List of peak-calling software
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 | This article includes a list of general references, but it lacks sufficient corresponding inline citations. (December 2025) |
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]
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 |
Notes
[edit | edit source]- p Published as pre-print
References
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