archive-org.com » ORG » I » IMGS.ORG

Total: 854

Choose link from "Titles, links and description words view":

Or switch to "Titles and links view".
  • 18th International Mouse Genome Conference (2004)
    genetic architecture of morphological traits Aspects of genetic architecture include number and size of gene effects dominance epistasis and pleiotropy Pleiotropy is predicted to evolve so that functionally and developmentally related traits are influenced by the same loci When functional systems evolve in concert adaptive evolution is facilitated Thus the evolution of development is predicted to result in a nested hierarchy of gene effects reflecting the nested hierarchy of functional

    Original URL path: http://www.imgs.org/Archive/abstracts/2004abstracts/abs/file149.shtml (2016-02-17)
    Open archived version from archive


  • 18th International Mouse Genome Conference (2004)
    Informatics MGI Database is a public resource that provides curated and integrated information on the biology and genetics of the laboratory mouse With the recent 3 0 release MGI has radically improved support for sequence data Nucleotide and protein sequences are represented as distinct database objects rather than accession ID attributes of other objects The full power of relational integration now connects sequence information to the existing MGI integrated platform of curated biological domains such as genes gene expression gene function phenotypes strains mammalian orthology and chromosomal positioning and soon to come SNPs MGI now stores information for all mouse sequences from GenBank SWISS PROT LocusLink RefSeq Ensembl and NCBI gene models TIGR and NIA Mouse Gene Indices and DoTS Information for each sequence includes source attributes such as library strain tissue gender etc mapped to MGI controlled vocabularies whenever possible and sequence attributes such as type description provider length genome assembly coordinates for gene models etc Users can query for sequences using any or all of the biological domains mentioned above and get results back in a variety of formats HTML tab delimited FASTA An additional aspect of this implementation is a robust and uniform system for loading sequence

    Original URL path: http://www.imgs.org/Archive/abstracts/2004abstracts/abs/file150.shtml (2016-02-17)
    Open archived version from archive

  • 18th International Mouse Genome Conference (2004)
    SW Konagaya AK Hayashizaki YH Shiroishi TS RIKEN Genomic Sciences Center Yokohama Japan RIKEN Genomic Sciences Center has been producing a wide range of bio resources and comprehensive data collections from genomic level to phenomic level Now various sorts of large scale datasets are available for the estimation of responsible genes in the forward genetics approaches Here we introduce a web based system named Genome ó Phenome Superhighway GPS by which users can browse all the genes and the available bio resources that exist in a genetic mapped interval and can select promising candidate genes based on the available omic datasets such as literature based network LIBNET gene expressions READ transcript annotations FANTOM genetic maps TraitMap and other species syntenic information Thus GPS integrates the genome transcriptome proteome metabolome and phenome resources large scale ENU mutant mice and provides bioinformatics based strategies for the gene hunting By specifying a chromosomal interval and a keyword for example diabetes a user can obtain a ranking list of candidate genes from not only the diabetes related genes that reportedly involve in the disease but also from the diabetes associated genes that is reportedly associated with other known diabetes related genes Then the user

    Original URL path: http://www.imgs.org/Archive/abstracts/2004abstracts/abs/file151.shtml (2016-02-17)
    Open archived version from archive

  • 18th International Mouse Genome Conference (2004)
    42 000 known and predicted genes in the mouse draft genome sequence Here we compare our data to the Novartis Gene Atlas Su et al 2004 and the Riken Transcriptome Analysis Bono et al 2003 To allow uniform comparison we first mapped the array sequences from all three data sets to 34 343 MGI curated genes Genes in any one data set were retained only if a single MGI gene was detected unambiguously We then compared the datasets on the basis of their ability to support the prediction of the known functional roles of genes shared by all three datasets We used Support Vector Machines SVMs to learn the relationship between each gene s expression profile in each one of the datasets and that gene s MGI Gene Ontology Biological Process GO BP annotation s Typically SVMs are used to predict new functional roles here we use them to calculate the predictive value of each dataset for known functions on the basis of precision of predicted annotations that were confirmed by the annotation database and recall of confirmed annotations that were predicted When restricting profiles to the 14 tissues shared by all three datasets we found profiles drawn from our

    Original URL path: http://www.imgs.org/Archive/abstracts/2004abstracts/abs/file152.shtml (2016-02-17)
    Open archived version from archive

  • 18th International Mouse Genome Conference (2004)
    Wang J Williams RW University of Tennessee Health Science Center Memphis TN United States Heritable differences in transcribed RNA levels can be mapped as quantitative trait loci QTLs One source of data is microarray analysis of RNA transcripts in a set of recombinant inbred lines such as that available at the WebQTL Web site www webqtl org search html QTL Reaper is software for rapid detection of QTLs in large data sets It estimates empirical p values by permutation tests adapting the number of permutations to the significance of the QTL The resulting p values lend themselves to modern multiple test statistical methods which can define statistically significant sets of QTLs without excessive numbers of either false positives or false negatives Recent work with QTL Reaper has focused on improving methods for combining data from individual transcript specific oligonucleotide probes The use of recombinant inbred lines allows replicate measurements on genetically identical individuals and replicates allow an estimate of the heritability of expression measured by individual oligonucleotide probes This heritability varies greatly among probes even among those designed to measure the same transcript Since heritability is necessary but not sufficient to define a QTL we have tested heritability weighted averages

    Original URL path: http://www.imgs.org/Archive/abstracts/2004abstracts/abs/file153.shtml (2016-02-17)
    Open archived version from archive

  • 18th International Mouse Genome Conference (2004)
    MOUSE Begley DA Eppig JT Finger JH Hayamizu TF Hill DP Kadin JA McCright IJ Richardson JE Smith CM Ringwald M The Jackson Laboratory Bar Harbor United States The Gene Expression Database GXD collects and integrates gene expression information about the developing laboratory mouse By combining diverse types of expression data GXD provides information about the expression profiles of transcripts and proteins in different mouse strains and mutants thus enabling insights into the molecular networks underlying developmental and disease processes Expression patterns are described using an extensive dictionary of standardized anatomical terms making it possible to record expression results from assays with differing spatial resolution in a consistent manner GXD is integrated with the Mouse Genome Database and interconnected with other community resources to include expression data in a larger biological and analytical context Data is acquired by direct curation from the literature database downloads and electronic laboratory submissions To facilitate the direct submission of data to GXD we have developed the Gene Expression Notebook GEN GEN functions as a laboratory notebook to store and organize expression data assay details and images for in situ hybridization immunohistochemistry RT PCR and Northern and Western blot experiments It also allows the researcher

    Original URL path: http://www.imgs.org/Archive/abstracts/2004abstracts/abs/file154.shtml (2016-02-17)
    Open archived version from archive

  • 18th International Mouse Genome Conference (2004)
    to all organisms even as knowledge of gene and protein roles in cells is accumulating and changing GO provides three structured networks of defined terms to describe gene product attributes GO is one of the controlled vocabularies of the Open Biological Ontologies effort The Gene Ontology development effort focuses on four domains of molecular information molecular function biological process cellular component and sequence features Over 17 500 terms have been carefully defined and are represented in structured vocabularies that provide hierarchical representations of relationships between terms Research scientists are providing their expertise in particular sub specialties to help with the continued evaluation and update of GO subtrees Most Model Organism Databases including MGI and RGD are now actively annotating genes and gene products to the GO vocabularies Almost 100 000 genes from over 20 model organisms have been hand annotated to the GO and tens of thousands more have computational assignments to GO terms All GO associations are supported by evidence and citation information Current priorities include analysis of annotation consistency between contributing research groups In addition to developing the GO resource and supporting genome annotation groups the GO Consortium hosts Web and database resources to enable researchers to access

    Original URL path: http://www.imgs.org/Archive/abstracts/2004abstracts/abs/file155.shtml (2016-02-17)
    Open archived version from archive

  • 18th International Mouse Genome Conference (2004)
    SNP are assumed to underlie many of the differences between inbred mouse strains It is important to identify as many SNP as possible to facilitate functional and mapping studies Large scale SNP discovery projects are being undertaken by the Whitehead Institute and others by resequencing of shotgun genomic clones The SNP that emerge form these random sequencing programmes are principally in intergenic regions and will be important for mapping and haplotype identification However it is expected that relatively few of these SNP will be functional We have screened EST reads from public databases in order to discover SNP with a higher proportion of functional variants EST reads have been neglected as a source of candidate polymorphisms since there is only a single read associated with each sequence and hence data quality is uncertain We have developed a SNP discovery pipeline using PolyBayes Marth et al Nature Genetics 1999 452 456 to screen EST trace files for high quality variant base calls SNP that are assigned high probabilities by PolyBayes are passed to a script which retrieves metadata about the EST libraries in which the SNP was identified Another script identifies the genomic position of the SNP in Ensembl and retrieves

    Original URL path: http://www.imgs.org/Archive/abstracts/2004abstracts/abs/file156.shtml (2016-02-17)
    Open archived version from archive



  •