TY - JOUR
T1 - Picking ChIP-seq peak detectors for analyzing chromatin modification experiments
AU - Micsinai, Mariann
AU - Parisi, Fabio
AU - Strino, Francesco
AU - Asp, Patrik
AU - Dynlacht, Brian D.
AU - Kluger, Yuval
N1 - Funding Information:
National Institutes of Health [Research Grant from the National Cancer Institute (CA-16359 to Y.K.) and (GM067132, GM067132-07S1 to B.D.)]; National Science Foundation (IGERT0333389 to M.M.); and by the American-Italian Cancer Foundation (Post-Doctoral Research Fellowship to F.S.). Funding for open access charge: Discretionary funds.
PY - 2012/5
Y1 - 2012/5
N2 - 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.
AB - 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.
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U2 - 10.1093/nar/gks048
DO - 10.1093/nar/gks048
M3 - Article
C2 - 22307239
AN - SCOPUS:84861397707
SN - 0305-1048
VL - 40
SP - e70
JO - Nucleic acids research
JF - Nucleic acids research
IS - 9
ER -