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

Research output: Contribution to journalArticle

11 Citations (Scopus)

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
Externally publishedYes

Fingerprint

Estrogen Receptor
Transcription factors
Binding sites
Transcription Factor
Mixture Model
Promoter
Estrogen Receptors
Transcription Factors
Genes
Binding Sites
Model-based
Gene
Target
Pattern Recognition
Pattern recognition
Estrogens
Prediction

All Science Journal Classification (ASJC) codes

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

Cite this

A mixture model-based discriminate analysis for identifying ordered transcription factor binding site pairs in gene promoters directly regulated by estrogen receptor-α. / Li, Lang; Cheng, Alfred S.L.; Jin, Victor X.; Paik, Henry H.; Fan, Meiyun; Li, Xiaoman; Zhang, Wei; Robarge, Jason; Balch, Curtis; Davuluri, Ramana V.; Kim, Sun; Huang, Tim H.M.; Nephew, Kenneth P.

In: Bioinformatics, Vol. 22, No. 18, 15.09.2006, p. 2210-2216.

Research output: Contribution to journalArticle

Li, L, Cheng, ASL, Jin, VX, Paik, HH, Fan, M, Li, X, Zhang, W, Robarge, J, Balch, C, Davuluri, RV, Kim, S, Huang, THM & Nephew, KP 2006, 'A mixture model-based discriminate analysis for identifying ordered transcription factor binding site pairs in gene promoters directly regulated by estrogen receptor-α', Bioinformatics, vol. 22, no. 18, pp. 2210-2216. https://doi.org/10.1093/bioinformatics/btl329
Li, Lang ; Cheng, Alfred S.L. ; Jin, Victor X. ; Paik, Henry H. ; Fan, Meiyun ; Li, Xiaoman ; Zhang, Wei ; Robarge, Jason ; Balch, Curtis ; Davuluri, Ramana V. ; Kim, Sun ; Huang, Tim H.M. ; Nephew, Kenneth P. / A mixture model-based discriminate analysis for identifying ordered transcription factor binding site pairs in gene promoters directly regulated by estrogen receptor-α. In: Bioinformatics. 2006 ; Vol. 22, No. 18. pp. 2210-2216.
@article{7685a5253c6a42fe987e1c566492a872,
title = "A mixture model-based discriminate analysis for identifying ordered transcription factor binding site pairs in gene promoters directly regulated by estrogen receptor-α",
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.",
author = "Lang Li and Cheng, {Alfred S.L.} and Jin, {Victor X.} and Paik, {Henry H.} and Meiyun Fan and Xiaoman Li and Wei Zhang and Jason Robarge and Curtis Balch and Davuluri, {Ramana V.} and Sun Kim and Huang, {Tim H.M.} and Nephew, {Kenneth P.}",
year = "2006",
month = "9",
day = "15",
doi = "10.1093/bioinformatics/btl329",
language = "English (US)",
volume = "22",
pages = "2210--2216",
journal = "Bioinformatics",
issn = "1367-4803",
publisher = "Oxford University Press",
number = "18",

}

TY - JOUR

T1 - A mixture model-based discriminate analysis for identifying ordered transcription factor binding site pairs in gene promoters directly regulated by estrogen receptor-α

AU - Li, Lang

AU - Cheng, Alfred S.L.

AU - Jin, Victor X.

AU - Paik, Henry H.

AU - Fan, Meiyun

AU - Li, Xiaoman

AU - Zhang, Wei

AU - Robarge, Jason

AU - Balch, Curtis

AU - Davuluri, Ramana V.

AU - Kim, Sun

AU - Huang, Tim H.M.

AU - Nephew, Kenneth P.

PY - 2006/9/15

Y1 - 2006/9/15

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=33748692896&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33748692896&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btl329

DO - 10.1093/bioinformatics/btl329

M3 - Article

VL - 22

SP - 2210

EP - 2216

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 18

ER -