DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool

DNA N<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mn>6</mn></msup></semantics></math></inline-formula>-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA rep...

Full description

Bibliographic Details
Main Authors: Mobeen Ur Rehman, Kil To Chong
Format: Article
Language:English
Published: MDPI AG 2020-08-01
Series:Genes
Subjects:
Online Access:https://www.mdpi.com/2073-4425/11/8/898
id doaj-ca438195434444b1aece754039150e1c
record_format Article
spelling doaj-ca438195434444b1aece754039150e1c2020-11-25T03:48:29ZengMDPI AGGenes2073-44252020-08-011189889810.3390/genes11080898DNA6mA-MINT: DNA-6mA Modification Identification Neural ToolMobeen Ur Rehman0Kil To Chong1Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, KoreaDepartment of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, KoreaDNA N<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mn>6</mn></msup></semantics></math></inline-formula>-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou’s 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on <i>Mus musculus</i>, Rice, and “Combined-species” genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.https://www.mdpi.com/2073-4425/11/8/898DNA N6-methyladenineChou’s 5-steps ruleConvolution Neural Network (CNN)Long Short-Term Memory (LSTM)computational biology
collection DOAJ
language English
format Article
sources DOAJ
author Mobeen Ur Rehman
Kil To Chong
spellingShingle Mobeen Ur Rehman
Kil To Chong
DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool
Genes
DNA N6-methyladenine
Chou’s 5-steps rule
Convolution Neural Network (CNN)
Long Short-Term Memory (LSTM)
computational biology
author_facet Mobeen Ur Rehman
Kil To Chong
author_sort Mobeen Ur Rehman
title DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool
title_short DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool
title_full DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool
title_fullStr DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool
title_full_unstemmed DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool
title_sort dna6ma-mint: dna-6ma modification identification neural tool
publisher MDPI AG
series Genes
issn 2073-4425
publishDate 2020-08-01
description DNA N<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mn>6</mn></msup></semantics></math></inline-formula>-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou’s 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on <i>Mus musculus</i>, Rice, and “Combined-species” genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.
topic DNA N6-methyladenine
Chou’s 5-steps rule
Convolution Neural Network (CNN)
Long Short-Term Memory (LSTM)
computational biology
url https://www.mdpi.com/2073-4425/11/8/898
work_keys_str_mv AT mobeenurrehman dna6mamintdna6mamodificationidentificationneuraltool
AT kiltochong dna6mamintdna6mamodificationidentificationneuraltool
_version_ 1724498810084786176