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.

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 optimised 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 generalised 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]

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[12] and MAnorm[13] 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,[14] ODIN.[15] and THOR. Differential peak calling can also be applied in the context of analyzing RNA-binding protein binding sites.[16]

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.
  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. doi:10.1038/nbt.2596. PMID 23770639. [1]
  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. ^ 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.
  13. ^ 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.
  14. ^ 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.
  15. ^ 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.
  16. ^ 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.


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