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Systematic determination of genetic network architecture

Systematic determination of genetic network architecture Technologies to measure whole-genome mRNA abundances 1,2,3 and methods to organize and display such data 4,5,6,7,8,9,10 are emerging as valuable tools for systems-level exploration of transcriptional regulatory networks. For instance, it has been shown that mRNA data from 118 genes, measured at several time points in the developing hindbrain of mice, can be hierarchically clustered into various patterns (or 'waves') whose members tend to participate in common processes 5 . We have previously shown that hierarchical clustering can group together genes whose cis-regulatory elements are bound by the same proteins in vivo 6 . Hierarchical clustering has also been used to organize genes into hierarchical dendograms on the basis of their expression across multiple growth conditions 7 . The application of Fourier analysis to synchronized yeast mRNA expression data has identified cell-cycle periodic genes, many of which have expected cis-regulatory elements 8 . Here we apply a systematic set of statistical algorithms, based on whole-genome mRNA data, partitional clustering and motif discovery, to identify transcriptional regulatory sub-networks in yeast—without any a priori knowledge of their structure or any assumptions about their dynamics. This approach uncovered new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. We used statistical characterization of known regulons and motifs to derive criteria by which we infer the biological significance of newly discovered regulons and motifs. Our approach holds promise for the rapid elucidation of genetic network architecture in sequenced organisms in which little biology is known. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nature Genetics Springer Journals

Systematic determination of genetic network architecture

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References (26)

Publisher
Springer Journals
Copyright
Copyright © 1999 by Nature America Inc.
Subject
Biomedicine; Biomedicine, general; Human Genetics; Cancer Research; Agriculture; Gene Function; Animal Genetics and Genomics
ISSN
1061-4036
eISSN
1546-1718
DOI
10.1038/10343
Publisher site
See Article on Publisher Site

Abstract

Technologies to measure whole-genome mRNA abundances 1,2,3 and methods to organize and display such data 4,5,6,7,8,9,10 are emerging as valuable tools for systems-level exploration of transcriptional regulatory networks. For instance, it has been shown that mRNA data from 118 genes, measured at several time points in the developing hindbrain of mice, can be hierarchically clustered into various patterns (or 'waves') whose members tend to participate in common processes 5 . We have previously shown that hierarchical clustering can group together genes whose cis-regulatory elements are bound by the same proteins in vivo 6 . Hierarchical clustering has also been used to organize genes into hierarchical dendograms on the basis of their expression across multiple growth conditions 7 . The application of Fourier analysis to synchronized yeast mRNA expression data has identified cell-cycle periodic genes, many of which have expected cis-regulatory elements 8 . Here we apply a systematic set of statistical algorithms, based on whole-genome mRNA data, partitional clustering and motif discovery, to identify transcriptional regulatory sub-networks in yeast—without any a priori knowledge of their structure or any assumptions about their dynamics. This approach uncovered new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. We used statistical characterization of known regulons and motifs to derive criteria by which we infer the biological significance of newly discovered regulons and motifs. Our approach holds promise for the rapid elucidation of genetic network architecture in sequenced organisms in which little biology is known.

Journal

Nature Genetics – Springer Journals

Published: Jul 1, 1999

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