Features that define the best ChIP-seq peak calling algorithms

Abstract

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an important tool for studying gene regulatory proteins, such as transcription factors and histones. Peak calling is one of the first steps in the analysis of these data. Peak calling consists of two sub-problems: identifying candidate peaks and testing candidate peaks for statistical significance. We surveyed 30 methods and identified 12 features of the two sub-problems that distinguish methods from each other. We picked six methods GEM, MACS2, MUSIC, BCP, Threshold-based method (TM) and ZINBA] that span this feature space and used a combination of 300 simulated ChIP-seq data sets, 3 real data sets and mathematical analyses to identify features of methods that allow some to perform better than the others. We prove that methods that explicitly combine the signals from ChIP and input samples are less powerful than methods that do not. Methods that use windows of different sizes are more powerful than the ones that do not. For statistical testing of candidate peaks, methods that use a Poisson test to rank their candidate peaks are more powerful than those that use a Binomial test. BCP and MACS2 have the best operating characteristics on simulated transcription factor binding data. GEM has the highest fraction of the top 500 peaks containing the binding motif of the immunoprecipitated factor, with 50% of its peaks within 10 base pairs of a motif. BCP and MUSIC perform best on histone data. These findings provide guidance and rationale for selecting the best peak caller for a given application.

Targeted Degradation of CTCF Decouples Local Insulation of Chromosome Domains from Genomic Compartmentalization

“…Chip-seq peaks were called on each replicate individually using all available reads. For peak calling we followed the guidelines described in (Thomas et al, 2016). For CTCF, which display focal enrichment, we used the Genome-wide Event finding and Motif discovery (GEM) method (Guo et al, 2012).…”

Characteristics of functional enrichment and gene expression level of human putative transcriptional target genes

“…Prediction of transcriptional target genes from open chromatin regions includes false positives, since DNase I cleavage bias affects the computational analysis of DNase-seq experiments [44]. However, the detection of ChIP-seq peaks is also changed depending on the methods to identify them and the depth of DNA sequencing of ChIP-seq experiments [45]. Though human putative transcriptional target genes include false positives, they showed significantly the largest number of functional enrichments, compared with target genes including 5–60% of randomly selected genes (Fig.…”

Characteristics of functional enrichment and gene expression level of human putative transcriptional target genes

“…Prediction of transcriptional target genes from open chromatin regions includes false positives, since DNase I cleavage The bold numbers are the highest numbers in each cell type and functional annotation database bias affects the computational analysis of DNase-seq experiments [44]. However, the detection of ChIP-seq peaks is also changed depending on the methods to identify them and the depth of DNA sequencing of ChIP-seq experiments [45]. Though human putative transcriptional target genes include false positives, they showed significantly the largest number of functional enrichments, compared with target genes including 5-60% of randomly selected genes (Fig.…”