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Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information
Transcription factors play key roles in cell-fate decisions by regulating 3D genome conformation and gene expression. The traditional view is that methylation of DNA hinders transcription factors binding to them, but recent research has shown that many transcription factors prefer to bind to methyla...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society of Gene & Cell Therapy
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691157/ https://www.ncbi.nlm.nih.gov/pubmed/33294291 http://dx.doi.org/10.1016/j.omtn.2020.07.035 |
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author | Liu, Meng-Lu Su, Wei Wang, Jia-Shu Yang, Yu-He Yang, Hui Lin, Hao |
author_facet | Liu, Meng-Lu Su, Wei Wang, Jia-Shu Yang, Yu-He Yang, Hui Lin, Hao |
author_sort | Liu, Meng-Lu |
collection | PubMed |
description | Transcription factors play key roles in cell-fate decisions by regulating 3D genome conformation and gene expression. The traditional view is that methylation of DNA hinders transcription factors binding to them, but recent research has shown that many transcription factors prefer to bind to methylated DNA. Therefore, identifying such transcription factors and understanding their functions is a stepping-stone for studying methylation-mediated biological processes. In this paper, a two-step discriminated method was proposed to recognize transcription factors and their preference for methylated DNA based only on sequences information. In the first step, the proposed model was used to discriminate transcription factors from non-transcription factors. The areas under the curve (AUCs) are 0.9183 and 0.9116, respectively, for the 5-fold cross-validation test and independent dataset test. Subsequently, for the classification of transcription factors that prefer methylated DNA and transcription factors that prefer non-methylated DNA, our model could produce the AUCs of 0.7744 and 0.7356, respectively, for the 5-fold cross-validation test and independent dataset test. Based on the proposed model, a user-friendly web server called TFPred was built, which can be freely accessed at http://lin-group.cn/server/TFPred/. |
format | Online Article Text |
id | pubmed-7691157 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American Society of Gene & Cell Therapy |
record_format | MEDLINE/PubMed |
spelling | pubmed-76911572020-12-07 Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information Liu, Meng-Lu Su, Wei Wang, Jia-Shu Yang, Yu-He Yang, Hui Lin, Hao Mol Ther Nucleic Acids Original Article Transcription factors play key roles in cell-fate decisions by regulating 3D genome conformation and gene expression. The traditional view is that methylation of DNA hinders transcription factors binding to them, but recent research has shown that many transcription factors prefer to bind to methylated DNA. Therefore, identifying such transcription factors and understanding their functions is a stepping-stone for studying methylation-mediated biological processes. In this paper, a two-step discriminated method was proposed to recognize transcription factors and their preference for methylated DNA based only on sequences information. In the first step, the proposed model was used to discriminate transcription factors from non-transcription factors. The areas under the curve (AUCs) are 0.9183 and 0.9116, respectively, for the 5-fold cross-validation test and independent dataset test. Subsequently, for the classification of transcription factors that prefer methylated DNA and transcription factors that prefer non-methylated DNA, our model could produce the AUCs of 0.7744 and 0.7356, respectively, for the 5-fold cross-validation test and independent dataset test. Based on the proposed model, a user-friendly web server called TFPred was built, which can be freely accessed at http://lin-group.cn/server/TFPred/. American Society of Gene & Cell Therapy 2020-07-31 /pmc/articles/PMC7691157/ /pubmed/33294291 http://dx.doi.org/10.1016/j.omtn.2020.07.035 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Article Liu, Meng-Lu Su, Wei Wang, Jia-Shu Yang, Yu-He Yang, Hui Lin, Hao Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information |
title | Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information |
title_full | Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information |
title_fullStr | Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information |
title_full_unstemmed | Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information |
title_short | Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information |
title_sort | predicting preference of transcription factors for methylated dna using sequence information |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691157/ https://www.ncbi.nlm.nih.gov/pubmed/33294291 http://dx.doi.org/10.1016/j.omtn.2020.07.035 |
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