Discriminative motif discovery in DNA and protein sequences using the DEME algorithm

Emma Redhead,Timothy L Bailey

doi:10.1186/1471-2105-8-385

Abstract

BackgroundMotif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to increase the sensitivity and selectivity of motif discovery by utilizing a second set of sequences, and searching only for patterns that can differentiate the two sets of sequences. Potential applications of discriminative motif discovery include discovering transcription factor binding site motifs in ChIP-chip data and finding protein motifs involved in thermal stability using sets of orthologous proteins from thermophilic and mesophilic organisms.ResultsWe describe DEME, a discriminative motif discovery algorithm for use with protein and DNA sequences. Input to DEME is two sets of sequences; a "positive" set and a "negative" set. DEME represents motifs using a probabilistic model, and uses a novel combination of global and local search to find the motif that optimally discriminates between the two sets of sequences. DEME is unique among discriminative motif finders in that it uses an informative Bayesian prior on protein motif columns, allowing it to incorporate prior knowledge of residue characteristics. We also introduce four, synthetic, discriminative motif discovery problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these synthetic problems and on two biological problems: finding yeast transcription factor binding motifs in ChIP-chip data, and finding motifs that discriminate between groups of thermophilic and mesophilic orthologous proteins.ConclusionUsing artificial data, we show that DEME is more effective than a non-discriminative approach when there are "decoy" motifs or when a variant of the motif is present in the "negative" sequences. With real data, we show that DEME is as good, but not better than non-discriminative algorithms at discovering yeast transcription factor binding motifs. We also show that DEME can find highly informative thermal-stability protein motifs. Binaries for the stand-alone program DEME is free for academic use and is available at

Highlights

Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences
We have shown using real and artificial datasets that DEME is very well suited to finding discriminative motifs in situations where decoy motifs are present in the positive and negative datasets, or when variant motifs are present in the negative dataset
We have demonstrated that the use of a Bayesian motif prior, made possible by a novel reparameterizing of the SharanSegal objective function, can give superior accuracy in DNA motif discovery contexts

Summary

Introduction

Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Since motifs are usually short and can be highly variable sequence patterns [1], a challenging problem for motif discovery algorithms is to distinguish functional motifs from random patterns that are over-represented by chance. Discriminative motif discovery attempts to find motifs that occur more frequently in one set of sequences compared to another set This can help with the problem of distinguishing functional motifs from randomly occurring sequence patterns, because the negative set of sequences may be a better representation of "random" sequences than can be captured in a probabilistic model. In order to discover the TFBS motif of a transcription factor (TF), the set of DNA probes from a ChIP-chip [4,5,6] or DIP-chip [7] experiment that do not bind to the TF can be used as the negative sequence set.

Methods

Results

Conclusion