High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.

Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site...

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Autores principales:	Phaedra Agius, Aaron Arvey, William Chang, William Stafford Noble, Christina Leslie
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/ca3cccfba20f4d749a137a9a04a5c70f
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spelling	oai:doaj.org-article:ca3cccfba20f4d749a137a9a04a5c70f2021-11-18T05:49:19ZHigh resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.1553-734X1553-735810.1371/journal.pcbi.1000916https://doaj.org/article/ca3cccfba20f4d749a137a9a04a5c70f2010-09-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20838582/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices (PSSMs), which may match large numbers of sites and produce an unreliable list of target genes. Recently, protein binding microarray (PBM) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities. PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities, so that individual sequence patterns are assigned enrichment scores (E-scores). This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns. Meanwhile, high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available. We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data. We first trained support vector regression (SVR) models on PBM data to learn the mapping from probe sequences to binding intensities. We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities. The SVR models are more compact than E-scores, more expressive than PSSMs, and can be readily used to scan genomics regions to predict in vivo occupancy. Using a large data set of yeast and mouse TFs, we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs. Moreover, by using SVRs to score yeast, mouse, and human genomic regions, we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments. Finally, we found that by training kernel-based models directly on ChIP-seq data, we greatly improved in vivo occupancy prediction, and by comparing a TF's in vitro and in vivo models, we could identify cofactors and disambiguate direct and indirect binding.Phaedra AgiusAaron ArveyWilliam ChangWilliam Stafford NobleChristina LesliePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 9 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Phaedra Agius Aaron Arvey William Chang William Stafford Noble Christina Leslie High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.
description	Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices (PSSMs), which may match large numbers of sites and produce an unreliable list of target genes. Recently, protein binding microarray (PBM) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities. PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities, so that individual sequence patterns are assigned enrichment scores (E-scores). This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns. Meanwhile, high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available. We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data. We first trained support vector regression (SVR) models on PBM data to learn the mapping from probe sequences to binding intensities. We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities. The SVR models are more compact than E-scores, more expressive than PSSMs, and can be readily used to scan genomics regions to predict in vivo occupancy. Using a large data set of yeast and mouse TFs, we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs. Moreover, by using SVRs to score yeast, mouse, and human genomic regions, we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments. Finally, we found that by training kernel-based models directly on ChIP-seq data, we greatly improved in vivo occupancy prediction, and by comparing a TF's in vitro and in vivo models, we could identify cofactors and disambiguate direct and indirect binding.
format	article
author	Phaedra Agius Aaron Arvey William Chang William Stafford Noble Christina Leslie
author_facet	Phaedra Agius Aaron Arvey William Chang William Stafford Noble Christina Leslie
author_sort	Phaedra Agius
title	High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.
title_short	High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.
title_full	High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.
title_fullStr	High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.
title_full_unstemmed	High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.
title_sort	high resolution models of transcription factor-dna affinities improve in vitro and in vivo binding predictions.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/ca3cccfba20f4d749a137a9a04a5c70f
work_keys_str_mv	AT phaedraagius highresolutionmodelsoftranscriptionfactordnaaffinitiesimproveinvitroandinvivobindingpredictions AT aaronarvey highresolutionmodelsoftranscriptionfactordnaaffinitiesimproveinvitroandinvivobindingpredictions AT williamchang highresolutionmodelsoftranscriptionfactordnaaffinitiesimproveinvitroandinvivobindingpredictions AT williamstaffordnoble highresolutionmodelsoftranscriptionfactordnaaffinitiesimproveinvitroandinvivobindingpredictions AT christinaleslie highresolutionmodelsoftranscriptionfactordnaaffinitiesimproveinvitroandinvivobindingpredictions
_version_	1718424815992307712

High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.

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