Cargando…

DNAcycP: a deep learning tool for DNA cyclizability prediction

DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq has been developed to quantify the intrinsic bendability of a massive number of DNA fragments simultaneously. Using the loop-seq data, we develop a so...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Keren, Carroll, Matthew, Vafabakhsh, Reza, Wang, Xiaozhong A, Wang, Ji-Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989542/
https://www.ncbi.nlm.nih.gov/pubmed/35288750
http://dx.doi.org/10.1093/nar/gkac162
_version_ 1784683198381293568
author Li, Keren
Carroll, Matthew
Vafabakhsh, Reza
Wang, Xiaozhong A
Wang, Ji-Ping
author_facet Li, Keren
Carroll, Matthew
Vafabakhsh, Reza
Wang, Xiaozhong A
Wang, Ji-Ping
author_sort Li, Keren
collection PubMed
description DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq has been developed to quantify the intrinsic bendability of a massive number of DNA fragments simultaneously. Using the loop-seq data, we develop a software tool, DNAcycP, based on a deep-learning approach for intrinsic DNA cyclizability prediction. We demonstrate DNAcycP predicts intrinsic DNA cyclizability with high fidelity compared to the experimental data. Using an independent dataset from in vitro selection for enrichment of loopable sequences, we further verified the predicted cyclizability score, termed C-score, can well distinguish DNA fragments with different loopability. We applied DNAcycP to multiple species and compared the C-scores with available high-resolution chemical nucleosome maps. Our analyses showed that both yeast and mouse genomes share a conserved feature of high DNA bendability spanning nucleosome dyads. Additionally, we extended our analysis to transcription factor binding sites and surprisingly found that the cyclizability is substantially elevated at CTCF binding sites in the mouse genome. We further demonstrate this distinct mechanical property is conserved across mammalian species and is inherent to CTCF binding DNA motif.
format Online
Article
Text
id pubmed-8989542
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-89895422022-04-08 DNAcycP: a deep learning tool for DNA cyclizability prediction Li, Keren Carroll, Matthew Vafabakhsh, Reza Wang, Xiaozhong A Wang, Ji-Ping Nucleic Acids Res Computational Biology DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq has been developed to quantify the intrinsic bendability of a massive number of DNA fragments simultaneously. Using the loop-seq data, we develop a software tool, DNAcycP, based on a deep-learning approach for intrinsic DNA cyclizability prediction. We demonstrate DNAcycP predicts intrinsic DNA cyclizability with high fidelity compared to the experimental data. Using an independent dataset from in vitro selection for enrichment of loopable sequences, we further verified the predicted cyclizability score, termed C-score, can well distinguish DNA fragments with different loopability. We applied DNAcycP to multiple species and compared the C-scores with available high-resolution chemical nucleosome maps. Our analyses showed that both yeast and mouse genomes share a conserved feature of high DNA bendability spanning nucleosome dyads. Additionally, we extended our analysis to transcription factor binding sites and surprisingly found that the cyclizability is substantially elevated at CTCF binding sites in the mouse genome. We further demonstrate this distinct mechanical property is conserved across mammalian species and is inherent to CTCF binding DNA motif. Oxford University Press 2022-03-14 /pmc/articles/PMC8989542/ /pubmed/35288750 http://dx.doi.org/10.1093/nar/gkac162 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
Li, Keren
Carroll, Matthew
Vafabakhsh, Reza
Wang, Xiaozhong A
Wang, Ji-Ping
DNAcycP: a deep learning tool for DNA cyclizability prediction
title DNAcycP: a deep learning tool for DNA cyclizability prediction
title_full DNAcycP: a deep learning tool for DNA cyclizability prediction
title_fullStr DNAcycP: a deep learning tool for DNA cyclizability prediction
title_full_unstemmed DNAcycP: a deep learning tool for DNA cyclizability prediction
title_short DNAcycP: a deep learning tool for DNA cyclizability prediction
title_sort dnacycp: a deep learning tool for dna cyclizability prediction
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989542/
https://www.ncbi.nlm.nih.gov/pubmed/35288750
http://dx.doi.org/10.1093/nar/gkac162
work_keys_str_mv AT likeren dnacycpadeeplearningtoolfordnacyclizabilityprediction
AT carrollmatthew dnacycpadeeplearningtoolfordnacyclizabilityprediction
AT vafabakhshreza dnacycpadeeplearningtoolfordnacyclizabilityprediction
AT wangxiaozhonga dnacycpadeeplearningtoolfordnacyclizabilityprediction
AT wangjiping dnacycpadeeplearningtoolfordnacyclizabilityprediction