A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome

Abstract DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money. Our study develops a convolutional neur...

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Autores principales:	Chowdhury Rafeed Rahman, Ruhul Amin, Swakkhar Shatabda, Md. Sadrul Islam Toaha
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8b9fbe0d9f8d4fb8bbcb8712e9e6303b
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spelling	oai:doaj.org-article:8b9fbe0d9f8d4fb8bbcb8712e9e6303b2021-12-02T16:50:17ZA convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome10.1038/s41598-021-89850-92045-2322https://doaj.org/article/8b9fbe0d9f8d4fb8bbcb8712e9e6303b2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89850-9https://doaj.org/toc/2045-2322Abstract DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money. Our study develops a convolutional neural network (CNN) based tool i6mA-CNN capable of identifying 6mA sites in the rice genome. Our model coordinates among multiple types of features such as PseAAC (Pseudo Amino Acid Composition) inspired customized feature vector, multiple one hot representations and dinucleotide physicochemical properties. It achieves auROC (area under Receiver Operating Characteristic curve) score of 0.98 with an overall accuracy of 93.97% using fivefold cross validation on benchmark dataset. Finally, we evaluate our model on three other plant genome 6mA site identification test datasets. Results suggest that our proposed tool is able to generalize its ability of 6mA site identification on plant genomes irrespective of plant species. An algorithm for potential motif extraction and a feature importance analysis procedure are two by products of this research. Web tool for this research can be found at: https://cutt.ly/dgp3QTR .Chowdhury Rafeed RahmanRuhul AminSwakkhar ShatabdaMd. Sadrul Islam ToahaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chowdhury Rafeed Rahman Ruhul Amin Swakkhar Shatabda Md. Sadrul Islam Toaha A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome
description	Abstract DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money. Our study develops a convolutional neural network (CNN) based tool i6mA-CNN capable of identifying 6mA sites in the rice genome. Our model coordinates among multiple types of features such as PseAAC (Pseudo Amino Acid Composition) inspired customized feature vector, multiple one hot representations and dinucleotide physicochemical properties. It achieves auROC (area under Receiver Operating Characteristic curve) score of 0.98 with an overall accuracy of 93.97% using fivefold cross validation on benchmark dataset. Finally, we evaluate our model on three other plant genome 6mA site identification test datasets. Results suggest that our proposed tool is able to generalize its ability of 6mA site identification on plant genomes irrespective of plant species. An algorithm for potential motif extraction and a feature importance analysis procedure are two by products of this research. Web tool for this research can be found at: https://cutt.ly/dgp3QTR .
format	article
author	Chowdhury Rafeed Rahman Ruhul Amin Swakkhar Shatabda Md. Sadrul Islam Toaha
author_facet	Chowdhury Rafeed Rahman Ruhul Amin Swakkhar Shatabda Md. Sadrul Islam Toaha
author_sort	Chowdhury Rafeed Rahman
title	A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome
title_short	A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome
title_full	A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome
title_fullStr	A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome
title_full_unstemmed	A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome
title_sort	convolution based computational approach towards dna n6-methyladenine site identification and motif extraction in rice genome
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8b9fbe0d9f8d4fb8bbcb8712e9e6303b
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A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome

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