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...
| Main Authors: | , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
BioMed Central Ltd
2010
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| Online Access: | http://hdl.handle.net/1721.1/59206 |
| _version_ | 1826213119196659712 |
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| author | Zhang, Yong Liu, Tao Meyer, Clifford A. Eeckhoute, Jerome Johnson, David S. Nusbaum, Chad Myers, Richard M. Brown, Myles Li, Wei Liu, Xiaole S. Bernstein, Bradley E. |
| author2 | Broad Institute of MIT and Harvard |
| author_facet | Broad Institute of MIT and Harvard Zhang, Yong Liu, Tao Meyer, Clifford A. Eeckhoute, Jerome Johnson, David S. Nusbaum, Chad Myers, Richard M. Brown, Myles Li, Wei Liu, Xiaole S. Bernstein, Bradley E. |
| author_sort | Zhang, Yong |
| collection | MIT |
| 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. |
| first_indexed | 2024-09-23T15:43:37Z |
| format | Article |
| id | mit-1721.1/59206 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:43:37Z |
| publishDate | 2010 |
| publisher | BioMed Central Ltd |
| record_format | dspace |
| spelling | mit-1721.1/592062022-10-02T03:39:41Z Model-based Analysis of ChIP-Seq (MACS) Zhang, Yong Liu, Tao Meyer, Clifford A. Eeckhoute, Jerome Johnson, David S. Nusbaum, Chad Myers, Richard M. Brown, Myles Li, Wei Liu, Xiaole S. Bernstein, Bradley E. Broad Institute of MIT and Harvard Bernstein, Bradley E. Nusbaum, Chad 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. National Institutes of Health (U.S) ( grant HG004069 ) National Institutes of Health (U.S) (grant HG004270) National Institutes of Health (U.S) (grant DK07496 ) 2010-10-12T18:26:40Z 2010-10-12T18:26:40Z 2008-09 2008-09 2010-09-03T16:13:51Z Article http://purl.org/eprint/type/JournalArticle 1465-6906 http://hdl.handle.net/1721.1/59206 Genome Biology. 2008 Sep 17;9(9):R137 18798982 en http://dx.doi.org/10.1186/gb-2008-9-9-r137 Genome Biology Creative Commons Attribution http://creativecommons.org/licenses/by/2.0 Zhang et al.; licensee BioMed Central Ltd. application/pdf BioMed Central Ltd BioMed Central Ltd |
| spellingShingle | Zhang, Yong Liu, Tao Meyer, Clifford A. Eeckhoute, Jerome Johnson, David S. Nusbaum, Chad Myers, Richard M. Brown, Myles Li, Wei Liu, Xiaole S. Bernstein, Bradley E. 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 |
| url | http://hdl.handle.net/1721.1/59206 |
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