Document Detail


Discovering homotypic binding events at high spatial resolution.
MedLine Citation:
PMID:  20966006     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
MOTIVATION: Clusters of protein-DNA interaction events involving the same transcription factor are known to act as key components of invertebrate and mammalian promoters and enhancers. However, detecting closely spaced homotypic events from ChIP-Seq data is challenging because random variation in the ChIP fragmentation process obscures event locations.
RESULTS: The Genome Positioning System (GPS) can predict protein-DNA interaction events at high spatial resolution from ChIP-Seq data, while retaining the ability to resolve closely spaced events that appear as a single cluster of reads. GPS models observed reads using a complexity penalized mixture model and efficiently predicts event locations with a segmented EM algorithm. An optional mode permits GPS to align common events across distinct experiments. GPS detects more joint events in synthetic and actual ChIP-Seq data and has superior spatial resolution when compared with other methods. In addition, the specificity and sensitivity of GPS are superior to or comparable with other methods.
AVAILABILITY: http://cgs.csail.mit.edu/gps.
Authors:
Yuchun Guo; Georgios Papachristoudis; Robert C Altshuler; Georg K Gerber; Tommi S Jaakkola; David K Gifford; Shaun Mahony
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2010-10-21
Journal Detail:
Title:  Bioinformatics (Oxford, England)     Volume:  26     ISSN:  1367-4811     ISO Abbreviation:  Bioinformatics     Publication Date:  2010 Dec 
Date Detail:
Created Date:  2010-12-02     Completed Date:  2011-03-17     Revised Date:  2013-07-03    
Medline Journal Info:
Nlm Unique ID:  9808944     Medline TA:  Bioinformatics     Country:  England    
Other Details:
Languages:  eng     Pagination:  3028-34     Citation Subset:  IM    
Affiliation:
MIT Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Binding Sites
Chromatin Immunoprecipitation / methods*
DNA-Binding Proteins / metabolism*
Genome
Models, Statistical
Sequence Analysis, DNA
Transcription Factors / metabolism
Grant Support
ID/Acronym/Agency:
1-UL1-RR024920/RR/NCRR NIH HHS; 5R01HG002668/HG/NHGRI NIH HHS; P01-NS055923/NS/NINDS NIH HHS
Chemical
Reg. No./Substance:
0/DNA-Binding Proteins; 0/Transcription Factors
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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