The value of position-specific priors in motif discovery using MEME.
BACKGROUND
Position-specific priors have been shown to be a flexible and elegant way to extend the power of Gibbs sampler-based motif discovery algorithms. Information of many types-including sequence conservation, nucleosome positioning, and negative examples-can be converted into a prior over the location of motif sites, which then guides the sequence motif discovery algorithm. This approach has been shown to confer many of the benefits of conservation-based and discriminative motif discovery approaches on Gibbs sampler-based motif discovery methods, but has not previously been studied with methods based on expectation maximization (EM).


RESULTS
We extend the popular EM-based MEME algorithm to utilize position-specific priors and demonstrate their effectiveness for discovering transcription factor (TF) motifs in yeast and mouse DNA sequences. Utilizing a discriminative, conservation-based prior dramatically improves MEME's ability to discover motifs in 156 yeast TF ChIP-chip datasets, more than doubling the number of datasets where it finds the correct motif. On these datasets, MEME using the prior has a higher success rate than eight other conservation-based motif discovery approaches. We also show that the same type of prior improves the accuracy of motifs discovered by MEME in mouse TF ChIP-seq data, and that the motifs tend to be of slightly higher quality those found by a Gibbs sampling algorithm using the same prior.


CONCLUSIONS
We conclude that using position-specific priors can substantially increase the power of EM-based motif discovery algorithms such as MEME algorithm.
Bailey
Timothy L
TL
Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Queensland, Australia. t.bailey@imb.uq.edu.au
Bodén
Mikael
M
Whitington
Tom
T
Machanick
Philip
P
D000465:Algorithms; D020816:Amino Acid Motifs; D000818:Animals; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D030562:Databases, Protein; D051379:Mice; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*
D014157:Transcription Factors