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PlasGUN: gene prediction in plasmid metagenomic short reads using deep learning

SUMMARY: We present the first tool of gene prediction, PlasGUN, for plasmid metagenomic short-read data. The tool, developed based on deep learning algorithm of multiple input Convolutional Neural Network, demonstrates much better performance when tested on a benchmark dataset of artificial short re...

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Detalles Bibliográficos
Autores principales: Fang, Zhencheng, Tan, Jie, Wu, Shufang, Li, Mo, Wang, Chunhui, Liu, Yongchu, Zhu, Huaiqiu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214025/
https://www.ncbi.nlm.nih.gov/pubmed/32091572
http://dx.doi.org/10.1093/bioinformatics/btaa103
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author Fang, Zhencheng
Tan, Jie
Wu, Shufang
Li, Mo
Wang, Chunhui
Liu, Yongchu
Zhu, Huaiqiu
author_facet Fang, Zhencheng
Tan, Jie
Wu, Shufang
Li, Mo
Wang, Chunhui
Liu, Yongchu
Zhu, Huaiqiu
author_sort Fang, Zhencheng
collection PubMed
description SUMMARY: We present the first tool of gene prediction, PlasGUN, for plasmid metagenomic short-read data. The tool, developed based on deep learning algorithm of multiple input Convolutional Neural Network, demonstrates much better performance when tested on a benchmark dataset of artificial short reads and presents more reliable results for real plasmid metagenomic data than traditional gene prediction tools designed primarily for chromosome-derived short reads. AVAILABILITY AND IMPLEMENTATION: The PlasGUN software is available at http://cqb.pku.edu.cn/ZhuLab/PlasGUN/ or https://github.com/zhenchengfang/PlasGUN/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-72140252020-05-15 PlasGUN: gene prediction in plasmid metagenomic short reads using deep learning Fang, Zhencheng Tan, Jie Wu, Shufang Li, Mo Wang, Chunhui Liu, Yongchu Zhu, Huaiqiu Bioinformatics Applications Notes SUMMARY: We present the first tool of gene prediction, PlasGUN, for plasmid metagenomic short-read data. The tool, developed based on deep learning algorithm of multiple input Convolutional Neural Network, demonstrates much better performance when tested on a benchmark dataset of artificial short reads and presents more reliable results for real plasmid metagenomic data than traditional gene prediction tools designed primarily for chromosome-derived short reads. AVAILABILITY AND IMPLEMENTATION: The PlasGUN software is available at http://cqb.pku.edu.cn/ZhuLab/PlasGUN/ or https://github.com/zhenchengfang/PlasGUN/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-05-15 2020-02-24 /pmc/articles/PMC7214025/ /pubmed/32091572 http://dx.doi.org/10.1093/bioinformatics/btaa103 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial 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 Applications Notes
Fang, Zhencheng
Tan, Jie
Wu, Shufang
Li, Mo
Wang, Chunhui
Liu, Yongchu
Zhu, Huaiqiu
PlasGUN: gene prediction in plasmid metagenomic short reads using deep learning
title PlasGUN: gene prediction in plasmid metagenomic short reads using deep learning
title_full PlasGUN: gene prediction in plasmid metagenomic short reads using deep learning
title_fullStr PlasGUN: gene prediction in plasmid metagenomic short reads using deep learning
title_full_unstemmed PlasGUN: gene prediction in plasmid metagenomic short reads using deep learning
title_short PlasGUN: gene prediction in plasmid metagenomic short reads using deep learning
title_sort plasgun: gene prediction in plasmid metagenomic short reads using deep learning
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214025/
https://www.ncbi.nlm.nih.gov/pubmed/32091572
http://dx.doi.org/10.1093/bioinformatics/btaa103
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