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NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data
BACKGROUND: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) can locate transcription factor binding sites on genomic scale. Although many models and programs are available to call peaks, none has dominated its competition in comparison studies. RESULTS: We propose a r...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3672025/ https://www.ncbi.nlm.nih.gov/pubmed/23706083 http://dx.doi.org/10.1186/1471-2164-14-349 |
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author | Kim, Nak-Kyeong Jayatillake, Rasika V Spouge, John L |
author_facet | Kim, Nak-Kyeong Jayatillake, Rasika V Spouge, John L |
author_sort | Kim, Nak-Kyeong |
collection | PubMed |
description | BACKGROUND: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) can locate transcription factor binding sites on genomic scale. Although many models and programs are available to call peaks, none has dominated its competition in comparison studies. RESULTS: We propose a rigorous statistical model, the normal-exponential two-peak (NEXT-peak) model, which parallels the physical processes generating the empirical data, and which can naturally incorporate mappability information. The model therefore estimates total strength of binding (even if some binding locations do not map uniquely into a reference genome, effectively censoring them); it also assigns an error to an estimated binding location. The comparison study with existing programs on real ChIP-seq datasets (STAT1, NRSF, and ZNF143) demonstrates that the NEXT-peak model performs well both in calling peaks and locating them. The model also provides a goodness-of-fit test, to screen out spurious peaks and to infer multiple binding events in a region. CONCLUSIONS: The NEXT-peak program calls peaks on any test dataset about as accurately as any other, but provides unusual accuracy in the estimated location of the peaks it calls. NEXT-peak is based on rigorous statistics, so its model also provides a principled foundation for a more elaborate statistical analysis of ChIP-seq data. |
format | Online Article Text |
id | pubmed-3672025 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-36720252013-06-10 NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data Kim, Nak-Kyeong Jayatillake, Rasika V Spouge, John L BMC Genomics Methodology Article BACKGROUND: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) can locate transcription factor binding sites on genomic scale. Although many models and programs are available to call peaks, none has dominated its competition in comparison studies. RESULTS: We propose a rigorous statistical model, the normal-exponential two-peak (NEXT-peak) model, which parallels the physical processes generating the empirical data, and which can naturally incorporate mappability information. The model therefore estimates total strength of binding (even if some binding locations do not map uniquely into a reference genome, effectively censoring them); it also assigns an error to an estimated binding location. The comparison study with existing programs on real ChIP-seq datasets (STAT1, NRSF, and ZNF143) demonstrates that the NEXT-peak model performs well both in calling peaks and locating them. The model also provides a goodness-of-fit test, to screen out spurious peaks and to infer multiple binding events in a region. CONCLUSIONS: The NEXT-peak program calls peaks on any test dataset about as accurately as any other, but provides unusual accuracy in the estimated location of the peaks it calls. NEXT-peak is based on rigorous statistics, so its model also provides a principled foundation for a more elaborate statistical analysis of ChIP-seq data. BioMed Central 2013-05-25 /pmc/articles/PMC3672025/ /pubmed/23706083 http://dx.doi.org/10.1186/1471-2164-14-349 Text en Copyright © 2013 Kim et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Kim, Nak-Kyeong Jayatillake, Rasika V Spouge, John L NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data |
title | NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data |
title_full | NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data |
title_fullStr | NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data |
title_full_unstemmed | NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data |
title_short | NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data |
title_sort | next-peak: a normal-exponential two-peak model for peak-calling in chip-seq data |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3672025/ https://www.ncbi.nlm.nih.gov/pubmed/23706083 http://dx.doi.org/10.1186/1471-2164-14-349 |
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