ArticleBriefings in bioinformatics2017
Features that define the best ChIP-seq peak calling algorithms.
Article in Briefings in bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed.
- Many roads lead to a plant cistrome: mapping and interpreting transcription factor binding in plants.Genome biology · 2026Review
- ChromAcS: an automated and flexible GUI for end-to-end reproducible ATAC-seq analysis across multiple species.BMC bioinformatics · 2026Article
- ChIP happens: from biochemical origins to the modern omics toolbox for understanding steroid hormone receptors.The Biochemical journal · 2026Review
- RM2Target v2.0: an updated database for the target genes of writers, erasers, and readers of RNA modifications.Nucleic acids research · 2026Article
- Accurate chromatin marks peak calling with Omnipeak.Nucleic acids research · 2026Article
- Benchmarking transcription factor binding site prediction models: a comparative analysis on synthetic and biological data.Briefings in bioinformatics · 2025Article
- Evaluating the Performance of Peak Calling Algorithms Available for Intracellular G-Quadruplex Sequencing.International journal of molecular sciences · 2025Article
- ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning.Nature communications · 2025Article
- An AI Agent for Fully Automated Multi-Omic Analyses.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data.NAR genomics and bioinformatics · 2024Article
- Differential Analysis of Protein-DNA Binding Using ChIP-Seq Data.Methods in molecular biology (Clifton, N.J.) · 2024Article
- PAPerFly: Partial Assembly-based Peak Finder for ab initio binding site reconstruction.BMC bioinformatics · 2023Article
- MESIA: multi-epigenome sample integration approach for precise peak calling.Scientific reports · 2023Article
- Analyzing histone ChIP-seq data with a bin-based probability of being signal.PLoS computational biology · 2023Article
- Rescuing biologically relevant consensus regions across replicated samples.BMC bioinformatics · 2023Article
- A survey on algorithms to characterize transcription factor binding sites.Briefings in bioinformatics · 2023Review
- RM2Target: a comprehensive database for targets of writers, erasers and readers of RNA modifications.Nucleic acids research · 2023Article
- DamID-seq: A Genome-Wide DNA Methylation Method that Captures Both Transient and Stable TF-DNA Interactions in Plant Cells.Methods in molecular biology (Clifton, N.J.) · 2023Article
- GoPeaks: histone modification peak calling for CUT&Tag.Genome biology · 2022Article
- ChIP-AP: an integrated analysis pipeline for unbiased ChIP-seq analysis.Briefings in bioinformatics · 2022Article
3 more citing papers are in PubMed but not listed here.
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4 authors.
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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.
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