Peak calling

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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.

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[10] and MAnorm[11] 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[12] and ODIN.[13]

See also[edit]


  1. ^ Valouev A et al. (September 2008). "Genome-wide analysis of transcription factor binding sites based on ChIP-seq data". Nature Methods 6 (5): 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. 
  3. ^ Wilbanks, Elizabeth G.; Facciotti, Marc T.; Veenstra, Gert Jan C. (7 July 2010). "Evaluation of Algorithm Performance in ChIP-Seq Peak Detection". PLoS ONE 5 (7): e11471. doi:10.1371/journal.pone.0011471. 
  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. doi:10.1371/journal.pcbi.1003326. 
  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. doi:10.1038/nature11112. PMID 22575960. 
  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. 
  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. doi:10.1371/journal.pone.0096303. 
  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. ^ Keles, Liang (26 October 2011). "Detecting differential binding of transcription factors with ChIP-seq". Bioinformatics. doi:10.1093/bioinformatics/btr605. 
  11. ^ Waxman, Shao; Zhang; Yuan; Orkin (16 March 2012). "MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets". Genome Biology 13 (3). doi:10.1186/gb-2012-13-3-r16. 
  12. ^ Xu, Sung; Wei; Lin (28 July 2008). "An HMM approach to genome-wide identification of differential histone modification sites from ChIP-seq data". Bioinformatics 20 (24). doi:10.1093/bioinformatics/btn402. 
  13. ^ 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.