An Algorithm for Identifying Novel Targets of Transcription Factor Families: Application to Hypoxia-inducible Factor 1 Targets

Citation data:

Cancer Informatics, Vol: 7, Page: 75-89

Publication Year:
2009
Usage 27
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Repository URL:
https://digitalscholarship.unlv.edu/ece_fac_articles/407; https://digitalscholarship.unlv.edu/cgi/viewcontent.cgi?article=1409&context=ece_fac_articles
Author(s):
Jiang, Yue; Cukic, Bojan; Adjeroh, Donald A.; Skinner, Heath D.; Lin, Jie; Shen, Qingxi J.; Jiang, Bing-Hua
Tags:
Computer algorithms; Gene mapping—Technique; Human genome—Research; Pattern recognition systems; Biology; Cell Biology; Electrical and Computer Engineering; Engineering; Genetics and Genomics; Immunology and Infectious Disease; Life Sciences; Microbiology
article description
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.