ArticleNucleic acids research2005
Computational technique for improvement of the position-weight matrices for the DNA/protein binding sites.
Article in Nucleic acids research, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 90 citations in OpenAlex.
- DNA-Protein Binding is Dominated by Short Anchoring Elements.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- WSHNN: A Weakly Supervised Hybrid Neural Network for the Identification of DNA-protein Binding Sites.Current computer-aided drug design · 2025Article
- The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes.BMC bioinformatics · 2024Article
- Investigating the sequence landscape in thePeerJ · 2023Article
- Bayesian Markov models improve the prediction of binding motifs beyond first order.NAR genomics and bioinformatics · 2021Article
- Contribution of nonconsensus base pairs within ArsR binding sequences toward ArsR-DNA binding and arsenic-mediated transcriptional induction.Journal of biological engineering · 2019Article
- Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences.Scientific reports · 2017Article
- Nucleotide Interdependency in Transcription Factor Binding Sites in the Drosophila Genome.Gene regulation and systems biology · 2016Article
- Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.BMC bioinformatics · 2015Article
- MARZ: an algorithm to combinatorially analyze gapped n-mer models of transcription factor binding.BMC bioinformatics · 2015Article
- Protein-DNA binding: complexities and multi-protein codes.Nucleic acids research · 2014Review
- Application of experimentally verified transcription factor binding sites models for computational analysis of ChIP-Seq data.BMC genomics · 2014Article
- The influence of promoter architectures and regulatory motifs on gene expression in Escherichia coli.PloS one · 2014Article
- Effective automated feature construction and selection for classification of biological sequences.PloS one · 2014Article
- Flanking sequence context-dependent transcription factor binding in early Drosophila development.BMC bioinformatics · 2013Article
- Identification of cis-regulatory modules in promoters of human genes exploiting mutual positioning of transcription factors.Nucleic acids research · 2013Article
- Article
- Discovery, optimization and validation of an optimal DNA-binding sequence for the Six1 homeodomain transcription factor.Nucleic acids research · 2012Article
- Optimizing the GATA-3 position weight matrix to improve the identification of novel binding sites.BMC genomics · 2012Article
- A flexible integrative approach based on random forest improves prediction of transcription factor binding sites.Nucleic acids research · 2012Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Position-weight matrices (PWMs) are broadly used to locate transcription factor binding sites in DNA sequences. The majority of existing PWMs provide a low level of both sensitivity and specificity. We present a new computational algorithm, a modification of the Staden-Bucher approach, that improves the PWM. We applied the proposed technique on the PWM of the GC-box, binding site for Sp1. The comparison of old and new PWMs shows that the latter increase both sensitivity and specificity. The statistical parameters of GC-box distribution in promoter regions and in the human genome, as well as in each chromosome, are presented. The majority of commonly used PWMs are the 4-row mononucleotide matrices, although 16-row dinucleotide matrices are known to be more informative. The algorithm efficiently determines the 16-row matrices and preliminary results show that such matrices provide better results than 4-row matrices.
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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.