{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Guo Y"],"funding":["NCRR NIH HHS","NHGRI NIH HHS","NINDS NIH HHS"],"pagination":["3028-34"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC2995123"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["26(24)"],"pubmed_abstract":["<h4>Motivation</h4>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.<h4>Results</h4>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.<h4>Availability</h4>http://cgs.csail.mit.edu/gps."],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["Discovering homotypic binding events at high spatial resolution."],"pmcid":["PMC2995123"],"funding_grant_id":["P01 NS055923","P01-NS055923","UL1 RR024920","1-UL1-RR024920","5R01HG002668","R01 HG002668"],"pubmed_authors":["Altshuler RC","Papachristoudis G","Guo Y","Mahony S","Gifford DK","Jaakkola TS","Gerber GK"],"additional_accession":[]},"is_claimable":false,"name":"Discovering homotypic binding events at high spatial resolution.","description":"<h4>Motivation</h4>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.<h4>Results</h4>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.<h4>Availability</h4>http://cgs.csail.mit.edu/gps.","dates":{"release":"2010-01-01T00:00:00Z","publication":"2010 Dec","modification":"2024-11-21T00:33:11.067Z","creation":"2019-03-27T00:37:03Z"},"accession":"S-EPMC2995123","cross_references":{"pubmed":["20966006"],"doi":["10.1093/bioinformatics/btq590"]}}