Reactome: a database of reactions, pathways and biological processes

Gene-class analysis of expression patterns induced by psychoactive pharmaceutical exposure in fathead minnow (Pimephales promelas) indicates induction of neuronal systems

Psychoactive pharmaceuticals are among the most frequently prescribed drugs, contributing to persistent measurable concentrations in aquatic systems. Typically, it is assumed that such contaminants have no human health implications because they exist in extremely low concentrations. We exposed juvenile fathead minnows (Pimephales promelas) to three pharmaceuticals, fluoxetine, venlafaxine and carbamazepine, individually and in a mixture, and measured their effect on the induction of gene expression in fish brains using microarray analysis. Gene expression changes were accompanied by behavioral changes and validated by qPCR analysis. Gene Set Enrichment Analysis was used to perform gene-class analysis of gene expression, testing for enrichment of gene sets known to be involved in human neuronal development, regulation and growth. We found significant enrichment of gene sets for each of the treatments, with the largest induction of expression by the mixture treatment. These results suggest that the psychoactive pharmaceuticals are able to alter expression of fish genes associated with development, regulation and differentiation of synapses, neurons and neurotransmitters. The results provide a new perspective for the consideration of potential consequence for human health due to environmental exposure to unmetabolized psychoactive pharmaceuticals.

Integrated inference and analysis of regulatory networks from multi-level measurements

Regulatory and signaling networks coordinate the enormously complex interactions and processes that control cellular processes (such as metabolism and cell division), coordinate response to the environment, and carry out multiple cell decisions (such as development and quorum sensing). Regulatory network inference is the process of inferring these networks, traditionally from microarray data but increasingly incorporating other measurement types such as proteomics, ChIP-seq, metabolomics, and mass cytometry. We discuss existing techniques for network inference. We review in detail our pipeline, which consists of an initial biclustering step, designed to estimate co-regulated groups; a network inference step, designed to select and parameterize likely regulatory models for the control of the co-regulated groups from the biclustering step; and a visualization and analysis step, designed to find and communicate key features of the network. Learning biological networks from even the most complete data sets is challenging; we argue that integrating new data types into the inference pipeline produces networks of increased accuracy, validity, and biological relevance.

MOPED: Model Organism Protein Expression Database

Large numbers of mass spectrometry proteomics studies are being conducted to understand all types of biological processes. The size and complexity of proteomics data hinders efforts to easily share, integrate, query and compare the studies. The Model Organism Protein Expression Database (MOPED, htttp://moped.proteinspire.org) is a new and expanding proteomics resource that enables rapid browsing of protein expression information from publicly available studies on humans and model organisms. MOPED is designed to simplify the comparison and sharing of proteomics data for the greater research community. MOPED uniquely provides protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis. Data can be queried for specific proteins, browsed based on organism, tissue, localization and condition and sorted by false discovery rate and expression. MOPED empowers users to visualize their own expression data and compare it with existing studies. Further, MOPED links to various protein and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of MOPED contains over 43,000 proteins with at least one spectral match and more than 11 million high certainty spectra.

A protein complex network of Drosophila melanogaster

Determining the composition of protein complexes is an essential step toward understanding the cell as an integrated system. Using coaffinity purification coupled to mass spectrometry analysis, we examined protein associations involving nearly 5,000 individual, FLAG-HA epitope-tagged Drosophila proteins. Stringent analysis of these data, based on a statistical framework designed to define individual protein-protein interactions, led to the generation of a Drosophila protein interaction map (DPiM) encompassing 556 protein complexes. The high quality of the DPiM and its usefulness as a paradigm for metazoan proteomes are apparent from the recovery of many known complexes, significant enrichment for shared functional attributes, and validation in human cells. The DPiM defines potential novel members for several important protein complexes and assigns functional links to 586 protein-coding genes lacking previous experimental annotation. The DPiM represents, to our knowledge, the largest metazoan protein complex map and provides a valuable resource for analysis of protein complex evolution.

Network systems biology for targeted cancer therapies

The era of targeted cancer therapies has arrived. However, due to the complexity of biological systems, the current progress is far from enough. From biological network modeling to structural/dynamic network analysis, network systems biology provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells. It has also introduced great changes into the research paradigm of cancer-associated drug discovery and drug resistance.

The Zebrafish Neurophenome Database (ZND): a dynamic open-access resource for zebrafish neurophenotypic data

Zebrafish (Danio rerio) are widely used in neuroscience research, where their utility as a model organism is rapidly expanding. Low cost, ease of experimental manipulations, and sufficient behavioral complexity make zebrafish a valuable tool for high-throughput studies in biomedicine. To complement the available repositories for zebrafish genetic information, there is a growing need for the collection of zebrafish neurobehavioral and neurological phenotypes. For this, we are establishing the Zebrafish Neurophenome Database (ZND; www.tulane.edu/∼znpindex/search ) as a new dynamic online open-access data repository for behavioral and related physiological data. ZND, currently focusing on adult zebrafish, combines zebrafish neurophenotypic data with a simple, easily searchable user interface, which allow scientists to view and compare results obtained by other laboratories using various treatments in different testing paradigms. As a developing community effort, ZND is expected to foster innovative research using zebrafish by federating the growing body of zebrafish neurophenotypic data.

Phosphorylation in protein-protein binding: effect on stability and function

Posttranslational modifications offer a dynamic way to regulate protein activity, subcellular localization, and stability. Here we estimate the effect of phosphorylation on protein binding and function for different types of complexes from human proteome. We find that phosphorylation sites tend to be located on binding interfaces in heterooligomeric and weak transient homooligomeric complexes. Analysis of molecular mechanisms of phosphorylation shows that phosphorylation may modulate the strength of interactions directly on interfaces and that binding hotspots tend to be phosphorylated in heterooligomers. Although the majority of complexes do not show significant estimated stability differences upon phosphorylation or dephosphorylation, for about one-third of all complexes it causes relatively large changes in binding energy. We discuss the cases where phosphorylation mediates the complex formation and regulates the function. We show that phosphorylation sites are more likely to be evolutionary conserved than other interfacial residues.

Gene expression and pathway analysis of ovarian cancer cells selected for resistance to cisplatin, paclitaxel, or doxorubicin

BACKGROUND

Resistance to current chemotherapeutic agents is a major cause of therapy failure in ovarian cancer patients, but the exact mechanisms leading to the development of drug resistance remain unclear.

METHODS

To better understand mechanisms of drug resistance, and possibly identify novel targets for therapy, we generated a series of drug resistant ovarian cancer cell lines through repeated exposure to three chemotherapeutic drugs (cisplatin, doxorubicin, or paclitaxel), and identified changes in gene expression patterns using Illumina whole-genome expression microarrays. Validation of selected genes was performed by RT-PCR and immunoblotting. Pathway enrichment analysis using the KEGG, GO, and Reactome databases was performed to identify pathways that may be important in each drug resistance phenotype.

RESULTS

A total of 845 genes (p < 0.01) were found altered in at least one drug resistance phenotype when compared to the parental, drug sensitive cell line. Focusing on each resistance phenotype individually, we identified 460, 366, and 337 genes significantly altered in cells resistant to cisplatin, doxorubicin, and paclitaxel, respectively. Of the 845 genes found altered, only 62 genes were simultaneously altered in all three resistance phenotypes. Using pathway analysis, we found many pathways enriched for each resistance phenotype, but some dominant pathways emerged. The dominant pathways included signaling from the cell surface and cell movement for cisplatin resistance, proteasome regulation and steroid biosynthesis for doxorubicin resistance, and control of translation and oxidative stress for paclitaxel resistance.

CONCLUSIONS

Ovarian cancer cells develop drug resistance through different pathways depending on the drug used in the generation of chemoresistance. A better understanding of these mechanisms may lead to the development of novel strategies to circumvent the problem of drug resistance.

The 2012 Nucleic Acids Research Database Issue and the online Molecular Biology Database Collection

The 19th annual Database Issue of Nucleic Acids Research features descriptions of 92 new online databases covering various areas of molecular biology and 100 papers describing recent updates to the databases previously described in NAR and other journals. The highlights of this issue include, among others, a description of neXtProt, a knowledgebase on human proteins; a detailed explanation of the principles behind the NCBI Taxonomy Database; NCBI and EBI papers on the recently launched BioSample databases that store sample information for a variety of database resources; descriptions of the recent developments in the Gene Ontology and UniProt Gene Ontology Annotation projects; updates on Pfam, SMART and InterPro domain databases; update papers on KEGG and TAIR, two universally acclaimed databases that face an uncertain future; and a separate section with 10 wiki-based databases, introduced in an accompanying editorial. The NAR online Molecular Biology Database Collection, available at http://www.oxfordjournals.org/nar/database/a/, has been updated and now lists 1380 databases. Brief machine-readable descriptions of the databases featured in this issue, according to the BioDBcore standards, will be provided at the http://biosharing.org/biodbcore web site. The full content of the Database Issue is freely available online on the Nucleic Acids Research web site (http://nar.oxfordjournals.org/).

IPAVS: Integrated Pathway Resources, Analysis and Visualization System

Integrated Pathway Resources, Analysis and Visualization System (iPAVS) is an integrated biological pathway database designed to support pathway discovery in the fields of proteomics, transcriptomics, metabolomics and systems biology. The key goal of IPAVS is to provide biologists access to expert-curated pathways from experimental data belonging to specific biological contexts related to cell types, tissues, organs and diseases. IPAVS currently integrates over 500 human pathways (consisting of 24 574 interactions) that include metabolic-, signaling- and disease-related pathways, drug–action pathways and several large process maps collated from other pathway resources. IPAVS web interface allows biologists to browse and search pathway resources and provides tools for data import, management, visualization and analysis to support the interpretation of biological data in light of cellular processes. Systems Biology Graphical Notations (SBGN) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway notations are used for the visual display of pathway information. The integrated datasets in IPAVS are made available in several standard data formats that can be downloaded. IPAVS is available at: http://ipavs.cidms.org.

AutismKB: an evidence-based knowledgebase of autism genetics

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder with a prevalence of 0.9–2.6%. Twin studies showed a heritability of 38–90%, indicating strong genetic contributions. Yet it is unclear how many genes have been associated with ASD and how strong the evidence is. A comprehensive review and analysis of literature and data may bring a clearer big picture of autism genetics. We show that as many as 2193 genes, 2806 SNPs/VNTRs, 4544 copy number variations (CNVs) and 158 linkage regions have been associated with ASD by GWAS, genome-wide CNV studies, linkage analyses, low-scale genetic association studies, expression profiling and other low-scale experimental studies. To evaluate the evidence, we collected metadata about each study including clinical and demographic features, experimental design and statistical significance, and used a scoring and ranking approach to select a core data set of 434 high-confidence genes. The genes mapped to pathways including neuroactive ligand–receptor interaction, synapse transmission and axon guidance. To better understand the genes we parsed over 30 databases to retrieve extensive data about expression patterns, protein interactions, animal models and pharmacogenetics. We constructed a MySQL-based online database and share it with the broader autism research community at http://autismkb.cbi.pku.edu.cn, supporting sophisticated browsing and searching functionalities.

MMMDB: Mouse Multiple Tissue Metabolome Database

The Mouse Multiple Tissue Metabolome Database (MMMDB) provides comprehensive and quantitative metabolomic information for multiple tissues from single mice. Manually curated databases that integrate literature-based individual metabolite information have been available so far. However, data sets on the absolute concentration of a single metabolite integrated from multiple resources are often difficult to be used when different metabolomic studies are compared because the relative balance of the multiple metabolite concentrations in the metabolic pathways as a snapshot of a dynamic system is more important than the absolute concentration of a single metabolite. We developed MMMDB by performing non-targeted analyses of cerebra, cerebella, thymus, spleen, lung, liver, kidney, heart, pancreas, testis and plasma using capillary electrophoresis time-of-flight mass spectrometry and detected 428 non-redundant features from which 219 metabolites were successfully identified. Quantified concentrations of the individual metabolites and the corresponding processed raw data; for example, the electropherograms and mass spectra with their annotations, such as isotope and fragment information, are stored in the database. MMMDB is designed to normalize users’ data, which can be submitted online and used to visualize overlaid electropherograms. Thus, MMMDB allows newly measured data to be compared with the other data in the database. MMMDB is available at: http://mmmdb.iab.keio.ac.jp.

hiPathDB: a human-integrated pathway database with facile visualization

One of the biggest challenges in the study of biological regulatory networks is the systematic organization and integration of complex interactions taking place within various biological pathways. Currently, the information of the biological pathways is dispersed in multiple databases in various formats. hiPathDB is an integrated pathway database that combines the curated human pathway data of NCI-Nature PID, Reactome, BioCarta and KEGG. In total, it includes 1661 pathways consisting of 8976 distinct physical entities. hiPathDB provides two different types of integration. The pathway-level integration, conceptually a simple collection of individual pathways, was achieved by devising an elaborate model that takes distinct features of four databases into account and subsequently reformatting all pathways in accordance with our model. The entity-level integration creates a single unified pathway that encompasses all pathways by merging common components. Even though the detailed molecular-level information such as complex formation or post-translational modifications tends to be lost, such integration makes it possible to investigate signaling network over the entire pathways and allows identification of pathway cross-talks. Another strong merit of hiPathDB is the built-in pathway visualization module that supports explorative studies of complex networks in an interactive fashion. The layout algorithm is optimized for virtually automatic visualization of the pathways. hiPathDB is available at http://hiPathDB.kobic.re.kr.

DNAtraffic--a new database for systems biology of DNA dynamics during the cell life

DNAtraffic (http://dnatraffic.ibb.waw.pl/) is dedicated to be a unique comprehensive and richly annotated database of genome dynamics during the cell life. It contains extensive data on the nomenclature, ontology, structure and function of proteins related to the DNA integrity mechanisms such as chromatin remodeling, histone modifications, DNA repair and damage response from eight organisms: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on the diseases related to the assembled human proteins. DNAtraffic is richly annotated in the systemic information on the nomenclature, chemistry and structure of DNA damage and their sources, including environmental agents or commonly used drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA network analysis. Database includes illustrations of pathways, damage, proteins and drugs. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines, it has to be extensively linked to numerous external data sources. Our database represents the result of the manual annotation work aimed at making the DNAtraffic much more useful for a wide range of systems biology applications.

GeneSigDB: a manually curated database and resource for analysis of gene expression signatures

GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a 'basket' feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org.

WikiPathways: building research communities on biological pathways

Here, we describe the development of WikiPathways (http://www.wikipathways.org), a public wiki for pathway curation, since it was first published in 2008. New features are discussed, as well as developments in the community of contributors. New features include a zoomable pathway viewer, support for pathway ontology annotations, the ability to mark pathways as private for a limited time and the availability of stable hyperlinks to pathways and the elements therein. WikiPathways content is freely available in a variety of formats such as the BioPAX standard, and the content is increasingly adopted by external databases and tools, including Wikipedia. A recent development is the use of WikiPathways as a staging ground for centrally curated databases such as Reactome. WikiPathways is seeing steady growth in the number of users, page views and edits for each pathway. To assess whether the community curation experiment can be considered successful, here we analyze the relation between use and contribution, which gives results in line with other wiki projects. The novel use of pathway pages as supplementary material to publications, as well as the addition of tailored content for research domains, is expected to stimulate growth further.

modMine: flexible access to modENCODE data

In an effort to comprehensively characterize the functional elements within the genomes of the important model organisms Drosophila melanogaster and Caenorhabditis elegans, the NHGRI model organism Encyclopaedia of DNA Elements (modENCODE) consortium has generated an enormous library of genomic data along with detailed, structured information on all aspects of the experiments. The modMine database (http://intermine.modencode.org) described here has been built by the modENCODE Data Coordination Center to allow the broader research community to (i) search for and download data sets of interest among the thousands generated by modENCODE; (ii) access the data in an integrated form together with non-modENCODE data sets; and (iii) facilitate fine-grained analysis of the above data. The sophisticated search features are possible because of the collection of extensive experimental metadata by the consortium. Interfaces are provided to allow both biologists and bioinformaticians to exploit these rich modENCODE data sets now available via modMine.

STITCH 3: zooming in on protein-chemical interactions

To facilitate the study of interactions between proteins and chemicals, we have created STITCH, an aggregated database of interactions connecting over 300 000 chemicals and 2.6 million proteins from 1133 organisms. Compared to the previous version, the number of chemicals with interactions and the number of high-confidence interactions both increase 4-fold. The database can be accessed interactively through a web interface, displaying interactions in an integrated network view. It is also available for computational studies through downloadable files and an API. As an extension in the current version, we offer the option to switch between two levels of detail, namely whether stereoisomers of a given compound are shown as a merged entity or as separate entities. Separate display of stereoisomers is necessary, for example, for carbohydrates and chiral drugs. Combining the isomers increases the coverage, as interaction databases and publications found through text mining will often refer to compounds without specifying the stereoisomer. The database is accessible at http://stitch.embl.de/.

SuperTarget goes quantitative: update on drug-target interactions

There are at least two good reasons for the on-going interest in drug–target interactions: first, drug-effects can only be fully understood by considering a complex network of interactions to multiple targets (so-called off-target effects) including metabolic and signaling pathways; second, it is crucial to consider drug-target-pathway relations for the identification of novel targets for drug development. To address this on-going need, we have developed a web-based data warehouse named SuperTarget, which integrates drug-related information associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. At present, the updated database contains >6000 target proteins, which are annotated with >330 000 relations to 196 000 compounds (including approved drugs); the vast majority of interactions include binding affinities and pointers to the respective literature sources. The user interface provides tools for drug screening and target similarity inclusion. A query interface enables the user to pose complex queries, for example, to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target proteins within a certain affinity range. SuperTarget is available at http://bioinformatics.charite.de/supertarget.

KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases

High-throughput experimental technologies often identify dozens to hundreds of genes related to, or changed in, a biological or pathological process. From these genes one wants to identify biological pathways that may be involved and diseases that may be implicated. Here, we report a web server, KOBAS 2.0, which annotates an input set of genes with putative pathways and disease relationships based on mapping to genes with known annotations. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). KOBAS 2.0 can be accessed at http://kobas.cbi.pku.edu.cn.

Critical amino acid residues in proteins: a BioMart integration of Reactome protein annotations with PRIDE mass spectrometry data and COSMIC somatic mutations

The reversible phosphorylation of serine, threonine and tyrosine hydroxyl groups is an especially prominent form of post-translational modification (PTM) of proteins. It plays critical roles in the regulation of diverse processes, and mutations that directly or indirectly affect these phosphorylation events have been associated with many cancers and other pathologies. Here, we describe the development of a new BioMart tool that gathers data from three different biological resources to provide the user with an integrated view of phosphorylation events associated with a human protein of interest, the complexes of which the protein (modified or not) is a part, the reactions in which the protein and its complexes participate and the somatic mutations that might be expected to perturb those functions. The three resources used are the Reactome, PRIDE and COSMIC databases. The Reactome knowledgebase contains annotations of phosphorylated human proteins linked to the reactions in which they are phosphorylated and dephosphorylated, to the complexes of which they are parts and to the reactions in which the phosphorylated proteins participate as substrates, catalysts and regulators. The PRIDE database holds extensive mass spectrometry data from which protein phosphorylation patterns can be inferred, and the COSMIC database holds records of somatic mutations found in human cancer cells. This tool supports both flexible, user-specified queries and standard (‘canned’) queries to retrieve frequently used combinations of data for user-specified proteins and reactions. We demonstrate using the Wnt signaling pathway and the human c-SRC protein how the tool can be used to place somatic mutation data into a functional perspective by changing critical residues involved in pathway modulation, and where available, check for mass spectrometry evidence in PRIDE supporting identification of the critical residue.

Database URL:http://www.reactome.org/cgi-bin/mart

BioPAX support in CellDesigner

MOTIVATION

BioPAX is a standard language for representing and exchanging models of biological processes at the molecular and cellular levels. It is widely used by different pathway databases and genomics data analysis software. Currently, the primary source of BioPAX data is direct exports from the curated pathway databases. It is still uncommon for wet-lab biologists to share and exchange pathway knowledge using BioPAX. Instead, pathways are usually represented as informal diagrams in the literature. In order to encourage formal representation of pathways, we describe a software package that allows users to create pathway diagrams using CellDesigner, a user-friendly graphical pathway-editing tool and save the pathway data in BioPAX Level 3 format.

AVAILABILITY

The plug-in is freely available and can be downloaded at ftp://ftp.pantherdb.org/CellDesigner/plugins/BioPAX/ CONTACT: huaiyumi@usc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Reactome BioMart

Reactome is an open source, expert-authored, manually curated and peer-reviewed database of reactions, pathways and biological processes. We provide an intuitive web-based user interface to pathway knowledge and a suite of data analysis tools. The Reactome BioMart provides biologists and bioinformaticians with a single web interface for performing simple or elaborate queries of the Reactome database, aggregating data from different sources and providing an opportunity to integrate experimental and computational results with information relating to biological pathways.

Database URL:http://www.reactome.org

Critical assessment of human metabolic pathway databases: a stepping stone for future integration

Background

Multiple pathway databases are available that describe the human metabolic network and have proven their usefulness in many applications, ranging from the analysis and interpretation of high-throughput data to their use as a reference repository. However, so far the various human metabolic networks described by these databases have not been systematically compared and contrasted, nor has the extent to which they differ been quantified. For a researcher using these databases for particular analyses of human metabolism, it is crucial to know the extent of the differences in content and their underlying causes. Moreover, the outcomes of such a comparison are important for ongoing integration efforts.

Results

We compared the genes, EC numbers and reactions of five frequently used human metabolic pathway databases. The overlap is surprisingly low, especially on reaction level, where the databases agree on 3% of the 6968 reactions they have combined. Even for the well-established tricarboxylic acid cycle the databases agree on only 5 out of the 30 reactions in total. We identified the main causes for the lack of overlap. Importantly, the databases are partly complementary. Other explanations include the number of steps a conversion is described in and the number of possible alternative substrates listed. Missing metabolite identifiers and ambiguous names for metabolites also affect the comparison.

Conclusions

Our results show that each of the five networks compared provides us with a valuable piece of the puzzle of the complete reconstruction of the human metabolic network. To enable integration of the networks, next to a need for standardizing the metabolite names and identifiers, the conceptual differences between the databases should be resolved. Considerable manual intervention is required to reach the ultimate goal of a unified and biologically accurate model for studying the systems biology of human metabolism. Our comparison provides a stepping stone for such an endeavor.

Functional enrichment analysis with structural variants: pitfalls and strategies

Interpreting the phenotypic consequences of human structural variation remains challenging. Functional enrichment analysis, which can identify functional enrichments among genes affected by structural variants, is providing significant biological insights into the genotype-phenotype relationship. In this review, we discuss the different approaches and choices in the application of this technique to human structural variation. We consider the importance of choosing the right background distribution for detection, the significance of the gene selection criteria, the effects of tissue-specific gene length biases and discuss sources of functional annotations with a focus on Gene Ontology and mouse phenotypic resources. Throughout this review, we highlight potential sources of significant bias that are of particular concern to the analysis of structural variants, and illustrate the importance of examining the expectations upon which enrichment analysis techniques depend.

The curation paradigm and application tool used for manual curation of the scientific literature at the Comparative Toxicogenomics Database

The Comparative Toxicogenomics Database (CTD) is a public resource that promotes understanding about the effects of environmental chemicals on human health. CTD biocurators read the scientific literature and convert free-text information into a structured format using official nomenclature, integrating third party controlled vocabularies for chemicals, genes, diseases and organisms, and a novel controlled vocabulary for molecular interactions. Manual curation produces a robust, richly annotated dataset of highly accurate and detailed information. Currently, CTD describes over 349 000 molecular interactions between 6800 chemicals, 20 900 genes (for 330 organisms) and 4300 diseases that have been manually curated from over 25 400 peer-reviewed articles. This manually curated data are further integrated with other third party data (e.g. Gene Ontology, KEGG and Reactome annotations) to generate a wealth of toxicogenomic relationships. Here, we describe our approach to manual curation that uses a powerful and efficient paradigm involving mnemonic codes. This strategy allows biocurators to quickly capture detailed information from articles by generating simple statements using codes to represent the relationships between data types. The paradigm is versatile, expandable, and able to accommodate new data challenges that arise. We have incorporated this strategy into a web-based curation tool to further increase efficiency and productivity, implement quality control in real-time and accommodate biocurators working remotely.

Database URL:http://ctd.mdibl.org

The representation of protein complexes in the Protein Ontology (PRO)

Background

Representing species-specific proteins and protein complexes in ontologies that are both human- and machine-readable facilitates the retrieval, analysis, and interpretation of genome-scale data sets. Although existing protin-centric informatics resources provide the biomedical research community with well-curated compendia of protein sequence and structure, these resources lack formal ontological representations of the relationships among the proteins themselves. The Protein Ontology (PRO) Consortium is filling this informatics resource gap by developing ontological representations and relationships among proteins and their variants and modified forms. Because proteins are often functional only as members of stable protein complexes, the PRO Consortium, in collaboration with existing protein and pathway databases, has launched a new initiative to implement logical and consistent representation of protein complexes.

Description

We describe here how the PRO Consortium is meeting the challenge of representing species-specific protein complexes, how protein complex representation in PRO supports annotation of protein complexes and comparative biology, and how PRO is being integrated into existing community bioinformatics resources. The PRO resource is accessible at http://pir.georgetown.edu/pro/.

Conclusion

PRO is a unique database resource for species-specific protein complexes. PRO facilitates robust annotation of variations in composition and function contexts for protein complexes within and between species.

Fungal genome resources at NCBI

The National Center for Biotechnology Information (NCBI) is well known for the nucleotide sequence archive, GenBank and sequence analysis tool BLAST. However, NCBI integrates many types of biomolecular data from variety of sources and makes it available to the scientific community as interactive web resources as well as organized releases of bulk data. These tools are available to explore and compare fungal genomes. Searching all databases with Fungi [organism] at http://www.ncbi.nlm.nih.gov/ is the quickest way to find resources of interest with fungal entries. Some tools though are resources specific and can be indirectly accessed from a particular database in the Entrez system. These include graphical viewers and comparative analysis tools such as TaxPlot, TaxMap and UniGene DDD (found via UniGene Homepage). Gene and BioProject pages also serve as portals to external data such as community annotation websites, BioGrid and UniProt. There are many different ways of accessing genomic data at NCBI. Depending on the focus and goal of research projects or the level of interest, a user would select a particular route for accessing genomic databases and resources. This review article describes methods of accessing fungal genome data and provides examples that illustrate the use of analysis tools.

ppiTrim: constructing non-redundant and up-to-date interactomes

Robust advances in interactome analysis demand comprehensive, non-redundant and consistently annotated data sets. By non-redundant, we mean that the accounting of evidence for every interaction should be faithful: each independent experimental support is counted exactly once, no more, no less. While many interactions are shared among public repositories, none of them contains the complete known interactome for any model organism. In addition, the annotations of the same experimental result by different repositories often disagree. This brings up the issue of which annotation to keep while consolidating evidences that are the same. The iRefIndex database, including interactions from most popular repositories with a standardized protein nomenclature, represents a significant advance in all aspects, especially in comprehensiveness. However, iRefIndex aims to maintain all information/annotation from original sources and requires users to perform additional processing to fully achieve the aforementioned goals. Another issue has to do with protein complexes. Some databases represent experimentally observed complexes as interactions with more than two participants, while others expand them into binary interactions using spoke or matrix model. To avoid untested interaction information buildup, it is preferable to replace the expanded protein complexes, either from spoke or matrix models, with a flat list of complex members.

To address these issues and to achieve our goals, we have developed ppiTrim, a script that processes iRefIndex to produce non-redundant, consistently annotated data sets of physical interactions. Our script proceeds in three stages: mapping all interactants to gene identifiers and removing all undesired raw interactions, deflating potentially expanded complexes, and reconciling for each interaction the annotation labels among different source databases. As an illustration, we have processed the three largest organismal data sets: yeast, human and fruitfly. While ppiTrim can resolve most apparent conflicts between different labelings, we also discovered some unresolvable disagreements mostly resulting from different annotation policies among repositories.

Database URL:http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads/ppiTrim.html

Exploring pathway interactions in insulin resistant mouse liver

BACKGROUND

Complex phenotypes such as insulin resistance involve different biological pathways that may interact and influence each other. Interpretation of related experimental data would be facilitated by identifying relevant pathway interactions in the context of the dataset.

RESULTS

We developed an analysis approach to study interactions between pathways by integrating gene and protein interaction networks, biological pathway information and high-throughput data. This approach was applied to a transcriptomics dataset to investigate pathway interactions in insulin resistant mouse liver in response to a glucose challenge. We identified regulated pathway interactions at different time points following the glucose challenge and also studied the underlying protein interactions to find possible mechanisms and key proteins involved in pathway cross-talk. A large number of pathway interactions were found for the comparison between the two diet groups at t = 0. The initial response to the glucose challenge (t = 0.6) was typed by an acute stress response and pathway interactions showed large overlap between the two diet groups, while the pathway interaction networks for the late response were more dissimilar.

CONCLUSIONS

Studying pathway interactions provides a new perspective on the data that complements established pathway analysis methods such as enrichment analysis. This study provided new insights in how interactions between pathways may be affected by insulin resistance. In addition, the analysis approach described here can be generally applied to different types of high-throughput data and will therefore be useful for analysis of other complex datasets as well.

Tutorial videos of bioinformatics resources: online distribution trial in Japan named TogoTV

In recent years, biological web resources such as databases and tools have become more complex because of the enormous amounts of data generated in the field of life sciences. Traditional methods of distributing tutorials include publishing textbooks and posting web documents, but these static contents cannot adequately describe recent dynamic web services. Due to improvements in computer technology, it is now possible to create dynamic content such as video with minimal effort and low cost on most modern computers. The ease of creating and distributing video tutorials instead of static content improves accessibility for researchers, annotators and curators. This article focuses on online video repositories for educational and tutorial videos provided by resource developers and users. It also describes a project in Japan named TogoTV (http://togotv.dbcls.jp/en/) and discusses the production and distribution of high-quality tutorial videos, which would be useful to viewer, with examples. This article intends to stimulate and encourage researchers who develop and use databases and tools to distribute how-to videos as a tool to enhance product usability.

The InterPro BioMart: federated query and web service access to the InterPro Resource

The InterPro BioMart provides users with query-optimized access to predictions of family classification, protein domains and functional sites, based on a broad spectrum of integrated computational models ('signatures') that are generated by the InterPro member databases: Gene3D, HAMAP, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. These predictions are provided for all protein sequences from both the UniProt Knowledge Base and the UniParc protein sequence archive. The InterPro BioMart is supplementary to the primary InterPro web interface (http://www.ebi.ac.uk/interpro), providing a web service and the ability to build complex, custom queries that can efficiently return thousands of rows of data in a variety of formats. This article describes the information available from the InterPro BioMart and illustrates its utility with examples of how to build queries that return useful biological information. Database URL: http://www.ebi.ac.uk/interpro/biomart/martview.

Ensembl BioMarts: a hub for data retrieval across taxonomic space

For a number of years the BioMart data warehousing system has proven to be a valuable resource for scientists seeking a fast and versatile means of accessing the growing volume of genomic data provided by the Ensembl project. The launch of the Ensembl Genomes project in 2009 complemented the Ensembl project by utilizing the same visualization, interactive and programming tools to provide users with a means for accessing genome data from a further five domains: protists, bacteria, metazoa, plants and fungi. The Ensembl and Ensembl Genomes BioMarts provide a point of access to the high-quality gene annotation, variation data, functional and regulatory annotation and evolutionary relationships from genomes spanning the taxonomic space. This article aims to give a comprehensive overview of the Ensembl and Ensembl Genomes BioMarts as well as some useful examples and a description of current data content and future objectives. Database URLs: http://www.ensembl.org/biomart/martview/; http://metazoa.ensembl.org/biomart/martview/; http://plants.ensembl.org/biomart/martview/; http://protists.ensembl.org/biomart/martview/; http://fungi.ensembl.org/biomart/martview/; http://bacteria.ensembl.org/biomart/martview/.

Systems-biology approaches to discover anti-viral effectors of the human innate immune response

Virus infections elicit an immediate innate response involving antiviral factors. The activities of some of these factors are, in turn, blocked by viral countermeasures. The ensuing battle between the host and the viruses is crucial for determining whether the virus establishes a foothold and/or induces adaptive immune responses. A comprehensive systems-level understanding of the repertoire of anti-viral effectors in the context of these immediate virus-host responses would provide significant advantages in devising novel strategies to interfere with the initial establishment of infections. Recent efforts to identify cellular factors in a comprehensive and unbiased manner, using genome-wide siRNA screens and other systems biology “omics” methodologies, have revealed several potential anti-viral effectors for viruses like Human immunodeficiency virus type 1 (HIV-1), Hepatitis C virus (HCV), West Nile virus (WNV), and influenza virus. This review describes the discovery of novel viral restriction factors and discusses how the integration of different methods in systems biology can be used to more comprehensively identify the intimate interactions of viruses and the cellular innate resistance.

Maps of open chromatin guide the functional follow-up of genome-wide association signals: application to hematological traits

Turning genetic discoveries identified in genome-wide association (GWA) studies into biological mechanisms is an important challenge in human genetics. Many GWA signals map outside exons, suggesting that the associated variants may lie within regulatory regions. We applied the formaldehyde-assisted isolation of regulatory elements (FAIRE) method in a megakaryocytic and an erythroblastoid cell line to map active regulatory elements at known loci associated with hematological quantitative traits, coronary artery disease, and myocardial infarction. We showed that the two cell types exhibit distinct patterns of open chromatin and that cell-specific open chromatin can guide the finding of functional variants. We identified an open chromatin region at chromosome 7q22.3 in megakaryocytes but not erythroblasts, which harbors the common non-coding sequence variant rs342293 known to be associated with platelet volume and function. Resequencing of this open chromatin region in 643 individuals provided strong evidence that rs342293 is the only putative causative variant in this region. We demonstrated that the C- and G-alleles differentially bind the transcription factor EVI1 affecting PIK3CG gene expression in platelets and macrophages. A protein–protein interaction network including up- and down-regulated genes in Pik3cg knockout mice indicated that PIK3CG is associated with gene pathways with an established role in platelet membrane biogenesis and thrombus formation. Thus, rs342293 is the functional common variant at this locus; to the best of our knowledge this is the first such variant to be elucidated among the known platelet quantitative trait loci (QTLs). Our data suggested a molecular mechanism by which a non-coding GWA index SNP modulates platelet phenotype.

Genome-wide scans have revealed multiple genetic regions underlying complex traits. However, the transition from an initial association signal to identifying the functional DNA change(s) has proved challenging. Many of the DNA changes discovered are located outside protein-coding regions and may exert their effects through gene regulation. We screened genetic regions associated with hematological traits in erythroblasts (red blood cells) and megakaryocytes (platelet-producing cells) and mapped sites of open chromatin, which harbor active gene regulatory elements. We investigated a DNA sequence change located within a site of open chromatin at chromosome 7 in megakaryocytes, but not erythroblasts, known to be associated with platelet volume. We showed that this DNA change is functional due to alteration of the binding site of a transcription factor, which regulates the expression of a gene that affects platelet characteristics. Mice lacking this gene revealed significant differences in expression of several important platelet genes compared to wild-type mice. The approach described here can be applied in different cell types to functionally follow-up association signals with many other biological traits by identification of the causative base change and how it affects gene function, thus paving the way to clinical benefit.

Multi-institutional prostate cancer study of genetic susceptibility in populations of African descent

Prostate cancer disparities have been reported in men of African descent who show the highest incidence, mortality, compared with other ethnic groups. Few studies have explored the genetic and environmental factors for prostate cancer in men of African ancestry. The glutathione-S-transferases family conjugates carcinogens before their excretion and is expressed in prostate tissue. This study addressed the role of GSTM1 and GSTT1 deletions on prostate cancer risk in populations of African descent. This multi-institutional case-control study gathered data from the Genetic Susceptibility to Environmental Carcinogens (GSEC) database, the African-Caribbean Cancer Consortium (AC3) and Men of African Descent and Carcinoma of the Prostate Consortium (MADCaP). The analysis included 10 studies (1715 cases and 2363 controls), five in African-Americans, three in African-Caribbean and two in African men. Both the GSTM1 and the GSTT1 deletions showed significant inverse associations with prostate cancer [odds ratio (OR): 0.90, 95% confidence interval (CI) 0.83-0.97 and OR 0.88, 95% CI: 0.82-0.96, respectively]. The association was restricted to Caribbean and African populations. A significant positive association was observed between GSTM1 deletion and prostate cancer in smokers in African-American studies (OR: 1.28, 95% CI: 1.01-1.56), whereas a reduced risk was observed in never-smokers (OR: 0.66, 95% CI: 0.46-0.95). The risk of prostate cancer increased across quartiles of pack-years among subjects carrying the deletion of GSTM1 but not among subjects carrying a functional GSTM1. Gene-environment interaction between smoking and GSTM1 may be involved in the etiology of prostate cancer in populations of African descent.

Published and perished? The influence of the searched protein database on the long-term storage of proteomics data

In proteomics, protein identifications are reported and stored using an unstable reference system: protein identifiers. These proprietary identifiers are created individually by every protein database and can change or may even be deleted over time.

To estimate the effect of the searched protein sequence database on the long-term storage of proteomics data we analyzed the changes of reported protein identifiers from all public experiments in the Proteomics Identifications (PRIDE) database by November 2010. To map the submitted protein identifier to a currently active entry, two distinct approaches were used. The first approach used the Protein Identifier Cross Referencing (PICR) service at the EBI, which maps protein identifiers based on 100% sequence identity. The second one (called logical mapping algorithm) accessed the source databases and retrieved the current status of the reported identifier.

Our analysis showed the differences between the main protein databases (International Protein Index (IPI), UniProt Knowledgebase (UniProtKB), National Center for Biotechnological Information nr database (NCBI nr), and Ensembl) in respect to identifier stability. For example, whereas 20% of submitted IPI entries were deleted after two years, virtually all UniProtKB entries remained either active or replaced. Furthermore, the two mapping algorithms produced markedly different results. For example, the PICR service reported 10% more IPI entries deleted compared with the logical mapping algorithm. We found several cases where experiments contained more than 10% deleted identifiers already at the time of publication. We also assessed the proportion of peptide identifications in these data sets that still fitted the originally identified protein sequences. Finally, we performed the same overall analysis on all records from IPI, Ensembl, and UniProtKB: two releases per year were used, from 2005. This analysis showed for the first time the true effect of changing protein identifiers on proteomics data. Based on these findings, UniProtKB seems the best database for applications that rely on the long-term storage of proteomics data.

Unraveling molecular signatures of immunostimulatory adjuvants in the female genital tract through systems biology

Sexually transmitted infections (STIs) unequivocally represent a major public health concern in both industrialized and developing countries. Previous efforts to develop vaccines for systemic immunization against a large number of STIs in humans have been unsuccessful. There is currently a drive to develop mucosal vaccines and adjuvants for delivery through the genital tract to confer protective immunity against STIs. Identification of molecular signatures that can be used as biomarkers for adjuvant potency can inform rational development of potent mucosal adjuvants. Here, we used systems biology to study global gene expression and signature molecules and pathways in the mouse vagina after treatment with two classes of experimental adjuvants. The Toll-like receptor 9 agonist CpG ODN and the invariant natural killer T cell agonist alpha-galactosylceramide, which we previously identified as equally potent vaginal adjuvants, were selected for this study. Our integrated analysis of genome-wide transcriptome data determined which signature pathways, processes and networks are shared by or otherwise exclusive to these 2 classes of experimental vaginal adjuvants in the mouse vagina. To our knowledge, this is the first integrated genome-wide transcriptome analysis of the effects of immunomodulatory adjuvants on the female genital tract of a mammal. These results could inform rational development of effective mucosal adjuvants for vaccination against STIs.

Experimental models for anxiolytic drug discovery in the era of omes and omics

INTRODUCTION

Animal behavioral models have become an indispensable tool for studying anxiety disorders and testing anxiety-modulating drugs. However, significant methodological and conceptual challenges affect the translational validity and accurate behavioral dissection in such models. They are also often limited to individual behavioral domains and fail to target the disorder's real clinical picture (its spectrum or overlap with other disorders), which hinder screening and development of novel anxiolytic drugs.

AREAS COVERED

In this article, the authors discuss and emphasize the importance of high-throughput multi-domain neurophenotyping based on the latest developments in video-tracking and bioinformatics. Additionally, the authors also explain how bioinformatics can provide new insight into the neural substrates of brain disorders and its benefit for drug discovery.

EXPERT OPINION

The throughput and utility of animal models of anxiety and other brain disorders can be markedly increased by a number of ways: i) analyzing systems of several domains and their interplay in a wider spectrum of model species; ii) using a larger number of end points generated by video-tracking tools; iii) correlating behavioral data with genomic, proteomic and other physiologically relevant markers using online databases and iv) creating molecular network-based models of anxiety to identify new targets for drug design and discovery. Experimental models utilizing bioinformatics tools and online databases will not only improve our understanding of both gene-behavior interactions and complex trait interconnectivity but also highlight new targets for novel anxiolytic drugs.

GLAMM: Genome-Linked Application for Metabolic Maps

The Genome-Linked Application for Metabolic Maps (GLAMM) is a unified web interface for visualizing metabolic networks, reconstructing metabolic networks from annotated genome data, visualizing experimental data in the context of metabolic networks and investigating the construction of novel, transgenic pathways. This simple, user-friendly interface is tightly integrated with the comparative genomics tools of MicrobesOnline [Dehal et al. (2010) Nucleic Acids Research, 38, D396-D400]. GLAMM is available for free to the scientific community at glamm.lbl.gov.

Data mining using the Catalogue of Somatic Mutations in Cancer BioMart

Catalogue of Somatic Mutations in Cancer (COSMIC) (http://www.sanger.ac.uk/cosmic) is a publicly available resource providing information on somatic mutations implicated in human cancer. Release v51 (January 2011) includes data from just over 19,000 genes, 161,787 coding mutations and 5573 gene fusions, described in more than 577,000 tumour samples. COSMICMart (COSMIC BioMart) provides a flexible way to mine these data and combine somatic mutations with other biological relevant data sets. This article describes the data available in COSMIC along with examples of how to successfully mine and integrate data sets using COSMICMart. DATABASE URL: http://www.sanger.ac.uk/genetics/CGP/cosmic/biomart/martview/.

A survey of metabolic databases emphasizing the MetaCyc family

Thanks to the confluence of genome sequencing and bioinformatics, the number of metabolic databases has expanded from a handful in the mid-1990s to several thousand today. These databases lie within distinct families that have common ancestry and common attributes. The main families are the MetaCyc, KEGG, Reactome, Model SEED, and BiGG families. We survey these database families, as well as important individual metabolic databases, including multiple human metabolic databases. The MetaCyc family is described in particular detail. It contains well over 1,000 databases, including highly curated databases for Escherichia coli, Saccharomyces cerevisiae, Mus musculus, and Arabidopsis thaliana. These databases are available through a number of web sites that offer a range of software tools for querying and visualizing metabolic networks. These web sites also provide multiple tools for analysis of gene expression and metabolomics data, including visualization of those datasets on metabolic network diagrams and over-representation analysis of gene sets and metabolite sets.

Regulatory mechanisms underlying C4 photosynthesis

C4 photosynthesis is an adaptation that evolved to alleviate the detrimental effects of photorespiration as a result of the gradual decline in atmospheric carbon dioxide levels. In most C4 plants, two cell types, bundle sheath and mesophyll, cooperate in carbon fixation, and, in so doing, are able to alleviate photorespiratory losses. Although much of the biochemistry is well characterized, little is known about the genetic mechanisms underlying the cell-type specificity driving C4 . However, several studies have shown that regulation acts at multiple levels, including transcriptional, post-transcriptional, post-translational and epigenetic. One example of such a regulatory mechanism is the cell-specific accumulation of major photorespiratory transcripts/proteins in bundle sheath cells, where ribulose-1,5-bisphosphate carboxylase/oxygenase is localized. Although many of the genes are expressed in the bundle sheath, some are expressed in both cell types, implicating post-transcriptional control mechanisms. Recently, ultra-high-throughput sequencing techniques and sophisticated mass spectrometry instrumentation have provided new opportunities to further our understanding of C4 regulation. Computational pipelines are being developed to accommodate the mass of data associated with these techniques. Finally, we discuss a readily transformable C4 grass--Setaria viridis--that has great potential to serve as a model for the genetic dissection of C4 photosynthesis in the grasses.