Software:List of peak-calling software

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Peak calling is a computational method to identify enriched regions of genome using sequencing data from immuno-precipitation-based DNA profiling methods such as ChIP-Seq, DNase-Seq, ATAC-seq, MeDIP-Seq, and related methods. This incomplete list includes tools that are commonly used for peak calling in bioinformatics analyses.[1]

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 [2]
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 [3]
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 [4]
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 [5]
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 [6]
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 [7]
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 [8]
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 [9]
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 [10]
p Published as pre-print

References

  1. Nooranikhojasteh, Amin; Tavallaee, Ghazaleh; Orouji, Elias (2025-07-01). "Benchmarking peak calling methods for CUT&RUN". Bioinformatics 41 (7). doi:10.1093/bioinformatics/btaf375. ISSN 1367-4811. PMID 40569178. 
  2. macs3-project/MACS, MACS3 project team, 2025-05-16, https://github.com/macs3-project/MACS, retrieved 2025-05-19 
  3. UVA, Zang Lab @ (2025-03-15), zanglab/SICER2, https://github.com/zanglab/SICER2, retrieved 2025-05-19 
  4. 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. doi:10.1093/bioinformatics/btz232. ISSN 1367-4803. PMID 30923821. 
  5. "Homer Software and Data Download". http://homer.ucsd.edu/homer/ngs/peaks.html. 
  6. Wenz, Brandon M.; He, Yuan; Chen, Nae-Chyun; Pickrell, Joseph K.; Li, Jeremiah H.; Dudek, Max F.; Li, Taibo; Keener, Rebecca et al. (2025). "Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms". Genome Biology 26. doi:10.1186/s13059-025-03538-1. PMID 40159496. 
  7. 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. PMID 24894502. 
  8. 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.
  9. Meers, Michael P.; Tenenbaum, Dan; Henikoff, Steven (2019-07-12). "Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling" (in en). Epigenetics & Chromatin 12 (1). doi:10.1186/s13072-019-0287-4. PMID 31300027. 
  10. 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.