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Multi-species biclustering: An integrative method to identify functional gene conservation between multiple species

Posted on:2013-05-05Degree:Ph.DType:Dissertation
University:New York UniversityCandidate:Waltman, PeterFull Text:PDF
GTID:1450390008985854Subject:Biology
Abstract/Summary:
Background: Several recent comparative functional genomics projects have indicated that the co-regulation of many genes is conserved across species, at least in part. This suggests that comparative analysis of functional genomics data-sets could prove powerful in identifying co-regulated groups that are conserved across multiple species.;Results: We present recent work to extend our cMonkey algorithm to simultaneously bicluster heterogeneous data from multiple species to identify conserved modules of orthologous genes, which can yield evolutionary insights into the formation of regulatory modules. We also present results from the multi-species analysis to two triplets of bacteria. The first of these is a triplet of Gram-positive bacteria consisting of Bacillus subtilis, Bacillus anthracis, and Listeria monocytogenes, while the second is a triplet of Gram-negative bacteria that includes Escherichia coli, Salmonella typhimurium and Vibrio cholerae. Finally, we will present initial results from the multi-species biclustering analysis of human and mouse hematopoietic differentiation data.;Conclusion: Analysis of biclusters obtained revealed a surprising number of gene groups with conserved modularity and high biological significance as judged by several measures of cluster quality. We also highlight cases of interest from the Gram-positive triplet, including one that suggests a temporal difference in the expression of genes governing sporulation in the two Bacillus species. While analysis of the mouse and human hematopoietic differentiation is preliminary, it indicates the applicability of this analysis to eukaryotic systems, including comparison of cancer model systems. Finally, we suggest ways in which this analysis could be extended to identify divergent modules that may exist between normal and disease tissue.
Keywords/Search Tags:Species, Identify, Functional, Multiple, Conserved
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