Finding regulatory modules through large-scale gene-expression data analysis

The use of gene microchips has enabled a rapid accumulation of gene-expression data. One of the major challenges of analyzing this data is the diversity, in both size and signal strength, of the various modules in the gene regulatory networks of organisms. Based on the Iterative Signature Algorithm...

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Bibliographic Details
Published inarXiv.org
Main Authors Kloster, Morten, Tang, Chao, Wingreen, Ned
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 19.01.2004
Subjects
Algorithms
Annotations
Binding sites
Data analysis
Gene expression
Identification methods
Modules
Signal strength
Yeast
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Summary:The use of gene microchips has enabled a rapid accumulation of gene-expression data. One of the major challenges of analyzing this data is the diversity, in both size and signal strength, of the various modules in the gene regulatory networks of organisms. Based on the Iterative Signature Algorithm [Bergmann, S., Ihmels, J. and Barkai, N. (2002) Phys. Rev. E 67, 031902], we present an algorithm - the Progressive Iterative Signature Algorithm (PISA) - that, by sequentially eliminating modules, allows unsupervised identification of both large and small regulatory modules. We applied PISA to a large set of yeast gene-expression data, and, using the Gene Ontology annotation database as a reference, found that our algorithm is much better able to identify regulatory modules than methods based on high-throughput transcription-factor binding experiments or on comparative genomics.
ISSN:2331-8422