ArticleBMC bioinformatics2007
Discriminative motif discovery in DNA and protein sequences using the DEME algorithm.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed, 101 citations in OpenAlex.
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- FaSTPACE: a fast and scalable tool for peptide alignment and consensus extraction.NAR genomics and bioinformatics · 2024Article
- Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models.Nucleic acids research · 2023Article
- GLTM: A Global-Local Attention LSTM Model to Locate Dimer Motif of Single-Pass Membrane Proteins.Frontiers in genetics · 2022Article
- Monocyte chemoattractant protein-induced protein 1 directly degrades viral miRNAs with a specific motif and inhibits KSHV infection.Nucleic acids research · 2021Article
- SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing.Bioinformatics (Oxford, England) · 2019Article
- Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX).Scientific reports · 2019Article
- AberrantGenome research · 2018Article
- WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data.Scientific reports · 2017Article
- Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies.BMC bioinformatics · 2017Article
- A common class of transcripts with 5'-intron depletion, distinct early coding sequence features, andRNA (New York, N.Y.) · 2017Article
- RefSelect: a reference sequence selection algorithm for planted (l, d) motif search.BMC bioinformatics · 2016Article
- Identification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search Space.PloS one · 2015Article
- Binding site discovery from nucleic acid sequences by discriminative learning of hidden Markov models.Nucleic acids research · 2014Article
- Identifying mRNA sequence elements for target recognition by human Argonaute proteins.Genome research · 2014Article
- Discriminative motif optimization based on perceptron training.Bioinformatics (Oxford, England) · 2014Article
- Discriminative motif analysis of high-throughput dataset.Bioinformatics (Oxford, England) · 2014Article
- An integrated model of multiple-condition ChIP-Seq data reveals predeterminants of Cdx2 binding.PLoS computational biology · 2014Article
- STEME: a robust, accurate motif finder for large data sets.PloS one · 2014Article
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2 authors at 1 institution in 1 country.
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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 http://bioinformatics.org.au/deme/
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