Model-based Analysis of ChIP-Seq (MACS)

We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dy...

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Detalles Bibliográficos
Autores principales: Zhang, Yong, Liu, Tao, Meyer, Clifford A, Eeckhoute, Jérôme, Johnson, David S, Bernstein, Bradley E, Nusbaum, Chad, Myers, Richard M, Brown, Myles, Li, Wei, Liu, X Shirley
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2592715/
https://www.ncbi.nlm.nih.gov/pubmed/18798982
http://dx.doi.org/10.1186/gb-2008-9-9-r137
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author Zhang, Yong
Liu, Tao
Meyer, Clifford A
Eeckhoute, Jérôme
Johnson, David S
Bernstein, Bradley E
Nusbaum, Chad
Myers, Richard M
Brown, Myles
Li, Wei
Liu, X Shirley
author_facet Zhang, Yong
Liu, Tao
Meyer, Clifford A
Eeckhoute, Jérôme
Johnson, David S
Bernstein, Bradley E
Nusbaum, Chad
Myers, Richard M
Brown, Myles
Li, Wei
Liu, X Shirley
author_sort Zhang, Yong
collection PubMed
description We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.
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spelling pubmed-25927152008-12-03 Model-based Analysis of ChIP-Seq (MACS) Zhang, Yong Liu, Tao Meyer, Clifford A Eeckhoute, Jérôme Johnson, David S Bernstein, Bradley E Nusbaum, Chad Myers, Richard M Brown, Myles Li, Wei Liu, X Shirley Genome Biol Method We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available. BioMed Central 2008 2008-09-17 /pmc/articles/PMC2592715/ /pubmed/18798982 http://dx.doi.org/10.1186/gb-2008-9-9-r137 Text en Copyright ©2008 Zhang 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 Method
Zhang, Yong
Liu, Tao
Meyer, Clifford A
Eeckhoute, Jérôme
Johnson, David S
Bernstein, Bradley E
Nusbaum, Chad
Myers, Richard M
Brown, Myles
Li, Wei
Liu, X Shirley
Model-based Analysis of ChIP-Seq (MACS)
title Model-based Analysis of ChIP-Seq (MACS)
title_full Model-based Analysis of ChIP-Seq (MACS)
title_fullStr Model-based Analysis of ChIP-Seq (MACS)
title_full_unstemmed Model-based Analysis of ChIP-Seq (MACS)
title_short Model-based Analysis of ChIP-Seq (MACS)
title_sort model-based analysis of chip-seq (macs)
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2592715/
https://www.ncbi.nlm.nih.gov/pubmed/18798982
http://dx.doi.org/10.1186/gb-2008-9-9-r137
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