The value of position-specific priors in motif discovery using MEME

Abstract

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.

Figure 1

Comparison of motifs found in mouse ChIP-seq datasets. The figure shows the motifs reported by Chen et al. [11] and those found by MEME in sequences identified as bound to the given transcription factor in 13 ChIP-seq experiments. The MEME motifs were found using 100 randomly chosen bound sequences and the OOPS- prior. The inter-motif distance (scaled Euclidean distance) is computed as described in Additional file 1.