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

^*Corresponding author for this work

Preventive Medicine

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

Funding

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

ASJC Scopus subject areas

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

@article{151890376aa6406ebaed83ee4554414d,

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",

month = sep,

day = "15",

doi = "10.1093/bioinformatics/btl329",

language = "English (US)",

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journal = "Bioinformatics",

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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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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.

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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ER -

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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