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

doi:10.1093/bioinformatics/btl329

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 journal › Article › peer-review

12 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 language	English (US)
Pages (from-to)	2210-2216
Number of pages	7
Journal	Bioinformatics
Volume	22
Issue number	18
DOIs	https://doi.org/10.1093/bioinformatics/btl329
State	Published - Sep 15 2006
Externally published	Yes

ASJC Scopus subject areas

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

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10.1093/bioinformatics/btl329

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Li, L., Cheng, A. S. L., Jin, V. X., Paik, H. H., Fan, M., Li, X., Zhang, W., Robarge, J., Balch, C., Davuluri, R. V., Kim, S., Huang, T. H. M., & Nephew, K. P. (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, 22(18), 2210-2216. https://doi.org/10.1093/bioinformatics/btl329

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

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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.}",

note = "Funding Information: This research was supported by National Cancer Institute grants CA085289 (K.P.N.) and CA113001 (T.H-M.H.).",

year = "2006",

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doi = "10.1093/bioinformatics/btl329",

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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.

N1 - Funding Information: This research was supported by National Cancer Institute grants CA085289 (K.P.N.) and CA113001 (T.H-M.H.).

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.

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

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