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

Motivation:Todetect and select patternsof transcription factor binding sites (TFBSs) which distinguish genes directly regulated by estrogen receptor-a (ERa), 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 ERa 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 ERa 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: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.