Discovering homotypic binding events at high spatial resolution.
BACKGROUND
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.


BACKGROUND
http://cgs.csail.mit.edu/gps.
MIT Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.
Yuchun
Y
Guo
Georgios
G
Papachristoudis
Robert C
RC
Altshuler
Georg K
GK
Gerber
Tommi S
TS
Jaakkola
David K
DK
Gifford
Shaun
S
Mahony
D000465:Algorithms; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D004268:DNA-Binding Proteins / Q000378:metabolism*; D016678:Genome; D015233:Models, Statistical; D017422:Sequence Analysis, DNA; D014157:Transcription Factors / Q000378:metabolism
D004268:DNA-Binding Proteins; D014157:Transcription Factors