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HOBACGEN: database system for comparative genomics in bacteria.

G Perrière1, L Duret, M Gouy

  • 1Laboratoire de Biométrie et Biologie Evolutive, Unité Mixte de Recherche Centre National de la Recherche Scientifique (UMR CNRS) n( degrees ). 5558, Université Claude Bernard-Lyon 1, 69622 Villeurbanne Cedex, France. perriere@biomserv.univ-lyon1.fr

Genome Research
|March 18, 2000
PubMed
Summary

HOBACGEN is a new bacterial comparative genomics database. It provides access to protein gene families, alignments, and phylogenetic trees, aiding in the interpretation of gene homology and orthologs/paralogs.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Microbial genetics

Background:

  • Comparative genomics facilitates understanding of gene function and evolution.
  • Databases are crucial for organizing and accessing large-scale genomic data.
  • Distinguishing orthologs from paralogs is fundamental in evolutionary studies.

Purpose of the Study:

  • To introduce HOBACGEN, a comprehensive database system for bacterial comparative genomics.
  • To provide tools for analyzing protein gene families, multiple alignments, and phylogenetic trees.
  • To facilitate the identification of homologous genes across different bacterial, archaeal, and yeast taxa.

Main Methods:

  • Integration of protein gene data from SWISS-PROT/TrEMBL for bacteria, archaea, and yeast.
  • Classification of genes into families with associated multiple alignments and phylogenetic trees.

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  • Development of a Java-based client-server architecture with a graphical user interface.

    • Main Results:

    • HOBACGEN offers a curated collection of bacterial, archaeal, and yeast protein genes organized into families.
    • The system provides visualization tools for multiple sequence alignments and phylogenetic trees.
    • Users can easily select homologous genes across specified taxa and access detailed sequence information.

    Conclusions:

    • HOBACGEN provides a user-friendly platform for comparative genomic analysis in bacteria.
    • The database simplifies the interpretation of homology relationships, aiding in ortholog and paralog identification.
    • The graphical interface enhances accessibility to complex genomic data for researchers.