This article is part of the supplement: Selected articles from the 4th International Conference on Computational Systems Biology (ISB 2010)
Integrating multiple types of data to predict novel cell cycle-related genes
1 Center for Theoretical Biology, Peking University, Beijing 100871, China
2 LMAM, School of Mathematical Sciences, Peking University, Beijing 100871, China
3 Center for Statistical Science, Peking University, Beijing 100871, China
4 School of Physics, Peking University, Beijing 100871, China
BMC Systems Biology 2011, 5(Suppl 1):S9 doi:10.1186/1752-0509-5-S1-S9Published: 20 June 2011
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).
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.
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.