Picking ChIP-seq peak detectors for analyzing chromatin modification experiments

Numerous algorithms have been developed to analyze ChIP-Seq data. However, the complexity of analyzing diverse patterns of ChIP-Seq signals, especially for epigenetic marks, still calls for the development of new algorithms and objective comparisons of existing methods. We developed Qeseq, an algori...

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Main Authors:	Micsinai, Mariann, Parisi, Fabio, Strino, Francesco, Asp, Patrik, Dynlacht, Brian D., Kluger, Yuval
Format:	Online
Language:	English
Published:	Oxford University Press 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3351193/

id	pubmed-3351193
recordtype	oai_dc
spelling	pubmed-33511932012-05-14 Picking ChIP-seq peak detectors for analyzing chromatin modification experiments Micsinai, Mariann Parisi, Fabio Strino, Francesco Asp, Patrik Dynlacht, Brian D. Kluger, Yuval Methods Online Numerous algorithms have been developed to analyze ChIP-Seq data. However, the complexity of analyzing diverse patterns of ChIP-Seq signals, especially for epigenetic marks, still calls for the development of new algorithms and objective comparisons of existing methods. We developed Qeseq, an algorithm to detect regions of increased ChIP read density relative to background. Qeseq employs critical novel elements, such as iterative recalibration and neighbor joining of reads to identify enriched regions of any length. To objectively assess its performance relative to other 14 ChIP-Seq peak finders, we designed a novel protocol based on Validation Discriminant Analysis (VDA) to optimally select validation sites and generated two validation datasets, which are the most comprehensive to date for algorithmic benchmarking of key epigenetic marks. In addition, we systematically explored a total of 315 diverse parameter configurations from these algorithms and found that typically optimal parameters in one dataset do not generalize to other datasets. Nevertheless, default parameters show the most stable performance, suggesting that they should be used. This study also provides a reproducible and generalizable methodology for unbiased comparative analysis of high-throughput sequencing tools that can facilitate future algorithmic development. Oxford University Press 2012-05 2012-02-14 /pmc/articles/PMC3351193/ /pubmed/22307239 http://dx.doi.org/10.1093/nar/gks048 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Micsinai, Mariann Parisi, Fabio Strino, Francesco Asp, Patrik Dynlacht, Brian D. Kluger, Yuval
spellingShingle	Micsinai, Mariann Parisi, Fabio Strino, Francesco Asp, Patrik Dynlacht, Brian D. Kluger, Yuval Picking ChIP-seq peak detectors for analyzing chromatin modification experiments
author_facet	Micsinai, Mariann Parisi, Fabio Strino, Francesco Asp, Patrik Dynlacht, Brian D. Kluger, Yuval
author_sort	Micsinai, Mariann
title	Picking ChIP-seq peak detectors for analyzing chromatin modification experiments
title_short	Picking ChIP-seq peak detectors for analyzing chromatin modification experiments
title_full	Picking ChIP-seq peak detectors for analyzing chromatin modification experiments
title_fullStr	Picking ChIP-seq peak detectors for analyzing chromatin modification experiments
title_full_unstemmed	Picking ChIP-seq peak detectors for analyzing chromatin modification experiments
title_sort	picking chip-seq peak detectors for analyzing chromatin modification experiments
description	Numerous algorithms have been developed to analyze ChIP-Seq data. However, the complexity of analyzing diverse patterns of ChIP-Seq signals, especially for epigenetic marks, still calls for the development of new algorithms and objective comparisons of existing methods. We developed Qeseq, an algorithm to detect regions of increased ChIP read density relative to background. Qeseq employs critical novel elements, such as iterative recalibration and neighbor joining of reads to identify enriched regions of any length. To objectively assess its performance relative to other 14 ChIP-Seq peak finders, we designed a novel protocol based on Validation Discriminant Analysis (VDA) to optimally select validation sites and generated two validation datasets, which are the most comprehensive to date for algorithmic benchmarking of key epigenetic marks. In addition, we systematically explored a total of 315 diverse parameter configurations from these algorithms and found that typically optimal parameters in one dataset do not generalize to other datasets. Nevertheless, default parameters show the most stable performance, suggesting that they should be used. This study also provides a reproducible and generalizable methodology for unbiased comparative analysis of high-throughput sequencing tools that can facilitate future algorithmic development.
publisher	Oxford University Press
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3351193/
_version_	1611529300007190528

Picking ChIP-seq peak detectors for analyzing chromatin modification experiments

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