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

@article{citeulike:1769635, abstract = {BACKGROUND:Motif 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.RESULTS:We 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 problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these 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.CONCLUSIONS:Using 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. Source code in C for the stand-alone program DEME is free for academic use and is available at: http://bioinformatics.org.au/deme/}, author = {Redhead, Emma and Bailey, Timothy }, citeulike-article-id = {1769635}, doi = {10.1186/1471-2105-8-385}, journal = {BMC Bioinformatics}, keywords = {dan\_presented, eisen\_journal\_club, motif\_detection, prediction, round\_robin}, number = {1}, posted-at = {2007-10-25 01:20:26}, priority = {3}, title = {Discriminative motif discovery in DNA and protein sequences using the DEME Algorithm}, url = {http://dx.doi.org/10.1186/1471-2105-8-385}, volume = {8}, year = {2007} }

TY - JOUR ID - citeulike:1769635 L3 - citeulike-article-id:1769635 TI - Discriminative motif discovery in DNA and protein sequences using the DEME Algorithm JF - BMC Bioinformatics VL - 8 IS - 1 N2 - BACKGROUND:Motif 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.RESULTS:We 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 problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these 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.CONCLUSIONS:Using 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. Source code in C for the stand-alone program DEME is free for academic use and is available at: http://bioinformatics.org.au/deme/ KW - dan_presented KW - eisen_journal_club KW - motif_detection KW - prediction KW - round_robin AU - Redhead, Emma AU - Bailey, Timothy PY - 2007/// UR - http://dx.doi.org/10.1186/1471-2105-8-385 ER -