ArticleNucleic acids research2006
A graph-based motif detection algorithm models complex nucleotide dependencies in transcription factor binding sites.
Article in Nucleic acids research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 24 citations in OpenAlex.
- Sequence information gain based motif analysis.BMC bioinformatics · 2015Article
- Characterization and functional analysis of eugenol O-methyltransferase gene reveal metabolite shifts, chemotype specific differential expression and developmental regulation in Ocimum tenuiflorum L.Molecular biology reports · 2014Article
- D-Light on promoters: a client-server system for the analysis and visualization of cis-regulatory elements.BMC bioinformatics · 2013Article
- Improved predictions of transcription factor binding sites using physicochemical features of DNA.Nucleic acids research · 2012Article
- Defining the DNA uptake specificity of naturally competent Haemophilus influenzae cells.Nucleic acids research · 2012Article
- A flexible integrative approach based on random forest improves prediction of transcription factor binding sites.Nucleic acids research · 2012Article
- A ChIP-Seq benchmark shows that sequence conservation mainly improves detection of strong transcription factor binding sites.PloS one · 2011Article
- Learning "graph-mer" motifs that predict gene expression trajectories in development.PLoS computational biology · 2010Article
- DISCOVER: a feature-based discriminative method for motif search in complex genomes.Bioinformatics (Oxford, England) · 2009Article
- Reliable prediction of regulator targets using 12 Drosophila genomes.Genome research · 2007Article
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4 authors at 1 institution in 1 country.
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Abstract
Given a set of known binding sites for a specific transcription factor, it is possible to build a model of the transcription factor binding site, usually called a motif model, and use this model to search for other sites that bind the same transcription factor. Typically, this search is performed using a position-specific scoring matrix (PSSM), also known as a position weight matrix. In this paper we analyze a set of eukaryotic transcription factor binding sites and show that there is extensive clustering of similar k-mers in eukaryotic motifs, owing to both functional and evolutionary constraints. The apparent limitations of probabilistic models in representing complex nucleotide dependencies lead us to a graph-based representation of motifs. When deciding whether a candidate k-mer is part of a motif or not, we base our decision not on how well the k-mer conforms to a model of the motif as a whole, but how similar it is to specific, known k-mers in the motif. We elucidate the reasons why we expect graph-based methods to perform well on motif data. Our MotifScan algorithm shows greatly improved performance over the prevalent PSSM-based method for the detection of eukaryotic motifs.
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