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Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels
BACKGROUND: A quantitative understanding of interactions between transcription factors (TFs) and their DNA binding sites is key to the rational design of gene regulatory networks. Recent advances in high-throughput technologies have enabled high-resolution measurements of protein-DNA binding affinit...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4305984/ https://www.ncbi.nlm.nih.gov/pubmed/25605483 http://dx.doi.org/10.1186/1752-0509-8-S5-S5 |
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author | Wang, Xiaolei Kuwahara, Hiroyuki Gao, Xin |
author_facet | Wang, Xiaolei Kuwahara, Hiroyuki Gao, Xin |
author_sort | Wang, Xiaolei |
collection | PubMed |
description | BACKGROUND: A quantitative understanding of interactions between transcription factors (TFs) and their DNA binding sites is key to the rational design of gene regulatory networks. Recent advances in high-throughput technologies have enabled high-resolution measurements of protein-DNA binding affinity. Importantly, such experiments revealed the complex nature of TF-DNA interactions, whereby the effects of nucleotide changes on the binding affinity were observed to be context dependent. A systematic method to give high-quality estimates of such complex affinity landscapes is, thus, essential to the control of gene expression and the advance of synthetic biology. RESULTS: Here, we propose a two-round prediction method that is based on support vector regression (SVR) with weighted degree (WD) kernels. In the first round, a WD kernel with shifts and mismatches is used with SVR to detect the importance of subsequences with different lengths at different positions. The subsequences identified as important in the first round are then fed into a second WD kernel to fit the experimentally measured affinities. To our knowledge, this is the first attempt to increase the accuracy of the affinity prediction by applying two rounds of string kernels and by identifying a small number of crucial k-mers. The proposed method was tested by predicting the binding affinity landscape of Gcn4p in Saccharomyces cerevisiae using datasets from HiTS-FLIP. Our method explicitly identified important subsequences and showed significant performance improvements when compared with other state-of-the-art methods. Based on the identified important subsequences, we discovered two surprisingly stable 10-mers and one sensitive 10-mer which were not reported before. Further test on four other TFs in S. cerevisiae demonstrated the generality of our method. CONCLUSION: We proposed in this paper a two-round method to quantitatively model the DNA binding affinity landscape. Since the ability to modify genetic parts to fine-tune gene expression rates is crucial to the design of biological systems, such a tool may play an important role in the success of synthetic biology going forward. |
format | Online Article Text |
id | pubmed-4305984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43059842015-02-12 Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels Wang, Xiaolei Kuwahara, Hiroyuki Gao, Xin BMC Syst Biol Research BACKGROUND: A quantitative understanding of interactions between transcription factors (TFs) and their DNA binding sites is key to the rational design of gene regulatory networks. Recent advances in high-throughput technologies have enabled high-resolution measurements of protein-DNA binding affinity. Importantly, such experiments revealed the complex nature of TF-DNA interactions, whereby the effects of nucleotide changes on the binding affinity were observed to be context dependent. A systematic method to give high-quality estimates of such complex affinity landscapes is, thus, essential to the control of gene expression and the advance of synthetic biology. RESULTS: Here, we propose a two-round prediction method that is based on support vector regression (SVR) with weighted degree (WD) kernels. In the first round, a WD kernel with shifts and mismatches is used with SVR to detect the importance of subsequences with different lengths at different positions. The subsequences identified as important in the first round are then fed into a second WD kernel to fit the experimentally measured affinities. To our knowledge, this is the first attempt to increase the accuracy of the affinity prediction by applying two rounds of string kernels and by identifying a small number of crucial k-mers. The proposed method was tested by predicting the binding affinity landscape of Gcn4p in Saccharomyces cerevisiae using datasets from HiTS-FLIP. Our method explicitly identified important subsequences and showed significant performance improvements when compared with other state-of-the-art methods. Based on the identified important subsequences, we discovered two surprisingly stable 10-mers and one sensitive 10-mer which were not reported before. Further test on four other TFs in S. cerevisiae demonstrated the generality of our method. CONCLUSION: We proposed in this paper a two-round method to quantitatively model the DNA binding affinity landscape. Since the ability to modify genetic parts to fine-tune gene expression rates is crucial to the design of biological systems, such a tool may play an important role in the success of synthetic biology going forward. BioMed Central 2014-12-12 /pmc/articles/PMC4305984/ /pubmed/25605483 http://dx.doi.org/10.1186/1752-0509-8-S5-S5 Text en Copyright © 2014 Wang et al.; licensee BioMed Central Ltd. 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 work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Wang, Xiaolei Kuwahara, Hiroyuki Gao, Xin Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels |
title | Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels |
title_full | Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels |
title_fullStr | Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels |
title_full_unstemmed | Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels |
title_short | Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels |
title_sort | modeling dna affinity landscape through two-round support vector regression with weighted degree kernels |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4305984/ https://www.ncbi.nlm.nih.gov/pubmed/25605483 http://dx.doi.org/10.1186/1752-0509-8-S5-S5 |
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