ArticlePloS one2014
A comparison of peak callers used for DNase-Seq data.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 44 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
44 citing papers in PubMed.
- Motif-Cluster: Motif driven prioritization of transcription factor binding clusters.PLoS computational biology · 2026Article
- ChromAcS: an automated and flexible GUI for end-to-end reproducible ATAC-seq analysis across multiple species.BMC bioinformatics · 2026Article
- A genome scale transcriptional regulatory model of the human placenta.Science advances · 2024Article
- Chromatin accessibility profiling reveals that human fibroblasts respond to mechanical stimulation in a cell-specific manner.JBMR plus · 2024Article
- JMnorm: a novel joint multi-feature normalization method for integrative and comparative epigenomics.Nucleic acids research · 2024Article
- Genetic variation in chromatin state across multiple tissues in Drosophila melanogaster.PLoS genetics · 2023Article
- Evaluating deep learning for predicting epigenomic profiles.Nature machine intelligence · 2022Article
- ChIP-AP: an integrated analysis pipeline for unbiased ChIP-seq analysis.Briefings in bioinformatics · 2022Article
- PEPATAC: an optimized pipeline for ATAC-seq data analysis with serial alignments.NAR genomics and bioinformatics · 2021Article
- Integrative analysis of epigenetics data identifies gene-specific regulatory elements.Nucleic acids research · 2021Article
- DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers.Genomics, proteomics & bioinformatics · 2021Article
- F-Seq2: improving the feature density based peak caller with dynamic statistics.NAR genomics and bioinformatics · 2021Article
- Self-organizing maps with variable neighborhoods facilitate learning of chromatin accessibility signal shapes associated with regulatory elements.BMC bioinformatics · 2021Article
- Chromatin accessibility profiling methods.Nature reviews. Methods primers · 2021Article
- STARRPeaker: uniform processing and accurate identification of STARR-seq active regions.Genome biology · 2020Article
- Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types.Cell reports · 2020Article
- Review
- The Road Not Taken with Pyrrole-Imidazole Polyamides: Off-Target Effects and Genomic Binding.Biomolecules · 2020Review
- Considerations in the analysis of plant chromatin accessibility data.Current opinion in plant biology · 2020Review
- From reads to insight: a hitchhiker's guide to ATAC-seq data analysis.Genome biology · 2020Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
Genome-wide profiling of open chromatin regions using DNase I and high-throughput sequencing (DNase-seq) is an increasingly popular approach for finding and studying regulatory elements. A variety of algorithms have been developed to identify regions of open chromatin from raw sequence-tag data, which has motivated us to assess and compare their performance. In this study, four published, publicly available peak calling algorithms used for DNase-seq data analysis (F-seq, Hotspot, MACS and ZINBA) are assessed at a range of signal thresholds on two published DNase-seq datasets for three cell types. The results were benchmarked against an independent dataset of regulatory regions derived from ENCODE in vivo transcription factor binding data for each particular cell type. The level of overlap between peak regions reported by each algorithm and this ENCODE-derived reference set was used to assess sensitivity and specificity of the algorithms. Our study suggests that F-seq has a slightly higher sensitivity than the next best algorithms. Hotspot and the ChIP-seq oriented method, MACS, both perform competitively when used with their default parameters. However the generic peak finder ZINBA appears to be less sensitive than the other three. We also assess accuracy of each algorithm over a range of signal thresholds. In particular, we show that the accuracy of F-Seq can be considerably improved by using a threshold setting that is different from the default value.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.