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	Integrating multiple types of data to predict novel cell cycle-related genes
	Wang, Lin ; Hou, Lin ; Qian, Minping ; Li, Fangting ; Deng, Minghua
	2011
关键词	SACCHAROMYCES-CEREVISIAE REGULATORY ASSOCIATIONS YEAST TRANSCRIPTION IDENTIFICATION CYCLEBASE.ORG NETWORKS GENOME MCM1 CDK1
英文摘要	Background: Cellular functions depend on genetic, physical and other types of interactions. As such, derived interaction networks can be utilized to discover novel genes involved in specific biological processes. Epistatic Miniarray Profile, or E-MAP, which is an experimental platform that measures genetic interactions on a genome-wide scale, has successfully recovered known pathways and revealed novel protein complexes in Saccharomyces cerevisiae (budding yeast). Results: By combining E-MAP data with co-expression data, we first predicted a potential cell cycle related gene set. Using Gene Ontology (GO) function annotation as a benchmark, we demonstrated that the prediction by combining microarray and E-MAP data is generally >50% more accurate in identifying co-functional gene pairs than the prediction using either data source alone. We also used transcription factor (TF)-DNA binding data (Chip-chip) and protein phosphorylation data to construct a local cell cycle regulation network based on potential cell cycle related gene set we predicted. Finally, based on the E-MAP screening with 48 cell cycle genes crossing 1536 library strains, we predicted four unknown genes (YPL158C, YPR174C, YJR054W, and YPR045C) as potential cell cycle genes, and analyzed them in detail. Conclusion: By integrating E-MAP and DNA microarray data, potential cell cycle-related genes were detected in budding yeast. This integrative method significantly improves the reliability of identifying co-functional gene pairs. In addition, the reconstructed network sheds light on both the function of known and predicted genes in the cell cycle process. Finally, our strategy can be applied to other biological processes and species, given the availability of relevant data.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000297313700008&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Mathematical & Computational Biology; SCI(E); PubMed; 3; ARTICLE; Suppl 1; S9; 5
语种	英语
出处	PubMed ; SCI
出版者	bmc systems biology
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/193467]
专题	数学科学学院 物理学院
推荐引用方式 GB/T 7714	Wang, Lin,Hou, Lin,Qian, Minping,et al. Integrating multiple types of data to predict novel cell cycle-related genes. 2011-01-01.

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