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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
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High-performance gene expression module analysis tool and its application to chemical toxicity data.

Wataru Fujibuchi1, Hyeryung Kim, Yoshifumi Okada

  • 1National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.

Methods in Molecular Biology (Clifton, N.J.)
|September 1, 2009
PubMed
Summary

This study introduces SAMURAI, a novel bioinformatics tool for gene clustering. SAMURAI efficiently identifies gene modules in large datasets, aiding in understanding toxicochemical effects on hepatocytes.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Toxicology

Background:

  • Gene clustering is crucial for analyzing gene function but faces challenges with large datasets.
  • Biclustering, which co-clusters genes and experiments, has emerged as a solution for large-scale gene expression data.
  • Interpreting results from traditional clustering becomes difficult with hundreds of samples.

Purpose of the Study:

  • To develop an efficient computational tool, SAMURAI, for detecting gene modules in large-scale gene expression data.
  • To create a comprehensive dictionary of gene modules using a chemical toxicity dataset.
  • To analyze these gene modules for understanding toxicochemical mechanisms.

Main Methods:

  • Developed SAMURAI, a program utilizing the Linear-time Closed itemset Miner (LCM) algorithm for ultrafast data mining.

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  • Applied SAMURAI to a chemical toxicity dataset from J&J rat liver experiments.
  • Compiled a dictionary of gene modules by querying datasets with individual chemical exposure experiments.

    • Main Results:

    • SAMURAI successfully detected gene modules with similar expression patterns to query profiles.
    • The analysis revealed that detected gene sets significantly represent Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.
    • Identified common reactions and mechanisms across different toxicochemical treatments of hepatocytes.

    Conclusions:

    • SAMURAI provides an efficient method for biclustering gene expression data.
    • The identified gene modules offer insights into the functional consequences of toxicochemical exposures.
    • This approach aids in unraveling molecular mechanisms underlying hepatocyte responses to various chemical treatments.