Dinucleotide weight matrices for predicting transcription factor binding sites: generalizing the position weight matrix.

<h4>Background</h4>Identifying transcription factor binding sites (TFBS) in silico is key in understanding gene regulation. TFBS are string patterns that exhibit some variability, commonly modelled as "position weight matrices" (PWMs). Though convenient, the PWM has significant...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Rahul Siddharthan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3162a462371d411e82eaca37dc440b1d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3162a462371d411e82eaca37dc440b1d
record_format	dspace
spelling	oai:doaj.org-article:3162a462371d411e82eaca37dc440b1d2021-11-25T06:25:18ZDinucleotide weight matrices for predicting transcription factor binding sites: generalizing the position weight matrix.1932-620310.1371/journal.pone.0009722https://doaj.org/article/3162a462371d411e82eaca37dc440b1d2010-03-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20339533/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Identifying transcription factor binding sites (TFBS) in silico is key in understanding gene regulation. TFBS are string patterns that exhibit some variability, commonly modelled as "position weight matrices" (PWMs). Though convenient, the PWM has significant limitations, in particular the assumed independence of positions within the binding motif; and predictions based on PWMs are usually not very specific to known functional sites. Analysis here on binding sites in yeast suggests that correlation of dinucleotides is not limited to near-neighbours, but can extend over considerable gaps.<h4>Methodology/principal findings</h4>I describe a straightforward generalization of the PWM model, that considers frequencies of dinucleotides instead of individual nucleotides. Unlike previous efforts, this method considers all dinucleotides within an extended binding region, and does not make an attempt to determine a priori the significance of particular dinucleotide correlations. I describe how to use a "dinucleotide weight matrix" (DWM) to predict binding sites, dealing in particular with the complication that its entries are not independent probabilities. Benchmarks show, for many factors, a dramatic improvement over PWMs in precision of predicting known targets. In most cases, significant further improvement arises by extending the commonly defined "core motifs" by about 10 bp on either side. Though this flanking sequence shows no strong motif at the nucleotide level, the predictive power of the dinucleotide model suggests that the "signature" in DNA sequence of protein-binding affinity extends beyond the core protein-DNA contact region.<h4>Conclusion/significance</h4>While computationally more demanding and slower than PWM-based approaches, this dinucleotide method is straightforward, both conceptually and in implementation, and can serve as a basis for future improvements.Rahul SiddharthanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 3, p e9722 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rahul Siddharthan Dinucleotide weight matrices for predicting transcription factor binding sites: generalizing the position weight matrix.
description	<h4>Background</h4>Identifying transcription factor binding sites (TFBS) in silico is key in understanding gene regulation. TFBS are string patterns that exhibit some variability, commonly modelled as "position weight matrices" (PWMs). Though convenient, the PWM has significant limitations, in particular the assumed independence of positions within the binding motif; and predictions based on PWMs are usually not very specific to known functional sites. Analysis here on binding sites in yeast suggests that correlation of dinucleotides is not limited to near-neighbours, but can extend over considerable gaps.<h4>Methodology/principal findings</h4>I describe a straightforward generalization of the PWM model, that considers frequencies of dinucleotides instead of individual nucleotides. Unlike previous efforts, this method considers all dinucleotides within an extended binding region, and does not make an attempt to determine a priori the significance of particular dinucleotide correlations. I describe how to use a "dinucleotide weight matrix" (DWM) to predict binding sites, dealing in particular with the complication that its entries are not independent probabilities. Benchmarks show, for many factors, a dramatic improvement over PWMs in precision of predicting known targets. In most cases, significant further improvement arises by extending the commonly defined "core motifs" by about 10 bp on either side. Though this flanking sequence shows no strong motif at the nucleotide level, the predictive power of the dinucleotide model suggests that the "signature" in DNA sequence of protein-binding affinity extends beyond the core protein-DNA contact region.<h4>Conclusion/significance</h4>While computationally more demanding and slower than PWM-based approaches, this dinucleotide method is straightforward, both conceptually and in implementation, and can serve as a basis for future improvements.
format	article
author	Rahul Siddharthan
author_facet	Rahul Siddharthan
author_sort	Rahul Siddharthan
title	Dinucleotide weight matrices for predicting transcription factor binding sites: generalizing the position weight matrix.
title_short	Dinucleotide weight matrices for predicting transcription factor binding sites: generalizing the position weight matrix.
title_full	Dinucleotide weight matrices for predicting transcription factor binding sites: generalizing the position weight matrix.
title_fullStr	Dinucleotide weight matrices for predicting transcription factor binding sites: generalizing the position weight matrix.
title_full_unstemmed	Dinucleotide weight matrices for predicting transcription factor binding sites: generalizing the position weight matrix.
title_sort	dinucleotide weight matrices for predicting transcription factor binding sites: generalizing the position weight matrix.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/3162a462371d411e82eaca37dc440b1d
work_keys_str_mv	AT rahulsiddharthan dinucleotideweightmatricesforpredictingtranscriptionfactorbindingsitesgeneralizingthepositionweightmatrix
_version_	1718413733179424768

Dinucleotide weight matrices for predicting transcription factor binding sites: generalizing the position weight matrix.

Ejemplares similares