ArticleNucleic acids research2011
Theoretical and empirical quality assessment of transcription factor-binding motifs.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed, 72 citations in OpenAlex.
- Computational inference of Rhizobium phaseoli transcriptional regulatory network predicts Transcription Factors involved in nodulation.Briefings in functional genomics · 2025Article
- The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes.BMC bioinformatics · 2024Article
- Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity inNAR genomics and bioinformatics · 2024Article
- RSAT 2022: regulatory sequence analysis tools.Nucleic acids research · 2022Article
- RegulonDB 11.0: Comprehensive high-throughput datasets on transcriptional regulation inMicrobial genomics · 2022Article
- The local transcriptional regulators SacR1 and SacR2 act as repressors of fructooligosaccharides metabolism in Lactobacillus plantarum.Microbial cell factories · 2020Article
- RhizoBindingSites, a Database of DNA-Binding Motifs in Nitrogen-Fixing Bacteria Inferred Using a Footprint Discovery Approach.Frontiers in microbiology · 2020Article
- RSAT variation-tools: An accessible and flexible framework to predict the impact of regulatory variants on transcription factor binding.Computational and structural biotechnology journal · 2019Article
- Regulatory motifs found in the small heat shock protein (sHSP) gene family in tomato.BMC genomics · 2018Article
- A unified resource for transcriptional regulation in Escherichia coli K-12 incorporating high-throughput-generated binding data into RegulonDB version 10.0.BMC biology · 2018Article
- RSAT 2018: regulatory sequence analysis tools 20th anniversary.Nucleic acids research · 2018Article
- Using RegulonDB, the Escherichia coli K-12 Gene Regulatory Transcriptional Network Database.Current protocols in bioinformatics · 2018Article
- Energetic funnel facilitates facilitated diffusion.Nucleic acids research · 2018Article
- HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis.Nucleic acids research · 2018Article
- CcpA-Dependent Carbon Catabolite Repression Regulates Fructooligosaccharides Metabolism inFrontiers in microbiology · 2018Article
- RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections.Nucleic acids research · 2017Article
- SalmoNet, an integrated network of tenNPJ systems biology and applications · 2017Article
- Most of the tight positional conservation of transcription factor binding sites near the transcription start site reflects their co-localization within regulatory modules.BMC bioinformatics · 2016Article
- Evaluating tools for transcription factor binding site prediction.BMC bioinformatics · 2016Article
- Sequences flanking the core-binding site modulate glucocorticoid receptor structure and activity.Nature communications · 2016Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 4 countries.
Funding
Abstract
Position-specific scoring matrices (PSSMs) are routinely used to predict transcription factor (TF)-binding sites in genome sequences. However, their reliability to predict novel binding sites can be far from optimum, due to the use of a small number of training sites or the inappropriate choice of parameters when building the matrix or when scanning sequences with it. Measures of matrix quality such as E-value and information content rely on theoretical models, and may fail in the context of full genome sequences. We propose a method, implemented in the program 'matrix-quality', that combines theoretical and empirical score distributions to assess reliability of PSSMs for predicting TF-binding sites. We applied 'matrix-quality' to estimate the predictive capacity of matrices for bacterial, yeast and mouse TFs. The evaluation of matrices from RegulonDB revealed some poorly predictive motifs, and allowed us to quantify the improvements obtained by applying multi-genome motif discovery. Interestingly, the method reveals differences between global and specific regulators. It also highlights the enrichment of binding sites in sequence sets obtained from high-throughput ChIP-chip (bacterial and yeast TFs), and ChIP-seq and experiments (mouse TFs). The method presented here has many applications, including: selecting reliable motifs before scanning sequences; improving motif collections in TFs databases; evaluating motifs discovered using high-throughput data sets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.