Peak calling

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
MACS 2021 (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-Clause 3.0.3 (Feb 20, 2025)

2.2.9.1 (Dec 2023)

Yes [19]
SICER 2019 (SICER2)

2009

Chongzhi 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 enrichment MIT License 1.0.2 (Feb 21, 2020) No [20]
epic2 2019 Johannes Dröge, Johannes Alneberg, et al. A reimplementation of the SICER algorithm focused on improving performance (speed, memory usage) for identifying broad domains. MIT License 0.2.2 (May 2023) Yes [21]
HOMER 2010 Sven 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 Academic 4.11 (Nov 2019) No [22]
SPP (R package) 2008 Peter V. Kharchenko, Mikhail Y. Tolstorukov, Peter J. Park Uses cross-correlation analysis to estimate fragment length and identify signal peaks. It was incorporated into the ENCODE analysis pipeline. Artistic License 2.0 1.15.4 (Oct 2023 / Bioconductor 3.18) No
Genrich 2018[p] John 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 License 0.6.1 (Jun 2021) No [23]
HPeak 2010 Zhaohui S Qin, Yongqun He, Arul M Chinnaiyan, et al. Peak-finding algorithm based on a Hidden Markov Model (HMM). Free Academic Use 1.0 (?) No
JAMM 2015 Mahmoud M. Ibrahim, Scott A. Lacadie, Nikolaus Rajewsky, et al. Uses mixture model clustering of biological replicates. GPL-3.0-only 1.0.7rev6 (~2014) No
PePr 2014 Yanxiao Zhang, Maureen A. Sartor Uses a sliding window approach modeling read counts with a negative binomial distribution. Ranks identified peaks based on consistency across replicates. GPL-3.0-only 1.1.20 (Sep 2019) No [24]
LanceOtron 2022 Ross S. Harris, Nathan D. Leclair, et al. Deep learning (convolutional neural network) based peak caller. GPL-3.0-only 1.0.1 (Jun 2023) Yes [25]
SEACR 2019 Michael P. Meers, Daniel Tenenbaum, Steven Henikoff Designed 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 License 1.3 (May 2019) No [26]
GoPeaks 2021 Vincent A. Zuber, Jeffrey E. Maxson, et al. Designed for CUT&RUN and CUT&Tag datasets. MIT License 1.0.0 (Feb 2023) Yes [27]
p Published as pre-print

See also

References

  1. ^ Valouev A, et al. (September 2008). "Genome-wide analysis of transcription factor binding sites based on ChIP-seq data". Nature Methods. 5 (9): 829–834. doi:10.1038/nmeth.1246. PMC 2917543. PMID 19160518.
  2. ^ Feng, Jianxing; Liu, Tao; Qin, Bo; Zhang, Yong; Liu, Xiaole Shirley (29 August 2012). "Identifying ChIP-seq enrichment using MACS". Nature Protocols. 7 (9): 1728–1740. doi:10.1038/nprot.2012.101. PMC 3868217. PMID 22936215.
  3. ^ Wilbanks, Elizabeth G.; Facciotti, Marc T. (7 July 2010). "Evaluation of Algorithm Performance in ChIP-Seq Peak Detection". PLOS ONE. 5 (7) e11471. Bibcode:2010PLoSO...511471W. doi:10.1371/journal.pone.0011471. PMC 2900203. PMID 20628599.
  4. ^ Bailey, TL; Krajewski P; Ladunga I; Lefebvre C; Li Q; Liu T; Madrigal P; Taslim C; Zhang J. (14 November 2013). "Practical guidelines for the comprehensive analysis of ChIP-seq data". PLOS Comput Biol. 9 (11) e1003326. Bibcode:2013PLSCB...9E3326B. doi:10.1371/journal.pcbi.1003326. PMC 3828144. PMID 24244136.
  5. ^ Meyer, Kate D.; Saletore, Yogesh; Zumbo, Paul; Elemento, Olivier; Mason, Christopher E.; Jaffrey, Samie R. (31 May 2012). "Comprehensive Analysis of mRNA Methylation Reveals Enrichment in 3′ UTRs and near Stop Codons". Cell. 149 (7): 1635–1646. doi:10.1016/j.cell.2012.05.003. PMC 3383396. PMID 22608085.
  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 (28 April 2012). "Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq". Nature. 485 (7397): 201–206. Bibcode:2012Natur.485..201D. doi:10.1038/nature11112. PMID 22575960. S2CID 3517716.
  7. ^ Meng, J.; Cui, X.; Rao, M. K.; Chen, Y.; Huang, Y. (14 April 2013). "Exome-based analysis for RNA epigenome sequencing data". Bioinformatics. 29 (12): 1565–1567. doi:10.1093/bioinformatics/btt171. PMC 3673212. PMID 23589649.
  8. ^ Koohy, Hashem; Down, Thomas A.; Spivakov, Mikhail; Hubbard, Tim; Helmer-Citterich, Manuela (8 May 2014). "A Comparison of Peak Callers Used for DNase-Seq Data". PLOS ONE. 9 (5) e96303. Bibcode:2014PLoSO...996303K. doi:10.1371/journal.pone.0096303. PMC 4014496. PMID 24810143.
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  10. ^ Wong, Ka-Chun; et al. (2014). "SignalSpider: probabilistic pattern discovery on multiple normalized ChIP-Seq signal profiles". Bioinformatics. 31 (1): 17–24. doi:10.1093/bioinformatics/btu604. PMID 25192742.
  11. ^ Madrigal, Pedro (2015). "Identification of Transcription Factor Binding Sites in ChIP-exo using R/Bioconductor". Epigenesys Bioinformatics Protocols. 68.
  12. ^ Nooranikhojasteh A, Tavallaee G, Orouji E (July 2025). "Benchmarking peak calling methods for CUT&RUN". Bioinformatics. 41 (7) btaf375. doi:10.1093/bioinformatics/btaf375. PMC 12255880. PMID 40569178.
  13. ^ Keles, Liang (26 October 2011). "Detecting differential binding of transcription factors with ChIP-seq". Bioinformatics. 28 (1): 121–122. doi:10.1093/bioinformatics/btr605. PMC 3244766. PMID 22057161.
  14. ^ Waxman, Shao; Zhang; Yuan; Orkin (16 March 2012). "MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets". Genome Biology. 13 (3): R16. doi:10.1186/gb-2012-13-3-r16. PMC 3439967. PMID 22424423.
  15. ^ Xu, Sung; Wei; Lin (28 July 2008). "An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data". Bioinformatics. 24 (20): 2344–2349. doi:10.1093/bioinformatics/btn402. PMID 18667444.
  16. ^ Allhoff, Costa; Sere; Chauvistre; Lin; Zenke (24 October 2014). "Detecting differential peaks in ChIP-seq signals with ODIN". Bioinformatics. 30 (24): 3467–3475. doi:10.1093/bioinformatics/btu722. PMID 25371479.
  17. ^ Holmqvist E, Wright PR, Li L, Bischler T, Barquist L, Reinhardt R, Backofen R, Vogel J (2016). "Global RNA recognition patterns of post-transcriptional regulators Hfq and CsrA revealed by UV crosslinking in vivo". EMBO J. 35 (9): 991–1011. doi:10.15252/embj.201593360. PMC 5207318. PMID 27044921.
  18. ^ Nooranikhojasteh, Amin; Tavallaee, Ghazaleh; Orouji, Elias (2025-07-01). "Benchmarking peak calling methods for CUT&RUN". Bioinformatics. 41 (7) btaf375. doi:10.1093/bioinformatics/btaf375. ISSN 1367-4811. PMC 12255880. PMID 40569178.
  19. ^ macs3-project/MACS, MACS3 project team, 2025-05-16, retrieved 2025-05-19
  20. ^ UVA, Zang Lab @ (2025-03-15), zanglab/SICER2, retrieved 2025-05-19
  21. ^ Stovner, Endre Bakken; Sætrom, Pål (March 28, 2019). "epic2 efficiently finds diffuse domains in ChIP-seq data". Bioinformatics. 35 (21). Oxford University Press (OUP): 4392–4393. doi:10.1093/bioinformatics/btz232. ISSN 1367-4803. PMID 30923821.
  22. ^ "Homer Software and Data Download". homer.ucsd.edu. Retrieved December 22, 2025.
  23. ^ Wenz, Brandon M.; 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 (2025). "Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms". Genome Biology. 26 81. bioRxiv 10.1101/2024.09.04.610850. doi:10.1186/s13059-025-03538-1. PMC 11956263. PMID 40159496.
  24. ^ 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. doi:10.1093/bioinformatics/btu372. PMC 4155259. PMID 24894502.
  25. ^ 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". bioRxiv 10.1101/2021.01.25.428108.
  26. ^ 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. doi:10.1186/s13072-019-0287-4. PMC 6624997. PMID 31300027.
  27. ^ 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". bioRxiv 10.1101/2022.01.10.475735.
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