Peak calling explained

Peak calling is a computational method used to identify areas in a genome that have been enriched with aligned reads as a consequence of performing a ChIP-sequencing or MeDIP-seq experiment. These areas are those where a protein interacts with DNA.[1] When the protein is a transcription factor, the enriched area is its transcription factor binding site (TFBS). Popular software programs include MACS.[2] Wilbanks and colleagues[3] is a survey of the ChIP-seq peak callers, and Bailey et al.[4] is a description of practical guidelines for peak calling in ChIP-seq data.

Methods

Peak calling may be conducted on transcriptome/exome as well to RNA epigenome sequencing data from MeRIPseq[5] or m6Aseq[6] for detection of post-transcriptional RNA modification sites with software programs, such as exomePeak.[7] Many of the peak calling tools are optimized for only some kind of assays such as only for transcription-factor ChIP-seq or only for DNase-Seq.[8] However new generation of peak callers such as DFilter[9] are based on generalized optimal theory of detection and has been shown to work for nearly all kinds for tag profile signals from next-gen sequencing data. It is also possible to do more complex analysis using such tools like combining multiple ChIP-seq signal to detect regulatory sites.[10] In the context of ChIP-exo, this process is known as 'peak-pair calling'.[11] A recent benchmarking study compared the performance of several peak calling tools, highlighting the strengths and limitations of each method. This study evaluated peak calling tools including MACS2, SEACR, GoPeaks, and LanceOtron, and provides guidance for selecting appropriate peak callers in CUT&RUN experiments.[12]

Differential peak calling

Differential peak calling is about identifying significant differences in two ChIP-seq signals. One can distinguish between one-stage and two-stage differential peak callers. One stage differential peak callers work in two phases: first, call peaks on individual ChIP-seq signals and second, combine individual signals and apply statistical tests to estimate differential peaks. DBChIP,[13] MACS2, and MAnorm[14] are examples for one stage differential peak callers.

Two stage differential peak callers segment two ChIP-seq signals and identify differential peaks in one step. They take advantage of signal segmentation approaches such as Hidden Markov Models. Examples for two-stage differential peak callers are ChIPDiff,[15] ODIN.[16] and THOR. Differential peak calling can also be applied in the context of analyzing RNA-binding protein binding sites.[17]

Software

This incomplete list includes tools that are commonly used for peak calling in bioinformatics analyses.[18]

List of peak-calling software! Program !! Year published !! Author(s) !! Description !! License !! Latest Version !! Active development!Source
MACS2021 (3.x)

2012 (2.x)

2008

Yong Zhang, Tao Liu, Clifford A Meyer, Michael S Lawrence, et al.Model-based Analysis of ChIP-Seq. Widely used for identifying narrow peaks (e.g., transcription factor binding sites). Models the characteristic tag shift size of ChIP-seq data and utilizes control samples for noise reduction.BSD 3-Clause3.0.3 (Feb 20, 2025)

2.2.9.1 (Dec 2023)

SICER2019 (SICER2)2009Chongzhi Zang, David E. Schones, Keji Zhao, W. Lee Kraus, et al.Spatial clustering approach initially developed for identifying diffuse signals and broad genomic regions of enrichmentMIT License1.0.2 (Feb 21, 2020)
epic22019Johannes Dröge, Johannes Alneberg, et al.A reimplementation of the SICER algorithm focused on improving performance (speed, memory usage) for identifying broad domains.MIT License0.2.2 (May 2023)[19]
HOMER2010Sven Heinz, Christopher Benner, Nelson Nery, et al.Part of a software suite, the `findPeaks` utility performs peak calling, with distinct modes for narrow peaks ('factor' style) and broad domains ('histone' style).GPL / Custom Academic4.11 (Nov 2019)[20]
SPP (R package)2008Peter V. Kharchenko, Mikhail Y. Tolstorukov, Peter J. ParkUses cross-correlation analysis to estimate fragment length and identify signal peaks. It was incorporated into the ENCODE analysis pipeline.Artistic License 2.01.15.4 (Oct 2023 / Bioconductor 3.18)
Genrich2018John S Hageman, Paweł Czyż, et al.Supports handling of multi-mapping reads, PCR duplicate removal, and integrated analysis of multiple replicates using Fisher's method.MIT License0.6.1 (Jun 2021)[21]
HPeak2010Zhaohui S Qin, Yongqun He, Arul M Chinnaiyan, et al.Peak-finding algorithm based on a Hidden Markov Model (HMM).Free Academic Use 1.0 (?)
JAMM2015Mahmoud M. Ibrahim, Scott A. Lacadie, Nikolaus Rajewsky, et al.Uses mixture model clustering of biological replicates.GPL-3.0-only1.0.7rev6 (~2014)
PePr2014Yanxiao Zhang, Maureen A. SartorUses a sliding window approach modeling read counts with a negative binomial distribution. Ranks identified peaks based on consistency across replicates.GPL-3.0-only1.1.20 (Sep 2019)[22]
LanceOtron2022Ross S. Harris, Nathan D. Leclair, et al.Deep learning (convolutional neural network) based peak caller.GPL-3.0-only1.0.1 (Jun 2023)[23]
SEACR2019Michael P. Meers, Daniel Tenenbaum, Steven HenikoffDesigned for low-background enrichment data common in techniques like CUT&RUN and CUT&Tag. It identifies enriched regions by comparing signal against the total signal, avoiding traditional input normalization.MIT License1.3 (May 2019)[24]
GoPeaks2021Vincent A. Zuber, Jeffrey E. Maxson, et al.Designed for CUT&RUN and CUT&Tag datasets.MIT License1.0.0 (Feb 2023)[25]

Published as pre-print

See also

External links

Notes and References

  1. Valouev A, etal . Genome-wide analysis of transcription factor binding sites based on ChIP-seq data . Nature Methods . 5 . 9 . 829–834 . September 2008 . 2917543 . 19160518 . 10.1038/nmeth.1246.
  2. Feng. Jianxing. Liu, Tao . Qin, Bo . Zhang, Yong . Liu, Xiaole Shirley . Identifying ChIP-seq enrichment using MACS. Nature Protocols. 29 August 2012. 7. 9. 1728–1740. 10.1038/nprot.2012.101. 22936215. 3868217.
  3. Wilbanks. Elizabeth G.. Facciotti, Marc T. . Evaluation of Algorithm Performance in ChIP-Seq Peak Detection. PLOS ONE. 7 July 2010. 5. 7. e11471. 10.1371/journal.pone.0011471. 20628599. 2900203. 2010PLoSO...511471W. free.
  4. Bailey. TL. Krajewski P . Ladunga I . Lefebvre C . Li Q . Liu T . Madrigal P . Taslim C . Zhang J. . Practical guidelines for the comprehensive analysis of ChIP-seq data. PLOS Comput Biol. 14 November 2013. 9. 11. e1003326. 10.1371/journal.pcbi.1003326. 24244136. 3828144. 2013PLSCB...9E3326B . free .
  5. Meyer. Kate D.. Saletore, Yogesh . Zumbo, Paul . Elemento, Olivier . Mason, Christopher E. . Jaffrey, Samie R. . Comprehensive Analysis of mRNA Methylation Reveals Enrichment in 3′ UTRs and near Stop Codons. Cell. 31 May 2012. 149. 7. 1635–1646. 10.1016/j.cell.2012.05.003. 22608085. 3383396.
  6. Dominissini . Dan . Moshitch-Moshkovitz . Sharon . Schwartz . Schraga . Salmon-Divon . Mali . Ungar . Lior . Osenberg . Sivan . Cesarkas . Karen . Jacob-Hirsch . Jasmine . Amariglio . Ninette . Kupiec . Martin . Sorek . Rotem . Rechavi . Gideon . Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq . Nature . 28 April 2012 . 485 . 7397 . 201–206 . 10.1038/nature11112. 22575960. 2012Natur.485..201D . 3517716 .
  7. Meng. J.. Cui, X. . Rao, M. K. . Chen, Y. . Huang, Y. . Exome-based analysis for RNA epigenome sequencing data. Bioinformatics. 14 April 2013. 29. 12. 1565–1567. 10.1093/bioinformatics/btt171 . 23589649 . 3673212.
  8. Koohy . Hashem . Down, Thomas A. . Spivakov, Mikhail . Hubbard, Tim . Helmer-Citterich, Manuela . A Comparison of Peak Callers Used for DNase-Seq Data . PLOS ONE . 8 May 2014 . 9 . 5 . e96303 . 10.1371/journal.pone.0096303 . 24810143 . 4014496. 2014PLoSO...996303K . free .
  9. Kumar . Vibhor . Masafumi Muratani . Nirmala Arul Rayan . Petra Kraus . Thomas Lufkin . Huck Hui Ng . Shyam Prabhakar . Jul 2013 . Uniform, optimal signal processing of mapped deep-sequencing data . Nature Biotechnology . 31 . 7 . 615–622 . 10.1038/nbt.2596 . 23770639 . free. http://collaborations.gis.a-star.edu.sg/~cmb6/dfilter/
  10. Wong . Ka-Chun . etal. SignalSpider: probabilistic pattern discovery on multiple normalized ChIP-Seq signal profiles . Bioinformatics . 2014 . 31 . 1 . 17–24 . 10.1093/bioinformatics/btu604 . 25192742. free .
  11. Madrigal . Pedro . Identification of Transcription Factor Binding Sites in ChIP-exo using R/Bioconductor . Epigenesys Bioinformatics Protocols . 2015 . 68 .
  12. Nooranikhojasteh A, Tavallaee G, Orouji E . Benchmarking peak calling methods for CUT&RUN . Bioinformatics . 41 . 7 . btaf375 . July 2025 . 40569178 . 12255880 . 10.1093/bioinformatics/btaf375 . free .
  13. Keles. Liang. Detecting differential binding of transcription factors with ChIP-seq. Bioinformatics. 26 October 2011. 10.1093/bioinformatics/btr605. 22057161. 28. 1. 121–122. 3244766.
  14. Waxman. Shao. Zhang . Yuan . Orkin . MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets. Genome Biology. 16 March 2012. 13. 3. 10.1186/gb-2012-13-3-r16 . R16 . 22424423 . 3439967 . free .
  15. Xu. Sung. Wei . Lin . An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data. Bioinformatics. 28 July 2008. 24. 20. 10.1093/bioinformatics/btn402. 2344–2349. 18667444. free.
  16. Allhoff. Costa. Sere. Chauvistre. Lin. Zenke. Detecting differential peaks in ChIP-seq signals with ODIN. Bioinformatics. 24 October 2014. 30. 24. 3467–3475. 10.1093/bioinformatics/btu722. 25371479.
  17. Holmqvist E, Wright PR, Li L, Bischler T, Barquist L, Reinhardt R, Backofen R, Vogel J . Global RNA recognition patterns of post-transcriptional regulators Hfq and CsrA revealed by UV crosslinking in vivo. . EMBO J . 2016 . 27044921 . 10.15252/embj.201593360 . 35 . 9 . 991–1011. 5207318 .
  18. Nooranikhojasteh . Amin . Tavallaee . Ghazaleh . Orouji . Elias . 2025-07-01 . Benchmarking peak calling methods for CUT&RUN . Bioinformatics . 41 . 7 . btaf375 . 10.1093/bioinformatics/btaf375 . 1367-4811 . 12255880 . 40569178 .
  19. Stovner . Endre Bakken . Sætrom . Pål . March 28, 2019 . epic2 efficiently finds diffuse domains in ChIP-seq data . Bioinformatics . Oxford University Press (OUP) . 35 . 21 . 4392–4393 . 10.1093/bioinformatics/btz232 . 30923821 . 1367-4803.
  20. Web site: Homer Software and Data Download . December 22, 2025 . homer.ucsd.edu.
  21. Wenz . Brandon M. . 10.1101/2024.09.04.610850 . He . Yuan . Chen . Nae-Chyun . Pickrell . Joseph K. . Li . Jeremiah H. . Dudek . Max F. . Li . Taibo . Keener . Rebecca . Voight . Benjamin F. . Brown . Christopher D. . Battle . Alexis . Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms . Genome Biology . 2025 . 26 . 81 . 10.1186/s13059-025-03538-1 . free . 40159496 .
  22. Zhang . Yanxiao . Lin . Yu-Hsuan . Johnson . Timothy D. . Rozek . Laura S. . Sartor . Maureen A. . 2014-09-15 . PePr: a peak-calling prioritization pipeline to identify consistent or differential peaks from replicated ChIP-Seq data . Bioinformatics (Oxford, England) . 30 . 18 . 2568–2575 . 10.1093/bioinformatics/btu372 . 24894502 . 4155259 .
  23. Hentges . Lance D. . Sergeant . Martin J. . Downes . Damien J. . Hughes . Jim R. . Taylor . Stephen . 2021-01-27 . LanceOtron: a deep learning peak caller for ATAC-seq, ChIP-seq, and DNase-seq . 10.1101/2021.01.25.428108 .
  24. Meers . Michael P. . Tenenbaum . Dan . Henikoff . Steven . 2019-07-12 . Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling . Epigenetics & Chromatin . 12 . 1 . 42 . 10.1186/s13072-019-0287-4 . free . 31300027 . 6624997 . en.
  25. Yashar . William M . Kong . Garth . VanCampen . Jake . Smith . Brittany M . Coleman . Daniel J . Carbone . Lucia . Yardimci . Galip Gürkan . Maxson . Julia E . Braun . Theodore P . 2022-01-12 . GoPeaks: Histone Modification Peak Calling for CUT&Tag . 10.1101/2022.01.10.475735 .

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