Eppig, J. T. et al. The Mouse Genome Database (MGD): from genes to mice—a community resource for mouse biology. Nucleic Acids Res. 33, D471-D475

The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.
Nucleic Acids Research (Impact Factor: 9.11). 02/2005; 33(Database issue):D471-5. DOI: 10.1093/nar/gki113
Source: PubMed

ABSTRACT The Mouse Genome Database (MGD) forms the core of the Mouse Genome Informatics (MGI) system (, a model organism database resource for the laboratory mouse. MGD provides essential integration of experimental knowledge for the mouse system with information annotated from both literature and online sources. MGD curates and presents consensus and experimental data representations of genotype (sequence) through phenotype information, including highly detailed reports about genes and gene products. Primary foci of integration are through representations of relationships among 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 improvements in MGD discussed here include the enhancement of phenotype resources, the re-development of the International Mouse Strain Resource, IMSR, the update of mammalian orthology datasets and the electronic publication of classic books in mouse genetics.

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Available from: Judith A Blake, Aug 12, 2015
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    • "The sAC C1KO mice display male infertility (Esposito et al., 2004). Extensive phenotypic analysis revealed that female mice have increased circulating cholesterol and triglyceride and that both male and female mice have slightly elevated heart rates [as deposited in the Mouse Genome Database (Eppig et al., 2005)]. More recently, it was reported that an alternative start site upstream of exon 5 is used by somatic cells to produce yet another isoforms containing only one catalytic unit C2. "
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    ABSTRACT: The evolutionarily conserved soluble adenylyl cyclase (sAC, adcy10) was recently identified as a unique source of cAMP in the cytoplasm and the nucleus. Its activity is regulated by bicarbonate and fine-tuned by calcium. As such, and in conjunction with carbonic anhydrase (CA), sAC constitutes an HCO(-) 3/CO(-) 2/pH sensor. In both alpha-intercalated cells of the collecting duct and the clear cells of the epididymis, sAC is expressed at significant level and involved in pH homeostasis via apical recruitment of vacuolar H(+)-ATPase (VHA) in a PKA-dependent manner. In addition to maintenance of pH homeostasis, sAC is also involved in metabolic regulation such as coupling of Krebs cycle to oxidative phosphorylation via bicarbonate/CO2 sensing. Additionally, sAC also regulates CFTR channel and plays an important role in regulation of barrier function and apoptosis. These observations suggest that sAC, via bicarbonate-sensing, plays an important role in maintaining homeostatic status of cells against fluctuations in their microenvironment.
    Frontiers in Physiology 02/2014; 5:42. DOI:10.3389/fphys.2014.00042 · 3.50 Impact Factor
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    • "Filtering on these three criteria resulted in 548 INDELs that affect 394 human genes (Supplemental Table 5). To gain insight into the likely phenotypic consequences of these disruptions , we identified 101 genes from this set that are associated with targeted disruption phenotypes in the mouse (Supplemental Table 6; Eppig et al. 2005, 2007). Of these 101 experimentally disrupted genes, 84 (83%) yield an abnormal phenotype upon homozygous disruption in the mouse (Supplemental Table 6). "
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    ABSTRACT: Human genetic variation is expected to play a central role in personalized medicine. Yet only a fraction of the natural genetic variation that is harbored by humans has been discovered to date. Here we report almost 2 million small insertions and deletions (INDELs) that range from 1 bp to 10,000 bp in length in the genomes of 79 diverse humans. These variants include 819,363 small INDELs that map to human genes. Small INDELs frequently were found in the coding exons of these genes, and several lines of evidence indicate that such variation is a major determinant of human biological diversity. Microarray-based genotyping experiments revealed several interesting observations regarding the population genetics of small INDEL variation. For example, we found that many of our INDELs had high levels of linkage disequilibrium (LD) with both HapMap SNPs and with high-scoring SNPs from genome-wide association studies. Overall, our study indicates that small INDEL variation is likely to be a key factor underlying inherited traits and diseases in humans.
    Genome Research 04/2011; 21(6):830-9. DOI:10.1101/gr.115907.110 · 13.85 Impact Factor
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    • "A human gene was defined as " essential " if a knockout of its mouse ortholog confers lethality. To find human essential genes, we first extracted mouse essential genes from the Mouse Genome Informatics Database [11], and obtained 2,564 human essential genes through the human-mouse ortholog associations. Using gene symbol mapping we obtained 2,059 essential genes in the UniHI network, and finally used 1,759 of them after removing those that either have been used as drug targets or disease genes, or have some topological features unavailable. "
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    ABSTRACT: Identifying potential drug targets is a crucial task for drug discovery. Traditional in silico approaches utilize only pro-tein sequence or structural information to predict whether a protein can be a drug target, and achieve limited suc-cess. Since proteins function in the context of interaction networks by interacting with other cellular macromolecules, analysis of topological features of proteins in such networks can reveal important insights on whether a protein can be a potential drug target. In this paper, we introduced ten new topological features extracted from human protein in-teraction networks. When designing these new features, we specially emphasized the roles of three disease-related groups of proteins: known drug targets, disease genes, and essen-tial genes. Based on the topological feature set, we built supervised learning models using support vector machines, L1-regularized logistic regression, and k-nearest neighbors to predict whether testing proteins can be drug targets or not. We also analyzed the relevance of each feature to the probability of proteins being drug targets. We achieved up to 80% classification accuracy using tenfold cross validation, and yielded very stable results with a large number of ran-dom samplings. Our method can also be used to prioritize multiple candidate proteins according to their probability of being drug targets.
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