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Comparative bioinformatics for mouse and human genes: getting started.

Douglas P Mortlock1

  • 1Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Current Protocols in Human Genetics
|April 23, 2008
PubMed
Summary
This summary is machine-generated.

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This guide explains how researchers can use bioinformatics tools to find mouse gene information, including genomic sequences and regulatory elements, for human gene orthologs.

Area of Science:

  • Bioinformatics and Genomics
  • Comparative Genomics
  • Mouse and Human Gene Analysis

Background:

  • Researchers often need to study mouse orthologs of human genes to understand gene function and regulation.
  • Identifying and analyzing homologous gene sequences across species is crucial for biological research.

Purpose of the Study:

  • To provide methods for utilizing online bioinformatics resources for mouse gene and genomic region identification.
  • To guide researchers in obtaining basic information on murine orthologs of human genes of interest.
  • To detail the retrieval of mouse genomic sequence, cDNA, exon/intron structure, and conserved noncoding regions.

Main Methods:

  • Utilizing Ensembl for mouse gene sequence retrieval (Basic Protocol 1).
  • Performing sequence homology searches with the UCSC genome browser to find mouse orthologs (Basic Protocol 2).

Related Experiment Videos

  • Employing PipMaker and VISTA for comparative analysis of large human and mouse genomic sequences (Basic Protocols 3 and 4).

    • Main Results:

    • Demonstrated successful retrieval of mouse gene sequences using Ensembl.
    • Showcased the identification of human gene orthologs in the mouse genome via UCSC genome browser searches.
    • Provided practical application of PipMaker and VISTA for cross-species genomic sequence comparison.

    Conclusions:

    • Online bioinformatics tools offer efficient methods for comparative gene analysis between human and mouse.
    • These protocols enable researchers to gather essential data on mouse orthologs for functional studies.
    • The described methods facilitate the identification of potential regulatory elements through conserved noncoding regions.