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

Lang Li*, Alfred S L Cheng, Victor X. Jin, Henry H. Paik, Meiyun Fan, Xiaoman Li, Wei Zhang, Jason Robarge, Curtis Balch, Ramana V. Davuluri, Sun Kim, Tim H M Huang, Kenneth P. Nephew

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

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 suggesTFBSs 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.

Original languageEnglish (US)
Pages (from-to)2210-2216
Number of pages7
JournalBioinformatics
Volume22
Issue number18
DOIs
StatePublished - Sep 15 2006

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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