ArticleBioinformatics (Oxford, England)2015
A DNA shape-based regulatory score improves position-weight matrix-based recognition of transcription factor binding sites.
Article in Bioinformatics (Oxford, England), 2015. 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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Who cites it
10 citing papers in PubMed.
- Predicting DNA structure using a deep learning method.Nature communications · 2024Article
- Harnessing regulatory networks in Actinobacteria for natural product discovery.Journal of industrial microbiology & biotechnology · 2024Review
- PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike Sequences.Biology · 2022Article
- Sequence and chromatin determinants of transcription factor binding and the establishment of cell type-specific binding patterns.Biochimica et biophysica acta. Gene regulatory mechanisms · 2020Review
- Landscape of transcriptional deregulation in lung cancer.BMC genomics · 2018Article
- A unified approach for quantifying and interpreting DNA shape readout by transcription factors.Molecular systems biology · 2018Article
- Expanding the repertoire of DNA shape features for genome-scale studies of transcription factor binding.Nucleic acids research · 2017Article
- Article
- DNA Shape Features Improve Transcription Factor Binding Site Predictions In Vivo.Cell systems · 2016Article
- Quantitative modeling of gene expression using DNA shape features of binding sites.Nucleic acids research · 2016Article
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2 authors.
Funding
Abstract
motivationThe position-weight matrix (PWM) is a useful representation of a transcription factor binding site (TFBS) sequence pattern because the PWM can be estimated from a small number of representative TFBS sequences. However, because the PWM probability model assumes independence between individual nucleotide positions, the PWMs for some TFs poorly discriminate binding sites from non-binding-sites that have similar sequence content. Since the local three-dimensional DNA structure ('shape') is a determinant of TF binding specificity and since DNA shape has a significant sequence-dependence, we combined DNA shape-derived features into a TF-generalized regulatory score and tested whether the score could improve PWM-based discrimination of TFBS from non-binding-sites.
resultsWe compared a traditional PWM model to a model that combines the PWM with a DNA shape feature-based regulatory potential score, for accuracy in detecting binding sites for 75 vertebrate transcription factors. The PWM+shape model was more accurate than the PWM-only model, for 45% of TFs tested, with no significant loss of accuracy for the remaining TFs. AVAILABILITY AND IMPLEMENTATION: The shape-based model is available as an open-source R package at that is archived on the GitHub software repository at https://github.com/ramseylab/regshape/. CONTACT: stephen.ramsey@oregonstate.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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