The Mouse Genome Database (MGD): mouse biology and model systems

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

The Protein Naming Utility: a rules database for protein nomenclature

Generation of syntactically correct and unambiguous names for proteins is a challenging, yet vital task for functional annotation processes. Proteins are often named based on homology to known proteins, many of which have problematic names. To address the need to generate high-quality protein names, and capture our significant experience correcting protein names manually, we have developed the Protein Naming Utility (PNU, http://www.jcvi.org/pn-utility). The PNU is a web-based database for storing and applying naming rules to identify and correct syntactically incorrect protein names, or to replace synonyms with their preferred name. The PNU allows users to generate and manage collections of naming rules, optionally building upon the growing body of rules generated at the J. Craig Venter Institute (JCVI). Since communities often enforce disparate conventions for naming proteins, the PNU supports grouping rules into user-managed collections. Users can check their protein names against a selected PNU rule collection, generating both statistics and corrected names. The PNU can also be used to correct GenBank table files prior to submission to GenBank. Currently, the database features 3080 manual rules that have been entered by JCVI Bioinformatics Analysts as well as 7458 automatically imported names

IUPHAR-DB: new receptors and tools for easy searching and visualization of pharmacological data

The IUPHAR database is an established online reference resource for several important classes of human drug targets and related proteins. As well as providing recommended nomenclature, the database integrates information on the chemical, genetic, functional and pathophysiological properties of receptors and ion channels, curated and peer-reviewed from the biomedical literature by a network of experts. The database now includes information on 616 gene products from four superfamilies in human and rodent model organisms: G protein-coupled receptors, voltage- and ligand-gated ion channels and, in a recent update, 49 nuclear hormone receptors (NHRs). New data types for NHRs include details on co-regulators, DNA binding motifs, target genes and 3D structures. Other recent developments include curation of the chemical structures of approximately 2000 ligand molecules, providing electronic descriptors, identifiers, link-outs and calculated molecular properties, all available via enhanced ligand pages. The interface now provides intelligent tools for the visualization and exploration of ligand structure-activity relationships and the structural diversity of compounds active at each target. The database is freely available at http://www.iuphar-db.org

Genome-Wide Interrogation of Mammalian Stem Cell Fate Determinants by Nested Chromosome Deletions

Understanding the function of important DNA elements in mammalian stem cell genomes would be enhanced by the availability of deletion collections in which segmental haploidies are precisely characterized. Using a modified Cre-loxP–based system, we now report the creation and characterization of a collection of ∼1,300 independent embryonic stem cell (ESC) clones enriched for nested chromosomal deletions. Mapping experiments indicate that this collection spans over 25% of the mouse genome with good representative coverage of protein-coding genes, regulatory RNAs, and other non-coding sequences. This collection of clones was screened for in vitro defects in differentiation of ESC into embryoid bodies (EB). Several putative novel haploinsufficient regions, critical for EB development, were identified. Functional characterization of one of these regions, through BAC complementation, identified the ribosomal gene Rps14 as a novel haploinsufficient determinant of embryoid body formation. This new library of chromosomal deletions in ESC (DelES: http://bioinfo.iric.ca/deles) will serve as a unique resource for elucidation of novel protein-coding and non-coding regulators of ESC activity

Using Evolutionary Conserved Modules in Gene Networks as a Strategy to Leverage High Throughput Gene Expression Queries

Background: Large-scale gene expression studies have not yielded the expected insight into genetic networks that control complex processes. These anticipated discoveries have been limited not by technology, but by a lack of effective strategies to investigate the data in a manageable and meaningful way. Previous work suggests that using a pre-determined seednetwork of gene relationships to query large-scale expression datasets is an effective way to generate candidate genes for further study and network expansion or enrichment. Based on the evolutionary conservation of gene relationships, we test the hypothesis that a seed network derived from studies of retinal cell determination in the fly, Drosophila melanogaster, will be an effective way to identify novel candidate genes for their role in mouse retinal development. Methodology/Principal Findings: Our results demonstrate that a number of gene relationships regulating retinal cell differentiation in the fly are identifiable as pairwise correlations between genes from developing mouse retina. In addition, we demonstrate that our extracted seed-network of correlated mouse genes is an effective tool for querying datasets and provides a context to generate hypotheses. Our query identified 46 genes correlated with our extracted seed-network members. Approximately 54% of these candidates had been previously linked to the developing brain and 33% had been previously linked to the developing retina. Five of six candidate genes investigated further were validated by experiments examining spatial and temporal protein expression in the developing retina. Conclusions/Significance: We present an effective strategy for pursuing a systems biology approach that utilizes an evolutionary comparative framework between two model organisms, fly and mouse. Future implementation of this strategy will be useful to determine the extent of network conservation, not just gene conservation, between species and will facilitate the use of prior biological knowledge to develop rational systems-based hypotheses

A Gene Wiki for Community Annotation of Gene Function

This manuscript describes the creation of comprehensive gene wiki, seeded with data from public domain sources, which will enable and encourage community annotation of gene function

The Mouse Genome Database: enhancements and updates

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