013  N/A  To predict RNA modification site (location and methylation class)  N/A  Yes  http://wanglab.pcbi.upenn.edu/hamr  M6Apred  Web server  2015  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://lin.uestc.edu.cn/server/m6Apred.php  iRNA-Methyl  Web server  2015  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-Methyl  PPUS  Web server  2015  Ψ  To predict Ψ site  N/A  Yes  http://lyh.pkmu.cn/ppus/  AthMethPre  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  No  http://bioinfo.tsinghua.edu.cn/AthMethPre/index.html  RNAMethPre  Web server  2016  m6A  To predict m6A site  Homo sapiens, M. musculus  No  http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html  m6ATH  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  Yes  http://lin.uestc.edu.cn/server/M6ATH  PRNAm-PC  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.jci-bioinfo.cn/pRNAm-PC  SRAMP  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.cuilab.cn/sramp/  RAMPred  Web server  2016  m1A  To predict m1A site  N/A  Yes  http://lin.uestc.edu.cn/server/RAMPred  iRNA-PseU  Web server  2016  Ψ  To predict Ψ site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseU  MethyRNA  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/methyrna  RAM-ESVM  Web server  2017  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://server.malab.cn/RAM-ESVM/  RAM-NPPS  Web server  2017  m6A  To predict m6A site  N/A  Yes  http://server.malab.cn/RAM-NPPS/  iRNA-AI  Web server  2017  A to I  To predict A-to-I site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-AI/  iRNA-PseColl  Web server  2017  m1A, m6A, m5C  To predict the occurrence sites of RNA modifications  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseColl  NGS data-based site detection tools  MeRIP-PF  Software  2013  m6A  To predict m6A-modified peaks  N/A  Yes  http://software.big.ac.cn/MeRIP-PF.html  exomePeak R/Bioconductor package  Software  2013  m6A  Alignment, site detection, motif discovery, differential analysis based on MeRIP-Seq data  N/A  Yes  http://www.bioconductor.org/packages/release/bioc /html/exomePeak.html  meRanTK  Software  2016  N/A  Alignment, site detection, differential analysis based on BS-seq data  N/A  Yes  http://icbi.at/software/meRanTK/  DRME  Software  2016  N/A  Differential analysis  N/A  Yes  https://github.com/lzcyzm/DRME  MeTPeak  Software  2016  m6A  To predict m6A-modified peaks  N/A  Yes  https://github.com/compgenomics/MeTPeak  txCoords  Web server  2016  m6A  To predict m6A-modified peaks  N/A  No  http://www.bioinfo.tsinghua.edu.cn/txCoords  Annotation and visualization of RNA modification tools  CAn  Software  2016  N/A  Visualization and analysis of modification signature in deep-sequencing data  N/A  Yes  https://sourceforge.net/projects/coverageanalyzer/  MetaPlotR  Software  2017  N/A  Create metagene plot  N/A  Yes  https://github.com/olarerin/metaPlotR  RCAS  Software  2017  N/A  Annotation  N/A  Yes  http://bioconductor.org/packages/release/bioc/html/RCAS  Name  Type  Year of first version  RNA methylation type  Objection  Species  Maintained  Link  Databases  RNAMDB  Database  2011  N/A  A database for RNA modifications  N/A  No  http://rna-mdb.cas.albany.edu/RNAmods/  MODOMICS  Database  2013  N/A  A database of RNA modifications  N/A  Yes  http://modomics.genesilico.pl  RADAR  Database  2014  A to I  Collection of A-to-I RNA editing sites with annotation  Homo sapiens, M. musculus, D. melanogaster  Yes  http://RNAedit.com  MeT-DB  Database  2015  m6A  A database for publicaly available m6A data sets  N/A  Yes  http://compgenomics.utsa.edu/methylation/  RMBase  Database  2015  m6A, m5C, 2'-O- methylation, Ψ  A database for RNA modifications  N/A  System maintaining  http://mirlab.sysu.edu.cn/rmbase/  REDIportal  Database  2016  A to I  Collection of A-to-I RNA editing sites with annotation  N/A  Yes  http://srv00.recas.ba.infn.it/atlas/  Sequence-based site prediction tools  HAMR  Web server  2013  N/A  To predict RNA modification site (location and methylation class)  N/A  Yes  http://wanglab.pcbi.upenn.edu/hamr  M6Apred  Web server  2015  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://lin.uestc.edu.cn/server/m6Apred.php  iRNA-Methyl  Web server  2015  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-Methyl  PPUS  Web server  2015  Ψ  To predict Ψ site  N/A  Yes  http://lyh.pkmu.cn/ppus/  AthMethPre  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  No  http://bioinfo.tsinghua.edu.cn/AthMethPre/index.html  RNAMethPre  Web server  2016  m6A  To predict m6A site  Homo sapiens, M. musculus  No  http://bioinfo.tsinghua.edu.cn/RNAMethPre/index.html  m6ATH  Web server  2016  m6A  To predict m6A site  Arabidopsis thaliana  Yes  http://lin.uestc.edu.cn/server/M6ATH  PRNAm-PC  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.jci-bioinfo.cn/pRNAm-PC  SRAMP  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://www.cuilab.cn/sramp/  RAMPred  Web server  2016  m1A  To predict m1A site  N/A  Yes  http://lin.uestc.edu.cn/server/RAMPred  iRNA-PseU  Web server  2016  Ψ  To predict Ψ site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseU  MethyRNA  Web server  2016  m6A  To predict m6A site  N/A  Yes  http://lin.uestc.edu.cn/server/methyrna  RAM-ESVM  Web server  2017  m6A  To predict m6A site  Saccharomyces cerevisiae  Yes  http://server.malab.cn/RAM-ESVM/  RAM-NPPS  Web server  2017  m6A  To predict m6A site  N/A  Yes  http://server.malab.cn/RAM-NPPS/  iRNA-AI  Web server  2017  A to I  To predict A-to-I site  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-AI/  iRNA-PseColl  Web server  2017  m1A, m6A, m5C  To predict the occurrence sites of RNA modifications  N/A  Yes  http://lin.uestc.edu.cn/server/iRNA-PseColl  NGS data-based site detection tools  MeRIP-PF  Software  2013  m6A  To predict m6A-modified peaks  N/A  Yes  http://software.big.ac.cn/MeRIP-PF.html  exomePeak R/Bioconductor package  Software  2013  m6A  Alignment, site detection, motif discovery, differential analysis based on MeRIP-Seq data  N/A  Yes  http://www.bioconductor.org/packages/release/bioc /html/exomePeak.html  meRanTK  Software  2016  N/A  Alignment, site detection, differential analysis based on BS-seq data  N/A  Yes  http://icbi.at/software/meRanTK/  DRME  Software  2016  N/A  Differential analysis  N/A  Yes  https://github.com/lzcyzm/DRME  MeTPeak  Software  2016  m6A  To predict m6A-modified peaks  N/A  Yes  https://github.com/compgenomics/MeTPeak  txCoords  Web server  2016  m6A  To predict m6A-modified peaks  N/A  No  http://www.bioinfo.tsinghua.edu.cn/txCoords  Annotation and visualization of RNA modification tools  CAn  Software  2016  N/A  Visualization and analysis of modification signature in deep-sequencing data  N/A  Yes  https://sourceforge.net/projects/coverageanalyzer/  MetaPlotR  Software  2017  N/A  Create metagene plot  N/A  Yes  https://github.com/olarerin/metaPlotR  RCAS  Software  2017  N/A  Annotation  N/A  Yes  http://bioconductor.org/packages/release/bioc/html/RCAS  Furthermore, some high-throughput sequencing data-based computational models, such as exomePeak, MeTPeak, m6aViewer, are developed to predict RNA methylation site, which can be further extended for differential analysis. exomePeak and MeTPeak are based on R environment, which rely on a bin-based method. exomePeak predicts a methylation site by testing the small, equally sized regions divided from the transcriptome. If the number of reads of an exact bin in the immunoprecipitated sample is higher than that in the input sample, the bin is predicted as a significantly enriched bin. Then, those consecutive significantly enriched bins are merged together to form a larger region as peak. MeTPeak further model those consecutive significantly enriched bins by an HMM model to form a peak. However, significantly enriched regions predicted by the above two methods can span a large range, which can only achieve rough site identification. M6aViewer is developed on Java. It aims at detecting high-confidence m6A peaks by an EM-based deconvolution method, which helps to pinpoint m6A methylation sites with better precision. M6aViewer also combined the sequencing data-based computational models with sequence-based predictor to enhance the performance of false-positive rate. A supervised ensemble learning classifier is built to distinguish true-positive m6A sites from false-positive peaks. It is worth noting that the integration of sequence-based computational models with sequencing data-based models could help to further improve the accuracy of site prediction. Nowadays, a wide range of databases, Web servers about methylation site prediction have been built, providing a variety of methods for RNA methylation data processing. However, most methods focus on m6A methylation. It is expected to build such resources for the other types of methylation. Furthermore, RNA methylation–miRNA association, RNA methylation–disease association databases, Web servers and computational models should be constructed in the near future, which would benefit biologists to experimentally unveil the functions, mechanisms of RNA methylation. Key Points We made a brief introduction of the functions of RNA methylation, eight types of most popular RNA methylation, seven publicly available RNA methylation-related databases some important publicly available RNA-methylation-related Web server, software and computational tools for RNA methylation site identification, differential analysis and so on. Developing effective computational models to precisely identify methylation sites based on sequence or sequencing data could benefit better understanding of complex functions of RNA methylation. Making full use of different types of data sources, such as sequencing data with different technologies, sequences, etc., could benefit more effective discovery of new RNA methylation functions. RNA methylation–miRNA regulatory patterns could be predicted based on powerful computational models. Complex RNA methylation–disease associations could be predicted based on powerful computational models. Funding Fundamental Research Funds for the Central Universities (2017XKQY083 to X.C.). Xing Chen, PhD, is a professor of School of Information and Control Engineering, China University of Mining and Technology. He is also the founding director of Institute of Bioinformatics, China University of Mining and Technology. His research interests include disease, noncoding RNAs, network pharmacology, complex network and machine learning. Ya-Zhou Sun, PhD, is a postdoctor of College of Computer Science and Software Engineering, Shenzhen University. Her research interests include disease, noncoding RNAs, genomics, DNA damage repair and precise medicine. Hui Liu, PhD, is an associate professor of School of Information and Control Engineering, China University of Mining and Technology. His interests include noncoding RNAs, computational biology and machine learning. Lin Zhang, PhD, is an associate professor of School of Information and Control Engineering, China University of Mining and Technology. Her interests include computational biology, statistical signal processing and Bayesian methods. Jian-Qiang Li, PhD, is an associate professor of College of Computer Science and Software Engineering, Shenzhen University. He is also the executive director of Institute of Network and Information Security, Shenzhen University. His research interests include bioinformatics, artificial intelligence, mobile medical and complex systems. Jia Meng, PhD, is an associate professor of Department of Biological Sciences, Xi’an Jiaotong-Liverpool University. His interests include epitranscriptome bioinformatics, statistical modeling and NGS data mining. References 1 Thauer RK. Biochemistry of methanogenesis: a tribute to Marjory Stephenson. 1998 Marjory Stephenson Prize Lecture. Microbiology  1998; 144( 9): 2377– 406. 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Published: Nov 18, 2017

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