A mixture model-based discriminate analysis for identifying ordered transcription factor binding site pairs in gene promoters directly regulated by estrogen receptor-[alpha]

• Published in: Bioinformatics (Oxford, England) Vol. 22; no. 18; p. 2210
• Main Authors: Lang, Li, Cheng, Alfred S L, Jin, Victor X, Paik, Henry H, Fan, Meiyun, Li, Xiaoman, Zhang, Wei, Robarge, Jason, Balch, Curtis, Davuluri, Ramana V, Sun, Kim, Tim H.-M. Huang, Nephew, Kenneth P
• Format: Journal Article
• Language: English
• Published: Oxford Oxford Publishing Limited (England) 15.09.2006
• ISSN: 1367-4803; 1367-4811

Motivation: To detect and select patterns of transcription factor binding sites (TFBSs) which distinguish genes directly regulated by estrogen receptor-α (ERα), we developed an innovative mixture model-based discriminate analysis for identifying ordered TFBS pairs. Results: Biologically, our proposed new algorithm clearly suggests that TFBSs are not randomly distributed within ERα target promoters (P-value < 0.001). The up-regulated targets significantly (P-value < 0.01) possess TFBS pairs, (DBP, MYC), (DBP, MYC/MAX heterodimer), (DBP, USF2) and (DBP, MYOGENIN); and down-regulated ERα target genes significantly (P-value < 0.01) possess TFBS pairs, such as (DBP, c-ETS1-68), (DBP, USF2) and (DBP, MYOGENIN). Statistically, our proposed mixture model-based discriminate analysis can simultaneously perform TFBS pattern recognition, TFBS pattern selection, and target class prediction; such integrative power cannot be achieved by current methods. Availability: The software is available on request from the authors. Contact: lali@iupui.edu Supplementary information: Supplementary data are available at Bioinformatics online.