Discovering homotypic binding events at high spatial resolution

• Published in: Bioinformatics 2010-12, Vol.26 (24), p.3028-3034
• Main Authors: Guo, Yuchun, Papachristoudis, Georgios, Altshuler, Robert C., Gerber, Georg K., Jaakkola, Tommi S., Gifford, David K., Mahony, Shaun
• Format: Article
• Language: English
• Subjects: Algorithms; Binding Sites; Biological and medical sciences; Chromatin Immunoprecipitation - methods; DNA-Binding Proteins - metabolism; Fundamental and applied biological sciences. Psychology; General aspects; Genome; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Statistical; Original Papers; Sequence Analysis, DNA; Transcription Factors - metabolism
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btq590

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 Contact: [e-mail]; [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.