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Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism

DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can a...

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Autores principales: Ahmad, Shandar, Prathipati, Philip, Tripathi, Lokesh P, Chen, Yi-An, Arya, Ajay, Murakami, Yoichi, Mizuguchi, Kenji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5758906/
https://www.ncbi.nlm.nih.gov/pubmed/29186632
http://dx.doi.org/10.1093/nar/gkx1166
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author Ahmad, Shandar
Prathipati, Philip
Tripathi, Lokesh P
Chen, Yi-An
Arya, Ajay
Murakami, Yoichi
Mizuguchi, Kenji
author_facet Ahmad, Shandar
Prathipati, Philip
Tripathi, Lokesh P
Chen, Yi-An
Arya, Ajay
Murakami, Yoichi
Mizuguchi, Kenji
author_sort Ahmad, Shandar
collection PubMed
description DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can accurately predict DNA-binding sites in known DBPs can also identify novel DBPs. Moreover, sequence information is blind to the cellular- and disease-specific contexts of DBP activities, whereas the under-utilized knowledge from public gene expression data offers great promise. To address these issues, we have developed novel methods for predicting DBPs by integrating sequence and gene expression-derived features and applied them to explore human, mouse and Arabidopsis proteomes. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment. Analysis of motif enrichment among the co-expressed genes of top 100 candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations.
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spelling pubmed-57589062018-01-16 Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism Ahmad, Shandar Prathipati, Philip Tripathi, Lokesh P Chen, Yi-An Arya, Ajay Murakami, Yoichi Mizuguchi, Kenji Nucleic Acids Res Computational Biology DNA-binding proteins (DBPs) perform diverse biological functions ranging from transcription to pathogen sensing. Machine learning methods can not only identify DBPs de novo but also provide insights into their DNA-recognition dynamics. However, it remains unclear whether available methods that can accurately predict DNA-binding sites in known DBPs can also identify novel DBPs. Moreover, sequence information is blind to the cellular- and disease-specific contexts of DBP activities, whereas the under-utilized knowledge from public gene expression data offers great promise. To address these issues, we have developed novel methods for predicting DBPs by integrating sequence and gene expression-derived features and applied them to explore human, mouse and Arabidopsis proteomes. While our sequence-based models outperformed the gene expression-based ones, some proteins with weaker DBP-like sequence features were correctly predicted by gene expression-based features, suggesting that these proteins acquire a tangible DBP functionality in a conducive gene expression environment. Analysis of motif enrichment among the co-expressed genes of top 100 candidates DBPs from hitherto unannotated genes provides further avenues to explore their functional associations. Oxford University Press 2018-01-09 2017-11-25 /pmc/articles/PMC5758906/ /pubmed/29186632 http://dx.doi.org/10.1093/nar/gkx1166 Text en © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Computational Biology
Ahmad, Shandar
Prathipati, Philip
Tripathi, Lokesh P
Chen, Yi-An
Arya, Ajay
Murakami, Yoichi
Mizuguchi, Kenji
Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism
title Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism
title_full Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism
title_fullStr Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism
title_full_unstemmed Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism
title_short Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism
title_sort integrating sequence and gene expression information predicts genome-wide dna-binding proteins and suggests a cooperative mechanism
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5758906/
https://www.ncbi.nlm.nih.gov/pubmed/29186632
http://dx.doi.org/10.1093/nar/gkx1166
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