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Meta-analysis of ChIP-seq Datasets through the Rank Aggregation Approach. / Kolmykov, Semyon K.; Kondrakhin, Yury V.; Sharipov, Ruslan N. et al.

Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 180-184 9214614 (Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Harvard

Kolmykov, SK, Kondrakhin, YV, Sharipov, RN, Yevshi, IS, Ryabova, AS & Kolpakov, FA 2020, Meta-analysis of ChIP-seq Datasets through the Rank Aggregation Approach. in Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020., 9214614, Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020, Institute of Electrical and Electronics Engineers Inc., pp. 180-184, 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020, Novosibirsk, Russian Federation, 06.07.2020. https://doi.org/10.1109/CSGB51356.2020.9214614

APA

Kolmykov, S. K., Kondrakhin, Y. V., Sharipov, R. N., Yevshi, I. S., Ryabova, A. S., & Kolpakov, F. A. (2020). Meta-analysis of ChIP-seq Datasets through the Rank Aggregation Approach. In Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020 (pp. 180-184). [9214614] (Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSGB51356.2020.9214614

Vancouver

Kolmykov SK, Kondrakhin YV, Sharipov RN, Yevshi IS, Ryabova AS, Kolpakov FA. Meta-analysis of ChIP-seq Datasets through the Rank Aggregation Approach. In Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 180-184. 9214614. (Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020). doi: 10.1109/CSGB51356.2020.9214614

Author

Kolmykov, Semyon K. ; Kondrakhin, Yury V. ; Sharipov, Ruslan N. et al. / Meta-analysis of ChIP-seq Datasets through the Rank Aggregation Approach. Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 180-184 (Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020).

BibTeX

@inproceedings{3e89e2c1ff3843bd8368c01c08f61c1d,
title = "Meta-analysis of ChIP-seq Datasets through the Rank Aggregation Approach",
abstract = "Understanding the basic mechanisms of transcription regulation is a major challenge in modern biology. Regulation of transcription is a complex process in which transcription factors (TFs) play a key role. Chromatin immunoprecipitation followed by high throughput sequencing is a widely and intensively used experimental technology for the identification of TF binding sites (TFBSs). Nowadays, there are tens or hundreds of ChIP-seq datasets measured for the same transcription factor. Meta-processing of such datasets into an integrated dataset is relevant. We have developed a novel method for creating these integrated datasets of TFBSs. This method consists of a three-stage application of the Rank Aggregation approach. The identified TFBSs can be sorted to further select the most reliable TFBSs. We have found a high saturation of site motifs in the most reliable TFBSs. We have also demonstrated that the most reliable TFBSs prefer to be located in open chromatin regions.",
keywords = "ChIP-seq, GTRD database, Meta-analysis, normal mixture, rank aggregation approach, transcription factor binding sites",
author = "Kolmykov, {Semyon K.} and Kondrakhin, {Yury V.} and Sharipov, {Ruslan N.} and Yevshi, {Ivan S.} and Ryabova, {Anna S.} and Kolpakov, {Fedor A.}",
note = "Funding Information: ACKNOWLEDGMENT This study was supported by the Russian Science Foundation, grant № 19-14-00295. Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020 ; Conference date: 06-07-2020 Through 10-07-2020",
year = "2020",
month = jul,
doi = "10.1109/CSGB51356.2020.9214614",
language = "English",
series = "Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "180--184",
booktitle = "Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020",
address = "United States",

}

RIS

TY - GEN

T1 - Meta-analysis of ChIP-seq Datasets through the Rank Aggregation Approach

AU - Kolmykov, Semyon K.

AU - Kondrakhin, Yury V.

AU - Sharipov, Ruslan N.

AU - Yevshi, Ivan S.

AU - Ryabova, Anna S.

AU - Kolpakov, Fedor A.

N1 - Funding Information: ACKNOWLEDGMENT This study was supported by the Russian Science Foundation, grant № 19-14-00295. Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/7

Y1 - 2020/7

N2 - Understanding the basic mechanisms of transcription regulation is a major challenge in modern biology. Regulation of transcription is a complex process in which transcription factors (TFs) play a key role. Chromatin immunoprecipitation followed by high throughput sequencing is a widely and intensively used experimental technology for the identification of TF binding sites (TFBSs). Nowadays, there are tens or hundreds of ChIP-seq datasets measured for the same transcription factor. Meta-processing of such datasets into an integrated dataset is relevant. We have developed a novel method for creating these integrated datasets of TFBSs. This method consists of a three-stage application of the Rank Aggregation approach. The identified TFBSs can be sorted to further select the most reliable TFBSs. We have found a high saturation of site motifs in the most reliable TFBSs. We have also demonstrated that the most reliable TFBSs prefer to be located in open chromatin regions.

AB - Understanding the basic mechanisms of transcription regulation is a major challenge in modern biology. Regulation of transcription is a complex process in which transcription factors (TFs) play a key role. Chromatin immunoprecipitation followed by high throughput sequencing is a widely and intensively used experimental technology for the identification of TF binding sites (TFBSs). Nowadays, there are tens or hundreds of ChIP-seq datasets measured for the same transcription factor. Meta-processing of such datasets into an integrated dataset is relevant. We have developed a novel method for creating these integrated datasets of TFBSs. This method consists of a three-stage application of the Rank Aggregation approach. The identified TFBSs can be sorted to further select the most reliable TFBSs. We have found a high saturation of site motifs in the most reliable TFBSs. We have also demonstrated that the most reliable TFBSs prefer to be located in open chromatin regions.

KW - ChIP-seq

KW - GTRD database

KW - Meta-analysis

KW - normal mixture

KW - rank aggregation approach

KW - transcription factor binding sites

UR - http://www.scopus.com/inward/record.url?scp=85094822537&partnerID=8YFLogxK

UR - https://elibrary.ru/item.asp?id=45218605

U2 - 10.1109/CSGB51356.2020.9214614

DO - 10.1109/CSGB51356.2020.9214614

M3 - Conference contribution

AN - SCOPUS:85094822537

T3 - Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

SP - 180

EP - 184

BT - Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020

Y2 - 6 July 2020 through 10 July 2020

ER -

ID: 25999554