期刊
NUCLEIC ACIDS RESEARCH
卷 32, 期 1, 页码 179-188出版社
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkh183
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资金
- NATIONAL CANCER INSTITUTE [R01CA081157] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [P20GM066401] Funding Source: NIH RePORTER
- NCI NIH HHS [R01-CA81157, R01 CA081157-05, R01 CA081157] Funding Source: Medline
- NIGMS NIH HHS [P20 GM066401, 1P20GM066401-01, P20 GM66401] Funding Source: Medline
We have developed a computational method for transcriptional regulatory network inference, CARRIE (Computational Ascertainment of Regu latory Relationships Inferred from Expression), which combines microarray and promoter sequence analysis. CARRIE uses sources of data to identify the transcription factors (TFs) that regulate gene expression changes in response to a stimulus and generates testable hypotheses about the regulatory network connecting these TFs to the genes they regulate. The promoter analysis component of CARRIE, ROVER (Relative OVER-abundance of cis-elements), is highly accurate at detecting the TFs that regulate the response to a stimulus. ROVER also predicts which genes are regulated by each of these TFs. CARRIE uses these transcriptional interactions to infer a regulatory network. To demonstrate our method, we applied CARRIE to six sets of publicly available DNA microarray experiments on Saccharomyces cerevisiae. The predicted networks were validated with comparisons to literature sources, experimental TF binding data, and gene ontology biological process information.
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