ArticlePLoS computational biology2011
De-novo discovery of differentially abundant transcription factor binding sites including their positional preference.
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 27 papers.
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27 citing papers in PubMed.
- Transcription Factors in Plant Gene Expression Regulation.International journal of molecular sciences · 2025Article
- Identification of a 301 bp promoter core region of the SrUGT91D2 gene from Stevia rebaudiana that contributes to hormone and abiotic stress inducibility.BMC plant biology · 2024Article
- Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data.NAR genomics and bioinformatics · 2024Article
- Methyl Jasmonate Activates theMolecules (Basel, Switzerland) · 2022Article
- Identification of Two Auxin-Regulated Potassium Transporters Involved in Seed Maturation.International journal of molecular sciences · 2018Article
- Responses to auxin signals: an operating principle for dynamical sensitivity yet high resilience.Royal Society open science · 2018Article
- Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies.BMC bioinformatics · 2017Article
- The orientation of transcription factor binding site motifs in gene promoter regions: does it matter?BMC genomics · 2016Article
- Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data.BMC bioinformatics · 2015Article
- Varying levels of complexity in transcription factor binding motifs.Nucleic acids research · 2015Article
- Binding site discovery from nucleic acid sequences by discriminative learning of hidden Markov models.Nucleic acids research · 2014Article
- Progressive promoter element combinations classify conserved orthogonal plant circadian gene expression modules.Journal of the Royal Society, Interface · 2014Article
- RECLU: a pipeline to discover reproducible transcriptional start sites and their alternative regulation using capped analysis of gene expression (CAGE).BMC genomics · 2014Article
- Effective automated feature construction and selection for classification of biological sequences.PloS one · 2014Article
- Article
- Computational analysis of auxin responsive elements in the Arabidopsis thaliana L. genome.BMC genomics · 2014Article
- A general approach for discriminative de novo motif discovery from high-throughput data.Nucleic acids research · 2013Article
- Using an ensemble of statistical metrics to quantify large sets of plant transcription factor binding sites.Plant methods · 2013Article
- Evaluation of methods for modeling transcription factor sequence specificity.Nature biotechnology · 2013Article
- DLocalMotif: a discriminative approach for discovering local motifs in protein sequences.Bioinformatics (Oxford, England) · 2013Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Transcription factors are a main component of gene regulation as they activate or repress gene expression by binding to specific binding sites in promoters. The de-novo discovery of transcription factor binding sites in target regions obtained by wet-lab experiments is a challenging problem in computational biology, which has not been fully solved yet. Here, we present a de-novo motif discovery tool called Dispom for finding differentially abundant transcription factor binding sites that models existing positional preferences of binding sites and adjusts the length of the motif in the learning process. Evaluating Dispom, we find that its prediction performance is superior to existing tools for de-novo motif discovery for 18 benchmark data sets with planted binding sites, and for a metazoan compendium based on experimental data from micro-array, ChIP-chip, ChIP-DSL, and DamID as well as Gene Ontology data. Finally, we apply Dispom to find binding sites differentially abundant in promoters of auxin-responsive genes extracted from Arabidopsis thaliana microarray data, and we find a motif that can be interpreted as a refined auxin responsive element predominately positioned in the 250-bp region upstream of the transcription start site. Using an independent data set of auxin-responsive genes, we find in genome-wide predictions that the refined motif is more specific for auxin-responsive genes than the canonical auxin-responsive element. In general, Dispom can be used to find differentially abundant motifs in sequences of any origin. However, the positional distribution learned by Dispom is especially beneficial if all sequences are aligned to some anchor point like the transcription start site in case of promoter sequences. We demonstrate that the combination of searching for differentially abundant motifs and inferring a position distribution from the data is beneficial for de-novo motif discovery. Hence, we make the tool freely available as a component of the open-source Java framework Jstacs and as a stand-alone application at http://www.jstacs.de/index.php/Dispom.
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