We have developed Mammalian Promoter Database (MPromDb), a novel database that integrates gene promoters with experimentally supported annotation of transcription start sites, cis-regulatory elements, CpG islands and chromatin immunoprecipitation microarray (ChIP-chip) experimental results with intuitively designed presentation. Release 1.0 of MPromDb currently contains 36 407 promoters and first exons (19 170 from human, 15 953 from mouse and 1284 from rat), 3739 transcription factor (TF)-binding sites (2027 from human, 1181 mouse and 531 rat) and 224 TFs with links to PubMed and GenBank references. Target promoters of TFs that have been identified by ChIP-chip assay are integrated into the database. MPromDb serves as a portal for genome-wide promoter analysis of data generated by ChIP-chip experimental studies. MPromDb can be accessed from .
The Affymetrix GeneChip is a widely used gene expression profiling platform. Since the chips were originally designed, the genome databases and gene definitions have been considerably updated. Thus, more accurate interpretation of microarray data requires parallel updating of the specificity of GeneChip probes. We propose a new probe remapping protocol, using the zebrafish GeneChips as an example, by removing nonspecific probes, and grouping the probes into transcript level probe sets using an integrated zebrafish genome annotation. This genome annotation is based on combining transcript information from multiple databases. This new remapping protocol, especially the new genome annotation, is shown here to be an important factor in improving the interpretation of gene expression microarray data.
Transcript data from the RefSeq, GenBank and Ensembl databases were downloaded from the UCSC genome browser, and integrated to generate a combined zebrafish genome annotation. Affymetrix probes were filtered and remapped according to the new annotation. The influence of transcript collection and gene definition methods was tested using two microarray data sets. Compared to remapping using a single database, this new remapping protocol results in up to 20% more probes being retained in the remapping, leading to approximately 1,000 more genes being detected. The differentially expressed gene lists are consequently increased by up to 30%. We are also able to detect up to three times more alternative splicing events. A small number of the bioinformatics predictions were confirmed using real-time PCR validation.
By combining gene definitions from multiple databases, it is possible to greatly increase the numbers of genes and splice variants that can be detected in microarray gene expression experiments.
The GenomeRNAi database (http://www.genomernai.org/) contains phenotypes from published cell-based RNA interference (RNAi) screens in Drosophila and Homo sapiens. The database connects observed phenotypes with annotations of targeted genes and information about the RNAi reagent used for the perturbation experiment. The availability of phenotypes from Drosophila and human screens also allows for phenotype searches across species. Besides reporting quantitative data from genome-scale screens, the new release of GenomeRNAi also enables reporting of data from microscopy experiments and curated phenotypes from published screens. In addition, the database provides an updated resource of RNAi reagents and their predicted quality that are available for the Drosophila and the human genome. The new version also facilitates the integration with other genomic data sets and contains expression profiling (RNA-Seq) data for several cell lines commonly used in RNAi experiments.
RegulonDB provides curated information on the transcriptional regulatory network of Escherichia coli and contains both experimental data and computationally predicted objects. To account for the heterogeneity of these data, we introduced in version 6.0, a two-tier rating system for the strength of evidence, classifying evidence as either ‘weak’ or ‘strong’ (Gama-Castro,S., Jimenez-Jacinto,V., Peralta-Gil,M. et al. RegulonDB (Version 6.0): gene regulation model of Escherichia Coli K-12 beyond transcription, active (experimental) annotated promoters and textpresso navigation. Nucleic Acids Res., 2008;36:D120–D124.). We now add to our classification scheme the classification of high-throughput evidence, including chromatin immunoprecipitation (ChIP) and RNA-seq technologies. To integrate these data into RegulonDB, we present two strategies for the evaluation of confidence, statistical validation and independent cross-validation. Statistical validation involves verification of ChIP data for transcription factor-binding sites, using tools for motif discovery and quality assessment of the discovered matrices. Independent cross-validation combines independent evidence with the intention to mutually exclude false positives. Both statistical validation and cross-validation allow to upgrade subsets of data that are supported by weak evidence to a higher confidence level. Likewise, cross-validation of strong confidence data extends our two-tier rating system to a three-tier system by introducing a third confidence score ‘confirmed’.
The Mouse Genome Informatics (MGI; http://www.informatics.jax.org/) database integrates genetic and genomic data with the primary mission of facilitating the use of the mouse as a model system for understanding human biology and disease processes. MGI is the authoritative source of official mouse genetic nomenclature, gene ontology annotations, mammalian phenotype annotations, and mouse anatomy terms. MGI staff enforce the use of standardized genetic nomenclature, ontologies, and controlled vocabularies to describe mouse sequence data, genes, strains, expression data, alleles, and phenotypes. Extensive links between gene-centric information in MGI and other informatics resources (e.g., OMIM, Ensembl, UCSC, NCBI, UniProt) are maintained and updated on a regular basis.
Using the Web-based query interfaces for MGI, users can query for a mouse gene or genes according to diverse biological attributes of those genes, including phenotype associations, gene expression, functional annotation, and genome location. The MGI MouseBLAST server allows users to interrogate the MGI database using nucleotide and/or protein sequences. Functional and phenotypic data from MGI can be viewed in a broader genomic context using an interactive genome browser called Mouse GBrowse. The power of the MGI database as a research tool for biomedicine stems from the degree to which data from diverse sources are integrated. Integration, in turn, allows the data to be evaluated in new contexts. For example, integration makes possible such complex queries as “Find all genes from Chromosome 1 where the function is annotated as transcription factor and there is a knockout allele that results in eye dysmorphology.”
The Mouse Genome Database (MGD) (http://www.informatics.jax.org) one component of a community database resource for the laboratory mouse, a key model organism for interpreting the human genome and for understanding human biology. MGD strives to provide an extensively integrated information resource with experimental details annotated from both literature and on-line genomic data sources. MGD curates and presents the consensus representation of genotype (sequence) to phenotype information including highly detailed information about genes and gene products. Primary foci of integration are through representations of relationships between genes, sequences and phenotypes. MGD collaborates with other bioinformatics groups to curate a definitive set of information about the laboratory mouse. Recent developments include a general implementation of database structures for controlled vocabularies and the integration of a phenotype classification system.
The Mouse Genome Database (MGD) is a major component of the Mouse Genome Informatics (MGI, http://www.informatics.jax.org/) database resource and serves as the primary community model organism database for the laboratory mouse. MGD is the authoritative source for mouse gene, allele and strain nomenclature and for phenotype and functional annotations of mouse genes. MGD contains comprehensive data and information related to mouse genes and their functions, standardized descriptions of mouse phenotypes, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information including comparative data on mammalian genes. Data for MGD are obtained from diverse sources including manual curation of the biomedical literature and direct contributions from individual investigator’s laboratories and major informatics resource centers, such as Ensembl, UniProt and NCBI. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology and the Mammalian Phenotype Ontology. Recent improvements in MGD described here includes integration of mouse gene trap allele and sequence data, integration of gene targeting information from the International Knockout Mouse Consortium, deployment of an MGI Biomart, and enhancements to our batch query capability for customized data access and retrieval.
Summary: Transcription and chromatin regulators, and histone modifications play essential roles in gene expression regulation. We have created CistromeMap as a web server to provide a comprehensive knowledgebase of all of the publicly available ChIP-Seq and DNase-Seq data in mouse and human. We have also manually curated metadata to ensure annotation consistency, and developed a user-friendly display matrix for quick navigation and retrieval of data for specific factors, cells and papers. Finally, we provide users with summary statistics of ChIP-Seq and DNase-Seq studies.
Availability: Freely available on the web at http://cistrome.dfci.harvard.edu/pc/
The Mouse Genome Database (MGD) is the community database resource
for the laboratory mouse, a key model organism for interpreting
the human genome and for understanding human biology and disease (http://www.informatics.jax.org).
MGD provides standard nomenclature and consensus map positions for
mouse genes and genetic markers; it provides a curated set of mammalian
homology records, user-defined chromosomal maps, experimental data
sets and the definitive mouse ‘gene to sequence’ reference set
for the research community. The integration and standardization
of these data sets facilitates the transition between mouse DNA
sequence, gene and phenotype annotations. A recent focus on allele
and phenotype representations enhances the ability of MGD to organize
and present data for exploring the relationship between genotype
and phenotype. This link between the genome and the biology of the mouse
is especially important as phenotype information grows from large
mutagenesis projects and genotype information grows from large-scale
The Mouse Genome Database (MGD) is one component of the Mouse Genome Informatics (MGI) system (http://www.informatics.jax.org), a community database resource for the laboratory mouse. MGD strives to provide a comprehensive knowledgebase about the mouse with experiments and data annotated from both literature and online sources. MGD curates and presents consensus and experimental data representations of genetic, genotype (sequence) and phenotype information including highly detailed reports about genes and gene products. Primary foci of integration are through representations of relationships between genes, sequences and phenotypes. MGD collaborates with other bioinformatics groups to curate a definitive set of information about the laboratory mouse and to build and implement the data and semantic standards that are essential for comparative genome analysis. Recent developments in MGD discussed here include an extensive integration of the mouse sequence data and substantial revisions in the presentation, query and visualization of sequence data.
NCBI's Reference Sequence (RefSeq) database (http://www.ncbi.nlm.nih.gov/RefSeq/) is a curated non-redundant collection of sequences representing genomes, transcripts and proteins. RefSeq records integrate information from multiple sources and represent a current description of the sequence, the gene and sequence features. The database includes over 5300 organisms spanning prokaryotes, eukaryotes and viruses, with records for more than 5.5 × 106 proteins (RefSeq release 30). Feature annotation is applied by a combination of curation, collaboration, propagation from other sources and computation. We report here on the recent growth of the database, recent changes to feature annotations and record types for eukaryotic (primarily vertebrate) species and policies regarding species inclusion and genome annotation. In addition, we introduce RefSeqGene, a new initiative to support reporting variation data on a stable genomic coordinate system.
The Mouse Genome Database (MGD) is the community database resource for the laboratory mouse, a key model organism for interpreting the human genome and for understanding human biology and disease (http://www.informatics.jax.org). MGD strives to provide a highly curated, highly integrated information resource that not only includes the consensus view of current knowledge about the mouse, but also provides comparative genomic information particularly for human and rat genomes. MGD includes extensive information about mouse genes, supporting all gene attribute assertions with experimental data, statements of evidence and citation. Detailed information about alleles and mouse mutants includes genotype, molecular variant and phenotype descriptions. Extensive collaboration with other data providers such as NCBI, RIKEN and SWISS-PROT provides standardization of gene:sequence associations and robust interconnections between large information systems based on shared sequence curation. Recent integration of large datasets of mouse full-length cDNAs and radiation-hybrid mapped ESTs, the continued development and use of extensive structured vocabularies and the expansion of the representation of phenotypes highlight this year’s developments.
NCBI's reference sequence (RefSeq) database () is a curated non-redundant collection of sequences representing genomes, transcripts and proteins. The database includes 3774 organisms spanning prokaryotes, eukaryotes and viruses, and has records for 2 879 860 proteins (RefSeq release 19). RefSeq records integrate information from multiple sources, when additional data are available from those sources and therefore represent a current description of the sequence and its features. Annotations include coding regions, conserved domains, tRNAs, sequence tagged sites (STS), variation, references, gene and protein product names, and database cross-references. Sequence is reviewed and features are added using a combined approach of collaboration and other input from the scientific community, prediction, propagation from GenBank and curation by NCBI staff. The format of all RefSeq records is validated, and an increasing number of tests are being applied to evaluate the quality of sequence and annotation, especially in the context of complete genomic sequence.
A database of coexpressed gene sets can provide valuable information for a wide variety of experimental designs, such as targeting of genes for functional identification, gene regulation and/or protein–protein interactions. Coexpressed gene databases derived from publicly available GeneChip data are widely used in Arabidopsis research, but platforms that examine coexpression for higher mammals are rather limited. Therefore, we have constructed a new database, COXPRESdb (coexpressed gene database) (http://coxpresdb.hgc.jp), for coexpressed gene lists and networks in human and mouse. Coexpression data could be calculated for 19 777 and 21 036 genes in human and mouse, respectively, by using the GeneChip data in NCBI GEO. COXPRESdb enables analysis of the four types of coexpression networks: (i) highly coexpressed genes for every gene, (ii) genes with the same GO annotation, (iii) genes expressed in the same tissue and (iv) user-defined gene sets. When the networks became too big for the static picture on the web in GO networks or in tissue networks, we used Google Maps API to visualize them interactively. COXPRESdb also provides a view to compare the human and mouse coexpression patterns to estimate the conservation between the two species.
The Gene3D structural domain database provides domain annotations for 7 million proteins, based on the manually curated structural domain superfamilies in CATH. These annotations are integrated with functional, genomic and molecular information from external resources, such as GO, EC, UniProt and the NCBI Taxonomy database. We have constructed a set of web services that provide programmatic access to this integrated database, as well as the Gene3D domain recognition tool (Gene3DScan) and protein sequence annotation pipeline for analysing novel protein sequences. Example queries include retrieving all curated GO terms for a domain superfamily or all the multi-domain architectures for the human genome. The services can be accessed using simple HTTP calls and are able to return results in a range of formats for quick downloading and easy parsing, graphical rendering and data storage. Hence, they provide a simple, but flexible means of integrating domain annotations and associated data sets into locally run pipelines and analysis software. The services can be found at http://gene3d.biochem.ucl.ac.uk/WebServices/.
The Mouse Genome Database, the Gene Expression Database and the Mouse Tumor Biology database are integrated components of the Mouse Genome Informatics (MGI) resource (http://www.informatics.jax.org). The MGI system presents both a consensus view and an experimental view of the knowledge concerning the genetics and genomics of the laboratory mouse. From genotype to phenotype, this information resource integrates information about genes, sequences, maps, expression analyses, alleles, strains and mutant phenotypes. Comparative mammalian data are also presented particularly in regards to the use of the mouse as a model for the investigation of molecular and genetic components of human diseases. These data are collected from literature curation as well as downloads of large datasets (SwissProt, LocusLink, etc.). MGI is one of the founding members of the Gene Ontology (GO) and uses the GO for functional annotation of genes. Here, we discuss the workflow associated with manual GO annotation at MGI, from literature collection to display of the annotations. Peer-reviewed literature is collected mostly from a set of journals available electronically. Selected articles are entered into a master bibliography and indexed to one of eight areas of interest such as ‘GO’ or ‘homology’ or ‘phenotype’. Each article is then either indexed to a gene already contained in the database or funneled through a separate nomenclature database to add genes. The master bibliography and associated indexing provide information for various curator-reports such as ‘papers selected for GO that refer to genes with NO GO annotation’. Once indexed, curators who have expertise in appropriate disciplines enter pertinent information. MGI makes use of several controlled vocabularies that ensure uniform data encoding, enable robust analysis and support the construction of complex queries. These vocabularies range from pick-lists to structured vocabularies such as the GO. All data associations are supported with statements of evidence as well as access to source publications.
The Fungal Secretome KnowledgeBase (FunSecKB) provides a resource of secreted fungal proteins, i.e. secretomes, identified from all available fungal protein data in the NCBI RefSeq database. The secreted proteins were identified using a well evaluated computational protocol which includes SignalP, WolfPsort and Phobius for signal peptide or subcellular location prediction, TMHMM for identifying membrane proteins, and PS-Scan for identifying endoplasmic reticulum (ER) target proteins. The entries were mapped to the UniProt database and any annotations of subcellular locations that were either manually curated or computationally predicted were included in FunSecKB. Using a web-based user interface, the database is searchable, browsable and downloadable by using NCBI’s RefSeq accession or gi number, UniProt accession number, keyword or by species. A BLAST utility was integrated to allow users to query the database by sequence similarity. A user submission tool was implemented to support community annotation of subcellular locations of fungal proteins. With the complete fungal data from RefSeq and associated web-based tools, FunSecKB will be a valuable resource for exploring the potential applications of fungal secreted proteins.
Database URL: http://proteomics.ysu.edu/secretomes/fungi.php
The Mouse Genome Database (MGD, http://www.informatics.jax.org) is the international community resource for integrated genetic, genomic and biological data about the laboratory mouse. Data in MGD are obtained through loads from major data providers and experimental consortia, electronic submissions from laboratories and from the biomedical literature. MGD maintains a comprehensive, unified, non-redundant catalog of mouse genome features generated by distilling gene predictions from NCBI, Ensembl and VEGA. MGD serves as the authoritative source for the nomenclature of mouse genes, mutations, alleles and strains. MGD is the primary source for evidence-supported functional annotations for mouse genes and gene products using the Gene Ontology (GO). MGD provides full annotation of phenotypes and human disease associations for mouse models (genotypes) using terms from the Mammalian Phenotype Ontology and disease names from the Online Mendelian Inheritance in Man (OMIM) resource. MGD is freely accessible online through our website, where users can browse and search interactively, access data in bulk using Batch Query or BioMart, download data files or use our web services Application Programming Interface (API). Improvements to MGD include expanded genome feature classifications, inclusion of new mutant allele sets and phenotype associations and extensions of GO to include new relationships and a new stream of annotations via phylogenetic-based approaches.
As part of the development of the database Bgee (a dataBase for Gene Expression Evolution), we annotate and analyse expression data from different types and different sources, notably Affymetrix data from GEO and ArrayExpress, and RNA-Seq data from SRA. During our quality control procedure, we have identified duplicated content in GEO and ArrayExpress, affecting ∼14% of our data: fully or partially duplicated experiments from independent data submissions, Affymetrix chips reused in several experiments, or reused within an experiment. We present here the procedure that we have established to filter such duplicates from Affymetrix data, and our procedure to identify future potential duplicates in RNA-Seq data.
The Mouse Genome Database, (MGD, http://www.informatics.jax.org/), integrates genetic, genomic and phenotypic information about the laboratory mouse, a primary animal model for studying human biology and disease. MGD data content includes comprehensive characterization of genes and their functions, standardized descriptions of mouse phenotypes, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information including comparative data on mammalian genes. Data within MGD are obtained from diverse sources including manual curation of the biomedical literature, direct contributions from individual investigator's laboratories and major informatics resource centers such as Ensembl, UniProt and NCBI. MGD collaborates with the bioinformatics community on the development of data and semantic standards such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology. MGD provides a data-mining platform that enables the development of translational research hypotheses based on comparative genotype, phenotype and functional analyses. Both web-based querying and computational access to data are provided. Recent improvements in MGD described here include the association of gene trap data with mouse genes and a new batch query capability for customized data access and retrieval.
Alternative promoters that are differentially used in various cellular contexts and tissue types add to the transcriptional complexity in mammalian genome. Identification of alternative promoters and the annotation of their activity in different tissues is one of the major challenges in understanding the transcriptional regulation of the mammalian genes and their isoforms. To determine the use of alternative promoters in different tissues, we performed ChIP-seq experiments using antibody against RNA Pol-II, in five adult mouse tissues (brain, liver, lung, spleen and kidney). Our analysis identified 38 639 Pol-II promoters, including 12 270 novel promoters, for both protein coding and non-coding mouse genes. Of these, 6384 promoters are tissue specific which are CpG poor and we find that only 34% of the novel promoters are located in CpG-rich regions, suggesting that novel promoters are mostly tissue specific. By identifying the Pol-II bound promoter(s) of each annotated gene in a given tissue, we found that 37% of the protein coding genes use alternative promoters in the five mouse tissues. The promoter annotations and ChIP-seq data presented here will aid ongoing efforts of characterizing gene regulatory regions in mammalian genomes.
In addition to maintaining the GenBank® nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval and resources that operate on the data in GenBank and a variety of other biological data made available through NCBI’s Web site. NCBI data retrieval resources include Entrez, PubMed, LocusLink and the Taxonomy Browser. Data analysis resources include BLAST, Electronic PCR, OrfFinder, RefSeq, UniGene, Database of Single Nucleotide Polymorphisms (dbSNP), Human Genome Sequencing pages, GeneMap’99, Davis Human–Mouse Homology Map, Cancer Chromosome Aberration Project (CCAP) pages, Entrez Genomes, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, Cancer Genome Anatomy Project (CGAP) pages, SAGEmap, Online Mendelian Inheritance in Man (OMIM) and the Molecular Modeling Database (MMDB). Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi.nlm.nih.gov
In addition to maintaining the GenBank® nucleic acid sequence
database, the National Center for Biotechnology Information (NCBI)
provides data analysis and retrieval resources that operate on the
data in GenBank and a variety of other biological data made available
through NCBI’s Web site. NCBI data retrieval resources
include Entrez, PubMed, LocusLink and the Taxonomy Browser. Data
analysis resources include BLAST, Electronic PCR, OrfFinder, RefSeq,
UniGene, HomoloGene, Database of Single Nucleotide Polymorphisms
(dbSNP), Human Genome Sequencing, Human MapViewer, GeneMap’99,
Human–Mouse Homology Map, Cancer Chromosome Aberration
Project (CCAP), Entrez Genomes, Clusters of Orthologous Groups (COGs) database,
Retroviral Genotyping Tools, Cancer Genome Anatomy Project (CGAP),
SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance
in Man (OMIM), the Molecular Modeling Database (MMDB) and the Conserved
Domain Database (CDD). Augmenting many of the Web applications are
custom implementations of the BLAST program optimized to search
specialized data sets. All of the resources can be accessed through
the NCBI home page at: http://www.ncbi.nlm.nih.gov.
Motivation: Experiments such as ChIP-chip, ChIP-seq, ChIP-PET and DamID (the four methods referred herein as ChIP-X) are used to profile the binding of transcription factors to DNA at a genome-wide scale. Such experiments provide hundreds to thousands of potential binding sites for a given transcription factor in proximity to gene coding regions.
Results: In order to integrate data from such studies and utilize it for further biological discovery, we collected interactions from such experiments to construct a mammalian ChIP-X database. The database contains 189 933 interactions, manually extracted from 87 publications, describing the binding of 92 transcription factors to 31 932 target genes. We used the database to analyze mRNA expression data where we perform gene-list enrichment analysis using the ChIP-X database as the prior biological knowledge gene-list library. The system is delivered as a web-based interactive application called ChIP Enrichment Analysis (ChEA). With ChEA, users can input lists of mammalian gene symbols for which the program computes over-representation of transcription factor targets from the ChIP-X database. The ChEA database allowed us to reconstruct an initial network of transcription factors connected based on shared overlapping targets and binding site proximity. To demonstrate the utility of ChEA we present three case studies. We show how by combining the Connectivity Map (CMAP) with ChEA, we can rank pairs of compounds to be used to target specific transcription factor activity in cancer cells.
Availability: The ChEA software and ChIP-X database is freely available online at: http://amp.pharm.mssm.edu/lib/chea.jsp
Supplementary information: Supplementary data are available at Bioinformatics online.
High-occupancy target (HOT) regions are compact genome loci occupied by many different transcription factors (TFs). HOT regions were initially defined in invertebrate model organisms, and we here show that they are a ubiquitous feature of the human gene-regulation landscape.
We identified HOT regions by a comprehensive analysis of ChIP-seq data from 96 DNA-associated proteins in 5 human cell lines. Most HOT regions co-localize with RNA polymerase II binding sites, but many are not near the promoters of annotated genes. At HOT promoters, TF occupancy is strongly predictive of transcription preinitiation complex recruitment and moderately predictive of initiating Pol II recruitment, but only weakly predictive of elongating Pol II and RNA transcript abundance. TF occupancy varies quantitatively within human HOT regions; we used this variation to discover novel associations between TFs. The sequence motif associated with any given TF’s direct DNA binding is somewhat predictive of its empirical occupancy, but a great deal of occupancy occurs at sites without the TF’s motif, implying indirect recruitment by another TF whose motif is present.
Mammalian HOT regions are regulatory hubs that integrate the signals from diverse regulatory pathways to quantitatively tune the promoter for RNA polymerase II recruitment.
Transcription factor; ChIP-seq; HOT region; Gene regulation