Predicting regulatory elements in repetitive sequences using transcription factor binding sites

Jorng Tzong Horng; Wen Fu Cho; Cheng Yan Kao

Predicting regulatory elements in repetitive sequences using transcription factor binding sites

Jorng Tzong Horng, Wen Fu Cho, Cheng Yan Kao

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Repeat sequences are the most abundant ones in the extragenic region of genomes. Biologists have already found a large number of regulatory elements in this region. These elements may profoundly impact the chromatin structure formation in nucleus and also contain important clues in gemetic evolution and phylogeny study. This study attempts to mine rules on how combinations of individual binding sites are distributed repeat sequences. The association rules mined would facilitate efforts to identify gene classes regulated by similar mechanisms and accurately predict regulatory elements. Herein, the combinations of transcription factor binding sites in the repeat sequences are obtained and, then, data mining techniques are applied to mine the association rules from the combination of binding sites. In addition, the discovered associations are further proned to remove those insignificant associations and obtain a set of discovered associations. Finally, the discovered association rules are used to partially classify the repeat sequences in our repeat database. Experiments on several genomes include C.Elegans, Human Chromosome 22, and Yeast.

Original language	English
Title of host publication	Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, CBGIST 2001
Editors	C.H. Wu, P.P. Wang, J.T.L. Wang
Pages	215-219
Number of pages	5
State	Published - 2001
Event	Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, GBGIST 2001 - Durham, NC, United States Duration: 15 Mar 2001 → 17 Mar 2001

Publication series

Name	Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technolgoy, CBGIST 2001

Conference

Conference	Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, GBGIST 2001
Country/Territory	United States
City	Durham, NC
Period	15/03/01 → 17/03/01

Cite this

Horng, J. T., Cho, W. F., & Kao, C. Y. (2001). Predicting regulatory elements in repetitive sequences using transcription factor binding sites. In C. H. Wu, P. P. Wang, & J. T. L. Wang (Eds.), Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, CBGIST 2001 (pp. 215-219). (Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technolgoy, CBGIST 2001).

Horng, Jorng Tzong ; Cho, Wen Fu ; Kao, Cheng Yan. / Predicting regulatory elements in repetitive sequences using transcription factor binding sites. Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, CBGIST 2001. editor / C.H. Wu ; P.P. Wang ; J.T.L. Wang. 2001. pp. 215-219 (Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technolgoy, CBGIST 2001).

@inproceedings{ebe86b30525e405c80f60cbeed7da84c,

title = "Predicting regulatory elements in repetitive sequences using transcription factor binding sites",

abstract = "Repeat sequences are the most abundant ones in the extragenic region of genomes. Biologists have already found a large number of regulatory elements in this region. These elements may profoundly impact the chromatin structure formation in nucleus and also contain important clues in gemetic evolution and phylogeny study. This study attempts to mine rules on how combinations of individual binding sites are distributed repeat sequences. The association rules mined would facilitate efforts to identify gene classes regulated by similar mechanisms and accurately predict regulatory elements. Herein, the combinations of transcription factor binding sites in the repeat sequences are obtained and, then, data mining techniques are applied to mine the association rules from the combination of binding sites. In addition, the discovered associations are further proned to remove those insignificant associations and obtain a set of discovered associations. Finally, the discovered association rules are used to partially classify the repeat sequences in our repeat database. Experiments on several genomes include C.Elegans, Human Chromosome 22, and Yeast.",

author = "Horng, {Jorng Tzong} and Cho, {Wen Fu} and Kao, {Cheng Yan}",

year = "2001",

language = "???core.languages.en_GB???",

isbn = "0970789009",

series = "Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technolgoy, CBGIST 2001",

pages = "215--219",

editor = "C.H. Wu and P.P. Wang and J.T.L. Wang",

booktitle = "Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, CBGIST 2001",

note = "Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, GBGIST 2001 ; Conference date: 15-03-2001 Through 17-03-2001",

}

Horng, JT, Cho, WF & Kao, CY 2001, Predicting regulatory elements in repetitive sequences using transcription factor binding sites. in CH Wu, PP Wang & JTL Wang (eds), Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, CBGIST 2001. Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technolgoy, CBGIST 2001, pp. 215-219, Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, GBGIST 2001, Durham, NC, United States, 15/03/01.

Predicting regulatory elements in repetitive sequences using transcription factor binding sites. / Horng, Jorng Tzong; Cho, Wen Fu; Kao, Cheng Yan.
Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, CBGIST 2001. ed. / C.H. Wu; P.P. Wang; J.T.L. Wang. 2001. p. 215-219 (Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technolgoy, CBGIST 2001).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Predicting regulatory elements in repetitive sequences using transcription factor binding sites

AU - Horng, Jorng Tzong

AU - Cho, Wen Fu

AU - Kao, Cheng Yan

PY - 2001

Y1 - 2001

N2 - Repeat sequences are the most abundant ones in the extragenic region of genomes. Biologists have already found a large number of regulatory elements in this region. These elements may profoundly impact the chromatin structure formation in nucleus and also contain important clues in gemetic evolution and phylogeny study. This study attempts to mine rules on how combinations of individual binding sites are distributed repeat sequences. The association rules mined would facilitate efforts to identify gene classes regulated by similar mechanisms and accurately predict regulatory elements. Herein, the combinations of transcription factor binding sites in the repeat sequences are obtained and, then, data mining techniques are applied to mine the association rules from the combination of binding sites. In addition, the discovered associations are further proned to remove those insignificant associations and obtain a set of discovered associations. Finally, the discovered association rules are used to partially classify the repeat sequences in our repeat database. Experiments on several genomes include C.Elegans, Human Chromosome 22, and Yeast.

AB - Repeat sequences are the most abundant ones in the extragenic region of genomes. Biologists have already found a large number of regulatory elements in this region. These elements may profoundly impact the chromatin structure formation in nucleus and also contain important clues in gemetic evolution and phylogeny study. This study attempts to mine rules on how combinations of individual binding sites are distributed repeat sequences. The association rules mined would facilitate efforts to identify gene classes regulated by similar mechanisms and accurately predict regulatory elements. Herein, the combinations of transcription factor binding sites in the repeat sequences are obtained and, then, data mining techniques are applied to mine the association rules from the combination of binding sites. In addition, the discovered associations are further proned to remove those insignificant associations and obtain a set of discovered associations. Finally, the discovered association rules are used to partially classify the repeat sequences in our repeat database. Experiments on several genomes include C.Elegans, Human Chromosome 22, and Yeast.

UR - http://www.scopus.com/inward/record.url?scp=1842453331&partnerID=8YFLogxK

M3 - 會議論文篇章

AN - SCOPUS:1842453331

SN - 0970789009

T3 - Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technolgoy, CBGIST 2001

SP - 215

EP - 219

BT - Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, CBGIST 2001

A2 - Wu, C.H.

A2 - Wang, P.P.

A2 - Wang, J.T.L.

T2 - Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, GBGIST 2001

Y2 - 15 March 2001 through 17 March 2001

ER -

Horng JT, Cho WF, Kao CY. Predicting regulatory elements in repetitive sequences using transcription factor binding sites. In Wu CH, Wang PP, Wang JTL, editors, Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technology, CBGIST 2001. 2001. p. 215-219. (Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technolgoy, CBGIST 2001).

Predicting regulatory elements in repetitive sequences using transcription factor binding sites

Abstract

Publication series

Conference

Fingerprint

Cite this