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 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.
Yale University School of Medicine, Department of Pathology, New Haven, CT 06520, USA.
Mariann
M
Micsinai
Fabio
F
Parisi
Francesco
F
Strino
Patrik
P
Asp
Brian D
BD
Dynlacht
Yuval
Y
Kluger
D000465:Algorithms; D000818:Animals; D016477:Artifacts; D002843:Chromatin / Q000378:metabolism*; D047369:Chromatin Immunoprecipitation; D044127:Epigenesis, Genetic; D059014:High-Throughput Nucleotide Sequencing; D006657:Histones / Q000378:metabolism; D051379:Mice; D017422:Sequence Analysis, DNA
D002843:Chromatin; D006657:Histones