ArticleNucleic acids research2010
Inclusion of neighboring base interdependencies substantially improves genome-wide prokaryotic transcription factor binding site prediction.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 26 citations in OpenAlex.
- Bioprospecting Chromobacterium violaceum for bioremediation: an alternative to environmental lead pollution.World journal of microbiology & biotechnology · 2026Article
- BML: a versatile web server for bipartite motif discovery.Briefings in bioinformatics · 2022Article
- DIpartite: A tool for detecting bipartite motifs by considering base interdependencies.PloS one · 2019Article
- Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies.BMC bioinformatics · 2017Article
- Parametric bootstrapping for biological sequence motifs.BMC bioinformatics · 2016Article
- Knowledge-based three-body potential for transcription factor binding site prediction.IET systems biology · 2016Article
- An affinity-structure database of helix-turn-helix: DNA complexes with a universal coordinate system.BMC bioinformatics · 2015Article
- Varying levels of complexity in transcription factor binding motifs.Nucleic acids research · 2015Article
- LASAGNA: a novel algorithm for transcription factor binding site alignment.BMC bioinformatics · 2013Article
- The next generation of transcription factor binding site prediction.PLoS computational biology · 2013Article
- Computational predictions provide insights into the biology of TAL effector target sites.PLoS computational biology · 2013Article
- Searching for transcription factor binding sites in vector spaces.BMC bioinformatics · 2012Article
- Logic minimization and rule extraction for identification of functional sites in molecular sequences.BioData mining · 2012Article
- Tree-based position weight matrix approach to model transcription factor binding site profiles.PloS one · 2011Article
- Dose-sensitivity, conserved non-coding sequences, and duplicate gene retention through multiple tetraploidies in the grasses.Frontiers in plant science · 2011Article
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2 authors at 1 institution in 1 country.
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Abstract
Prediction of transcription factor binding sites is an important challenge in genome analysis. The advent of next generation genome sequencing technologies makes the development of effective computational approaches particularly imperative. We have developed a novel training-based methodology intended for prokaryotic transcription factor binding site prediction. Our methodology extends existing models by taking into account base interdependencies between neighbouring positions using conditional probabilities and includes genomic background weighting. This has been tested against other existing and novel methodologies including position-specific weight matrices, first-order Hidden Markov Models and joint probability models. We have also tested the use of gapped and ungapped alignments and the inclusion or exclusion of background weighting. We show that our best method enhances binding site prediction for all of the 22 Escherichia coli transcription factors with at least 20 known binding sites, with many showing substantial improvements. We highlight the advantage of using block alignments of binding sites over gapped alignments to capture neighbouring position interdependencies. We also show that combining these methods with ChIP-on-chip data has the potential to further improve binding site prediction. Finally we have developed the ungapped likelihood under positional background platform: a user friendly website that gives access to the prediction method devised in this work.
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