Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks [electronic resource]
Background: The learning of global genetic regulatory networks from expression data is aseverely under-constrained problem that is aided by reducing the dimensionality of the searchspace by means of clustering genes into putatively co-regulated groups, as opposed to those that aresimply co-expressed...
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| Format: | Electronic eBook |
| Language: | English |
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Washington, D.C. : Oak Ridge, Tenn. :
United States. Department of Energy. Office of Science ; Distributed by the Office of Scientific and Technical Information, U.S. Department of Energy,
2006.
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| Subjects: |
MARC
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| 245 | 0 | 0 | |a Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks |h [electronic resource] |
| 260 | |a Washington, D.C. : |b United States. Department of Energy. Office of Science ; |a Oak Ridge, Tenn. : |b Distributed by the Office of Scientific and Technical Information, U.S. Department of Energy, |c 2006. | ||
| 300 | |a Size: Article No. 280 : |b digital, PDF file. | ||
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| 500 | |a 01/01/2006. | ||
| 500 | |a "Journal ID: ISSN 1471-2105." | ||
| 500 | |a "Other: PII: 1471-2105-7-280." | ||
| 500 | |a Reiss, David J. ; Baliga, Nitin S. ; Bonneau, Richard ; | ||
| 520 | 3 | |a Background: The learning of global genetic regulatory networks from expression data is aseverely under-constrained problem that is aided by reducing the dimensionality of the searchspace by means of clustering genes into putatively co-regulated groups, as opposed to those that aresimply co-expressed. Be cause genes may be co-regulated only across a subset of all observedexperimental conditions, biclustering (clustering of genes and conditions) is more appropriate thanstandard clustering. Co-regulated genes are also often functionally (physically, spatially, genetically,and/or evolutionarily) associated, and such a priori known or pre-computed associations canprovide support for appropriately grouping genes. One important association is the presence ofone or more common cis-regulatory motifs. In organisms where these motifs are not known, theirde novo detection, integrated into the clustering algorithm, can help to guide the process towardsmore biologically parsimonious solutions.Results: We have developed an algorithm, cMonkey, that detects putative co-regulated genegroupings by integrating the biclustering of gene expression data and various functional associationswith the de novo detection of sequence motifs.Conclusion: We have applied this procedure to the archaeon Halobacterium NRC-1, as part ofour efforts to decipher its regulatory network. In addition, we used cMonkey on public data forthree organisms in the other two domains of life: Helicobacter pylori, Saccharomyces cerevisiae, andEscherichia coli. The biclusters detected by cMonkey both recapitulated known biology and enablednovel predictions (some for Halobacterium were subsequently confirmed in the laboratory). Forexample, it identified the bacteriorhodopsin regulon, assigned additional genes to this regulon withapparently unrelated function, and detected its known promoter motif. We have performed athorough comparison of cMonkey results against other clustering methods, and find that cMonkeybiclusters are more parsimonious with all available evidence for co-regulation. | |
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| 650 | 7 | |a Biotechnology & applied microbiology |2 local. | |
| 650 | 7 | |a Mathematical & computational biology |2 local. | |
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