Journal
BIOINFORMATICS
Volume 25, Issue 18, Pages 2318-2325Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btp434
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Funding
- Russian Fund of Basic Research projects [07-04-01623, 07-04-01584]
- INTAS Project [05-1000008-8028]
- Russian Federation Agency in Science and Innovation State Contract [02.531.11.9003]
- Russian Academy of Sciences Program in Molecular and Cellular Biology [10]
- French INRIA Equipe associe MIGEC
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Motivation: Footprint data is an important source of information on transcription factor recognition motifs. However, a footprinting fragment can contain no sequences similar to known protein recognition sites. Inspection of genome fragments nearby can help to identify missing site positions. Results: Genome fragments containing footprints were supplied to a pipeline that constructed a position weight matrix ( PWM) for different motif lengths and selected the optimal PWM. Fragments were aligned with the SeSiMCMC sampler and a new heuristic algorithm, Bigfoot. Footprints with missing hits were found for similar to 50% of factors. Adding only 2 bp on both sides of a footprinting fragment recovered most hits. We automatically constructed motifs for 41 Drosophila factors. New motifs can recognize footprints with a greater sensitivity at the same false positive rate than existing models. Also we discuss possible overfitting of constructed motifs.
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