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Population size estimation for quality control of ChIP-Seq datasets

Chromatin immunoprecipitation followed by sequencing, i.e. ChIP-Seq, is a widely used experimental technology for the identification of functional protein-DNA interactions. Nowadays, such databases as ENCODE, GTRD, ChIP-Atlas and ReMap systematically collect and annotate a large number of ChIP-Seq d...

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Autores principales: Kolmykov, Semyon K., Kondrakhin, Yury V., Yevshin, Ivan S., Sharipov, Ruslan N., Ryabova, Anna S., Kolpakov, Fedor A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715275/
https://www.ncbi.nlm.nih.gov/pubmed/31465497
http://dx.doi.org/10.1371/journal.pone.0221760
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author Kolmykov, Semyon K.
Kondrakhin, Yury V.
Yevshin, Ivan S.
Sharipov, Ruslan N.
Ryabova, Anna S.
Kolpakov, Fedor A.
author_facet Kolmykov, Semyon K.
Kondrakhin, Yury V.
Yevshin, Ivan S.
Sharipov, Ruslan N.
Ryabova, Anna S.
Kolpakov, Fedor A.
author_sort Kolmykov, Semyon K.
collection PubMed
description Chromatin immunoprecipitation followed by sequencing, i.e. ChIP-Seq, is a widely used experimental technology for the identification of functional protein-DNA interactions. Nowadays, such databases as ENCODE, GTRD, ChIP-Atlas and ReMap systematically collect and annotate a large number of ChIP-Seq datasets. Comprehensive control of dataset quality is currently indispensable to select the most reliable data for further analysis. In addition to existing quality control metrics, we have developed two novel metrics that allow to control false positives and false negatives in ChIP-Seq datasets. For this purpose, we have adapted well-known population size estimate for determination of unknown number of genuine transcription factor binding regions. Determination of the proposed metrics was based on overlapping distinct binding sites derived from processing one ChIP-Seq experiment by different peak callers. Moreover, the metrics also can be useful for assessing quality of datasets obtained from processing distinct ChIP-Seq experiments by a given peak caller. We also have shown that these metrics appear to be useful not only for dataset selection but also for comparison of peak callers and identification of site motifs based on ChIP-Seq datasets. The developed algorithm for determination of the false positive control metric and false negative control metric for ChIP-Seq datasets was implemented as a plugin for a BioUML platform: https://ict.biouml.org/bioumlweb/chipseq_analysis.html.
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spelling pubmed-67152752019-09-10 Population size estimation for quality control of ChIP-Seq datasets Kolmykov, Semyon K. Kondrakhin, Yury V. Yevshin, Ivan S. Sharipov, Ruslan N. Ryabova, Anna S. Kolpakov, Fedor A. PLoS One Research Article Chromatin immunoprecipitation followed by sequencing, i.e. ChIP-Seq, is a widely used experimental technology for the identification of functional protein-DNA interactions. Nowadays, such databases as ENCODE, GTRD, ChIP-Atlas and ReMap systematically collect and annotate a large number of ChIP-Seq datasets. Comprehensive control of dataset quality is currently indispensable to select the most reliable data for further analysis. In addition to existing quality control metrics, we have developed two novel metrics that allow to control false positives and false negatives in ChIP-Seq datasets. For this purpose, we have adapted well-known population size estimate for determination of unknown number of genuine transcription factor binding regions. Determination of the proposed metrics was based on overlapping distinct binding sites derived from processing one ChIP-Seq experiment by different peak callers. Moreover, the metrics also can be useful for assessing quality of datasets obtained from processing distinct ChIP-Seq experiments by a given peak caller. We also have shown that these metrics appear to be useful not only for dataset selection but also for comparison of peak callers and identification of site motifs based on ChIP-Seq datasets. The developed algorithm for determination of the false positive control metric and false negative control metric for ChIP-Seq datasets was implemented as a plugin for a BioUML platform: https://ict.biouml.org/bioumlweb/chipseq_analysis.html. Public Library of Science 2019-08-29 /pmc/articles/PMC6715275/ /pubmed/31465497 http://dx.doi.org/10.1371/journal.pone.0221760 Text en © 2019 Kolmykov et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kolmykov, Semyon K.
Kondrakhin, Yury V.
Yevshin, Ivan S.
Sharipov, Ruslan N.
Ryabova, Anna S.
Kolpakov, Fedor A.
Population size estimation for quality control of ChIP-Seq datasets
title Population size estimation for quality control of ChIP-Seq datasets
title_full Population size estimation for quality control of ChIP-Seq datasets
title_fullStr Population size estimation for quality control of ChIP-Seq datasets
title_full_unstemmed Population size estimation for quality control of ChIP-Seq datasets
title_short Population size estimation for quality control of ChIP-Seq datasets
title_sort population size estimation for quality control of chip-seq datasets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6715275/
https://www.ncbi.nlm.nih.gov/pubmed/31465497
http://dx.doi.org/10.1371/journal.pone.0221760
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