Abstract

Yue Jiang¹, Bojan Cukic¹, Donald A. Adjeroh¹, Heath D. Skinner², Jie Lin¹, Qingxi J. Shen³ and Bing-Hua Jiang²

¹Lane Department of Computer Science and Electrical Engineering. ²Mary Babb Randolph Cancer Center, and Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 26506, U.S.A. ³Department of Biological Sciences, University of Nevada, Las Vegas, NV 89154, U.S.A.

Abstract

Efficient and effective analysis of the growing genomic databases requires the development of adequate computational tools. We introduce a fast method based on the suffix tree data structure for predicting novel targets of hypoxia-inducible factor 1 (HIF-1) from huge genome databases. The suffix tree data structure has two powerful applications here: one is to extract unknown patterns from multiple strings/sequences in linear time; the other is to search multiple strings/sequences using multiple patterns in linear time. Using 15 known HIF-1 target gene sequences as a training set, we extracted 105 common patterns that all occur in the 15 training genes using suffix trees. Using these 105 common patterns along with known subsequences surrounding HIF-1 binding sites from the literature, the algorithm searches a genome database that contains 2,078,786 DNA sequences. It reported 258 potentially novel HIF-1 targets including 25 known HIF-1 targets. Based on microarray studies from the literature, 17 putative genes were confirmed to be upregulated by HIF-1 or hypoxia inside these 258 genes. We further studied one of the potential targets, COX-2, in the biological lab; and showed that it was a biologically relevant HIF-1 target. These results demonstrate that our methodology is an effective computational approach for identifying novel HIF-1 targets.

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