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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 proceeding › Conference contribution › Research › peer-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, Novosibirsk, Russian Federation,
06.07.2020.
https://doi.org/10.1109/CSGB51356.2020.9214614APA
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/CSGB51356.2020.9214614Vancouver
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
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 ; Conference date: 06-07-2020 Through 10-07-2020",
year = "2020",
month = jul,
doi = "10.1109/CSGB51356.2020.9214614",
language = "English",
isbn = "9781728195971",
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",
url = "https://ieeexplore.ieee.org/xpl/conhome/9210711/proceeding",
}
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 - Conference code: Second
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
UR - https://www.mendeley.com/catalogue/b5b0f4eb-3e39-396b-ba3b-aa9c7c094511/
U2 - 10.1109/CSGB51356.2020.9214614
DO - 10.1109/CSGB51356.2020.9214614
M3 - Conference contribution
AN - SCOPUS:85094822537
SN - 9781728195971
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
Y2 - 6 July 2020 through 10 July 2020
ER -
