Nucleic Acids Research

You, Leiming; Wu, Jiexin; Feng, Yuchao; Fu, Yonggui; Guo, Yanan; Long, Liyuan; Zhang, Hui; Luan, Yijie; Tian, Peng; Chen, Liangfu; Huang, Guangrui; Huang, Shengfeng; Li, Yuxin; Li, Jie; Chen, Chengyong; Zhang, Yaqing; Chen, Shangwu; Xu, Anlong

doi: 10.1093/nar/gku1076pmid: 25378337

Increasing amounts of genes have been shown to utilize alternative polyadenylation (APA) 3′-processing sites depending on the cell and tissue type and/or physiological and pathological conditions at the time of processing, and the construction of genome-wide database regarding APA is urgently needed for better understanding poly(A) site selection and APA-directed gene expression regulation for a given biology. Here we present a web-accessible database, named APASdb (http://mosas.sysu.edu.cn/utr), which can visualize the precise map and usage quantification of different APA isoforms for all genes. The datasets are deeply profiled by the sequencing alternative polyadenylation sites (SAPAS) method capable of high-throughput sequencing 3′-ends of polyadenylated transcripts. Thus, APASdb details all the heterogeneous cleavage sites downstream of poly(A) signals, and maintains near complete coverage for APA sites, much better than the previous databases using conventional methods. Furthermore, APASdb provides the quantification of a given APA variant among transcripts with different APA sites by computing their corresponding normalized-reads, making our database more useful. In addition, APASdb supports URL-based retrieval, browsing and display of exon-intron structure, poly(A) signals, poly(A) sites location and usage reads, and 3′-untranslated regions (3′-UTRs). Currently, APASdb involves APA in various biological processes and diseases in human, mouse and zebrafish.

Altenhoff, Adrian M.; Škunca, Nives; Glover, Natasha; Train, Clément-Marie; Sueki, Anna; Piližota, Ivana; Gori, Kevin; Tomiczek, Bartlomiej; Müller, Steven; Redestig, Henning; Gonnet, Gaston H.; Dessimoz, Christophe

doi: 10.1093/nar/gku1158pmid: 25399418

The Orthologous Matrix (OMA) project is a method and associated database inferring evolutionary relationships amongst currently 1706 complete proteomes (i.e. the protein sequence associated for every protein-coding gene in all genomes). In this update article, we present six major new developments in OMA: (i) a new web interface; (ii) Gene Ontology function predictions as part of the OMA pipeline; (iii) better support for plant genomes and in particular homeologs in the wheat genome; (iv) a new synteny viewer providing the genomic context of orthologs; (v) statically computed hierarchical orthologous groups subsets downloadable in OrthoXML format; and (vi) possibility to export parts of the all-against-all computations and to combine them with custom data for ‘client-side’ orthology prediction. OMA can be accessed through the OMA Browser and various programmatic interfaces at http://omabrowser.org.

Abbott, Kenneth L.; Nyre, Erik T.; Abrahante, Juan; Ho, Yen-Yi; Isaksson Vogel, Rachel; Starr, Timothy K.

doi: 10.1093/nar/gku770pmid: 25190456

Identification of cancer driver gene mutations is crucial for advancing cancer therapeutics. Due to the overwhelming number of passenger mutations in the human tumor genome, it is difficult to pinpoint causative driver genes. Using transposon mutagenesis in mice many laboratories have conducted forward genetic screens and identified thousands of candidate driver genes that are highly relevant to human cancer. Unfortunately, this information is difficult to access and utilize because it is scattered across multiple publications using different mouse genome builds and strength metrics. To improve access to these findings and facilitate meta-analyses, we developed the Candidate Cancer Gene Database (CCGD, http://ccgd-starrlab.oit.umn.edu/). The CCGD is a manually curated database containing a unified description of all identified candidate driver genes and the genomic location of transposon common insertion sites (CISs) from all currently published transposon-based screens. To demonstrate relevance to human cancer, we performed a modified gene set enrichment analysis using KEGG pathways and show that human cancer pathways are highly enriched in the database. We also used hierarchical clustering to identify pathways enriched in blood cancers compared to solid cancers. The CCGD is a novel resource available to scientists interested in the identification of genetic drivers of cancer.

Li, Peng; Liu, Yongrui; Wang, Huan; He, Yuan; Wang, Xue; He, Yundong; Lv, Fang; Chen, Huaqing; Pang, Xiufeng; Liu, Mingyao; Shi, Tieliu; Yi, Zhengfang

doi: 10.1093/nar/gku1139pmid: 25392416

Angiogenesis is the process of generating new blood vessels based on existing ones, which is involved in many diseases including cancers, cardiovascular diseases and diabetes mellitus. Recently, great efforts have been made to explore the mechanisms of angiogenesis in various diseases and many angiogenic factors have been discovered as therapeutic targets in anti- or pro-angiogenic drug development. However, the resulted information is sparsely distributed and no systematical summarization has been made. In order to integrate these related results and facilitate the researches for the community, we conducted manual text-mining from published literature and built a database named as PubAngioGen (http://www.megabionet.org/aspd/). Our online application displays a comprehensive network for exploring the connection between angiogenesis and diseases at multilevels including protein–protein interaction, drug-target, disease-gene and signaling pathways among various cells and animal models recorded through text-mining. To enlarge the scope of the PubAngioGen application, our database also links to other common resources including STRING, DrugBank and OMIM databases, which will facilitate understanding the underlying molecular mechanisms of angiogenesis and drug development in clinical therapy.

Raies, Arwa Bin; Mansour, Hicham; Incitti, Roberto; Bajic, Vladimir B.

doi: 10.1093/nar/gku1168pmid: 25398897

Gathering information about associations between methylated genes and diseases is important for diseases diagnosis and treatment decisions. Recent advancements in epigenetics research allow for large-scale discoveries of associations of genes methylated in diseases in different species. Searching manually for such information is not easy, as it is scattered across a large number of electronic publications and repositories. Therefore, we developed DDMGD database (http://www.cbrc.kaust.edu.sa/ddmgd/) to provide a comprehensive repository of information related to genes methylated in diseases that can be found through text mining. DDMGD's scope is not limited to a particular group of genes, diseases or species. Using the text mining system DEMGD we developed earlier and additional post-processing, we extracted associations of genes methylated in different diseases from PubMed Central articles and PubMed abstracts. The accuracy of extracted associations is 82% as estimated on 2500 hand-curated entries. DDMGD provides a user-friendly interface facilitating retrieval of these associations ranked according to confidence scores. Submission of new associations to DDMGD is provided. A comparison analysis of DDMGD with several other databases focused on genes methylated in diseases shows that DDMGD is comprehensive and includes most of the recent information on genes methylated in diseases.

Schwach, Frank; Bushell, Ellen; Gomes, Ana Rita; Anar, Burcu; Girling, Gareth; Herd, Colin; Rayner, Julian C.; Billker, Oliver

doi: 10.1093/nar/gku1143pmid: 25593348

The Plasmodium Genetic Modification (PlasmoGEM) database (http://plasmogem.sanger.ac.uk) provides access to a resource of modular, versatile and adaptable vectors for genome modification of Plasmodium spp. parasites. PlasmoGEM currently consists of >2000 plasmids designed to modify the genome of Plasmodium berghei, a malaria parasite of rodents, which can be requested by non-profit research organisations free of charge. PlasmoGEM vectors are designed with long homology arms for efficient genome integration and carry gene specific barcodes to identify individual mutants. They can be used for a wide array of applications, including protein localisation, gene interaction studies and high-throughput genetic screens. The vector production pipeline is supported by a custom software suite that automates both the vector design process and quality control by full-length sequencing of the finished vectors. The PlasmoGEM web interface allows users to search a database of finished knock-out and gene tagging vectors, view details of their designs, download vector sequence in different formats and view available quality control data as well as suggested genotyping strategies. We also make gDNA library clones and intermediate vectors available for researchers to produce vectors for themselves.

Banerjee, Priyanka; Erehman, Jevgeni; Gohlke, Björn-Oliver; Wilhelm, Thomas; Preissner, Robert; Dunkel, Mathias

doi: 10.1093/nar/gku886pmid: 25300487

Natural products play a significant role in drug discovery and development. Many topological pharmacophore patterns are common between natural products and commercial drugs. A better understanding of the specific physicochemical and structural features of natural products is important for corresponding drug development. Several encyclopedias of natural compounds have been composed, but the information remains scattered or not freely available. The first version of the Supernatural database containing ∼50 000 compounds was published in 2006 to face these challenges. Here we present a new, updated and expanded version of natural product database, Super Natural II (http://bioinformatics.charite.de/supernatural), comprising ∼326 000 molecules. It provides all corresponding 2D structures, the most important structural and physicochemical properties, the predicted toxicity class for ∼170 000 compounds and the vendor information for the vast majority of compounds. The new version allows a template-based search for similar compounds as well as a search for compound names, vendors, specific physical properties or any substructures. Super Natural II also provides information about the pathways associated with synthesis and degradation of the natural products, as well as their mechanism of action with respect to structurally similar drugs and their target proteins.

Fernandez-Pozo, Noe; Menda, Naama; Edwards, Jeremy D.; Saha, Surya; Tecle, Isaak Y.; Strickler, Susan R.; Bombarely, Aureliano; Fisher-York, Thomas; Pujar, Anuradha; Foerster, Hartmut; Yan, Aimin; Mueller, Lukas A.

doi: 10.1093/nar/gku1195pmid: 25428362

The Sol Genomics Network (SGN, http://solgenomics.net) is a web portal with genomic and phenotypic data, and analysis tools for the Solanaceae family and close relatives. SGN hosts whole genome data for an increasing number of Solanaceae family members including tomato, potato, pepper, eggplant, tobacco and Nicotiana benthamiana. The database also stores loci and phenotype data, which researchers can upload and edit with user-friendly web interfaces. Tools such as BLAST, GBrowse and JBrowse for browsing genomes, expression and map data viewers, a locus community annotation system and a QTL analysis tools are available. A new tool was recently implemented to improve Virus-Induced Gene Silencing (VIGS) constructs called the SGN VIGS tool. With the growing genomic and phenotypic data in the database, SGN is now advancing to develop new web-based breeding tools and implement the code and database structure for other species or clade-specific databases.

Nagano, Nozomi; Nakayama, Naoko; Ikeda, Kazuyoshi; Fukuie, Masaru; Yokota, Kiyonobu; Doi, Takuo; Kato, Tsuyoshi; Tomii, Kentaro

doi: 10.1093/nar/gku946pmid: 25324316

The EzCatDB database (http://ezcatdb.cbrc.jp/EzCatDB/) has emphasized manual classification of enzyme reactions from the viewpoints of enzyme active-site structures and their catalytic mechanisms based on literature information, amino acid sequences of enzymes (UniProtKB) and the corresponding tertiary structures from the Protein Data Bank (PDB). Reaction types such as hydrolysis, transfer, addition, elimination, isomerization, hydride transfer and electron transfer have been included in the reaction classification, RLCP. This database includes information related to ligand molecules on the enzyme structures in the PDB data, classified in terms of cofactors, substrates, products and intermediates, which are also necessary to elucidate the catalytic mechanisms. Recently, the database system was updated. The 3D structures of active sites for each PDB entry can be viewed using Jmol or Rasmol software. Moreover, sequence search systems of two types were developed for the EzCatDB database: EzCat-BLAST and EzCat-FORTE. EzCat-BLAST is suitable for quick searches, adopting the BLAST algorithm, whereas EzCat-FORTE is more suitable for detecting remote homologues, adopting the algorithm for FORTE protein structure prediction software. Another system, EzMetAct, is also available to searching for major active-site structures in EzCatDB, for which PDB-formatted queries can be searched.

Chiu, Tsu-Pei; Yang, Lin; Zhou, Tianyin; Main, Bradley J.; Parker, Stephen C.J.; Nuzhdin, Sergey V.; Tullius, Thomas D.; Rohs, Remo

doi: 10.1093/nar/gku977pmid: 25326329

Many regulatory mechanisms require a high degree of specificity in protein-DNA binding. Nucleotide sequence does not provide an answer to the question of why a protein binds only to a small subset of the many putative binding sites in the genome that share the same core motif. Whereas higher-order effects, such as chromatin accessibility, cooperativity and cofactors, have been described, DNA shape recently gained attention as another feature that fine-tunes the DNA binding specificities of some transcription factor families. Our Genome Browser for DNA shape annotations (GBshape; freely available at http://rohslab.cmb.usc.edu/GBshape/) provides minor groove width, propeller twist, roll, helix twist and hydroxyl radical cleavage predictions for the entire genomes of 94 organisms. Additional genomes can easily be added using the GBshape framework. GBshape can be used to visualize DNA shape annotations qualitatively in a genome browser track format, and to download quantitative values of DNA shape features as a function of genomic position at nucleotide resolution. As biological applications, we illustrate the periodicity of DNA shape features that are present in nucleosome-occupied sequences from human, fly and worm, and we demonstrate structural similarities between transcription start sites in the genomes of four Drosophila species.