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Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors
Gene regulatory networks are ultimately encoded by the sequence-specific binding of (TFs) to short DNA segments. Although it is customary to represent the binding specificity of a TF by a position-specific weight matrix (PSWM), which assumes each position within a site contributes independently to t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5550003/ https://www.ncbi.nlm.nih.gov/pubmed/28753602 http://dx.doi.org/10.1371/journal.pcbi.1005176 |
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author | Omidi, Saeed Zavolan, Mihaela Pachkov, Mikhail Breda, Jeremie Berger, Severin van Nimwegen, Erik |
author_facet | Omidi, Saeed Zavolan, Mihaela Pachkov, Mikhail Breda, Jeremie Berger, Severin van Nimwegen, Erik |
author_sort | Omidi, Saeed |
collection | PubMed |
description | Gene regulatory networks are ultimately encoded by the sequence-specific binding of (TFs) to short DNA segments. Although it is customary to represent the binding specificity of a TF by a position-specific weight matrix (PSWM), which assumes each position within a site contributes independently to the overall binding affinity, evidence has been accumulating that there can be significant dependencies between positions. Unfortunately, methodological challenges have so far hindered the development of a practical and generally-accepted extension of the PSWM model. On the one hand, simple models that only consider dependencies between nearest-neighbor positions are easy to use in practice, but fail to account for the distal dependencies that are observed in the data. On the other hand, models that allow for arbitrary dependencies are prone to overfitting, requiring regularization schemes that are difficult to use in practice for non-experts. Here we present a new regulatory motif model, called dinucleotide weight tensor (DWT), that incorporates arbitrary pairwise dependencies between positions in binding sites, rigorously from first principles, and free from tunable parameters. We demonstrate the power of the method on a large set of ChIP-seq data-sets, showing that DWTs outperform both PSWMs and motif models that only incorporate nearest-neighbor dependencies. We also demonstrate that DWTs outperform two previously proposed methods. Finally, we show that DWTs inferred from ChIP-seq data also outperform PSWMs on HT-SELEX data for the same TF, suggesting that DWTs capture inherent biophysical properties of the interactions between the DNA binding domains of TFs and their binding sites. We make a suite of DWT tools available at dwt.unibas.ch, that allow users to automatically perform ‘motif finding’, i.e. the inference of DWT motifs from a set of sequences, binding site prediction with DWTs, and visualization of DWT ‘dilogo’ motifs. |
format | Online Article Text |
id | pubmed-5550003 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-55500032017-08-15 Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors Omidi, Saeed Zavolan, Mihaela Pachkov, Mikhail Breda, Jeremie Berger, Severin van Nimwegen, Erik PLoS Comput Biol Research Article Gene regulatory networks are ultimately encoded by the sequence-specific binding of (TFs) to short DNA segments. Although it is customary to represent the binding specificity of a TF by a position-specific weight matrix (PSWM), which assumes each position within a site contributes independently to the overall binding affinity, evidence has been accumulating that there can be significant dependencies between positions. Unfortunately, methodological challenges have so far hindered the development of a practical and generally-accepted extension of the PSWM model. On the one hand, simple models that only consider dependencies between nearest-neighbor positions are easy to use in practice, but fail to account for the distal dependencies that are observed in the data. On the other hand, models that allow for arbitrary dependencies are prone to overfitting, requiring regularization schemes that are difficult to use in practice for non-experts. Here we present a new regulatory motif model, called dinucleotide weight tensor (DWT), that incorporates arbitrary pairwise dependencies between positions in binding sites, rigorously from first principles, and free from tunable parameters. We demonstrate the power of the method on a large set of ChIP-seq data-sets, showing that DWTs outperform both PSWMs and motif models that only incorporate nearest-neighbor dependencies. We also demonstrate that DWTs outperform two previously proposed methods. Finally, we show that DWTs inferred from ChIP-seq data also outperform PSWMs on HT-SELEX data for the same TF, suggesting that DWTs capture inherent biophysical properties of the interactions between the DNA binding domains of TFs and their binding sites. We make a suite of DWT tools available at dwt.unibas.ch, that allow users to automatically perform ‘motif finding’, i.e. the inference of DWT motifs from a set of sequences, binding site prediction with DWTs, and visualization of DWT ‘dilogo’ motifs. Public Library of Science 2017-07-28 /pmc/articles/PMC5550003/ /pubmed/28753602 http://dx.doi.org/10.1371/journal.pcbi.1005176 Text en © 2017 Omidi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Omidi, Saeed Zavolan, Mihaela Pachkov, Mikhail Breda, Jeremie Berger, Severin van Nimwegen, Erik Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors |
title | Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors |
title_full | Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors |
title_fullStr | Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors |
title_full_unstemmed | Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors |
title_short | Automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors |
title_sort | automated incorporation of pairwise dependency in transcription factor binding site prediction using dinucleotide weight tensors |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5550003/ https://www.ncbi.nlm.nih.gov/pubmed/28753602 http://dx.doi.org/10.1371/journal.pcbi.1005176 |
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