PlasGUN: Gene Prediction in Plasmid Metagenomic Short Reads Using Deep Learning

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2020 Feb 25

PMID 32091572

Citations 6

Authors

Zhencheng Fang

Jie Tan

Shufang Wu

Mo Li

Chunhui Wang

Yongchu Liu

Huaiqiu Zhu

Affiliations

Soon will be listed here.

Abstract

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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References

Ji P, Zhang Y, Wang J, Zhao F . MetaSort untangles metagenome assembly by reducing microbial community complexity. Nat Commun. 2017; 8:14306. PMC: 5264255. DOI: 10.1038/ncomms14306. View

Liu Y, Guo J, Hu G, Zhu H . Gene prediction in metagenomic fragments based on the SVM algorithm. BMC Bioinformatics. 2013; 14 Suppl 5:S12. PMC: 3622649. DOI: 10.1186/1471-2105-14-S5-S12. View

Szczepanowski R, Bekel T, Goesmann A, Krause L, Kromeke H, Kaiser O . Insight into the plasmid metagenome of wastewater treatment plant bacteria showing reduced susceptibility to antimicrobial drugs analysed by the 454-pyrosequencing technology. J Biotechnol. 2008; 136(1-2):54-64. DOI: 10.1016/j.jbiotec.2008.03.020. View

Hoff K, Lingner T, Meinicke P, Tech M . Orphelia: predicting genes in metagenomic sequencing reads. Nucleic Acids Res. 2009; 37(Web Server issue):W101-5. PMC: 2703946. DOI: 10.1093/nar/gkp327. View

Rozov R, Kav A, Bogumil D, Shterzer N, Halperin E, Mizrahi I . Recycler: an algorithm for detecting plasmids from de novo assembly graphs. Bioinformatics. 2016; 33(4):475-482. PMC: 5408804. DOI: 10.1093/bioinformatics/btw651. View

Sharpton T . An introduction to the analysis of shotgun metagenomic data. Front Plant Sci. 2014; 5:209. PMC: 4059276. DOI: 10.3389/fpls.2014.00209. View

Krawczyk P, Lipinski L, Dziembowski A . PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res. 2018; 46(6):e35. PMC: 5887522. DOI: 10.1093/nar/gkx1321. View

Shi W, Ji P, Zhao F . The combination of direct and paired link graphs can boost repetitive genome assembly. Nucleic Acids Res. 2016; 45(6):e43. PMC: 5399794. DOI: 10.1093/nar/gkw1191. View

Zhou F, Xu Y . cBar: a computer program to distinguish plasmid-derived from chromosome-derived sequence fragments in metagenomics data. Bioinformatics. 2010; 26(16):2051-2. PMC: 2916713. DOI: 10.1093/bioinformatics/btq299. View

10.

Zhu W, Lomsadze A, Borodovsky M . Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 2010; 38(12):e132. PMC: 2896542. DOI: 10.1093/nar/gkq275. View

11.

Clausen P, Zankari E, Aarestrup F, Lund O . Benchmarking of methods for identification of antimicrobial resistance genes in bacterial whole genome data. J Antimicrob Chemother. 2016; 71(9):2484-8. DOI: 10.1093/jac/dkw184. View

12.

Rho M, Tang H, Ye Y . FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 2010; 38(20):e191. PMC: 2978382. DOI: 10.1093/nar/gkq747. View

13.

Hyatt D, LoCascio P, Hauser L, Uberbacher E . Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics. 2012; 28(17):2223-30. DOI: 10.1093/bioinformatics/bts429. View

14.

Jones B, Marchesi J . Transposon-aided capture (TRACA) of plasmids resident in the human gut mobile metagenome. Nat Methods. 2006; 4(1):55-61. DOI: 10.1038/nmeth964. View

15.

Fang Z, Tan J, Wu S, Li M, Xu C, Xie Z . PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning. Gigascience. 2019; 8(6). PMC: 6586199. DOI: 10.1093/gigascience/giz066. View

16.

Noguchi H, Taniguchi T, Itoh T . MetaGeneAnnotator: detecting species-specific patterns of ribosomal binding site for precise gene prediction in anonymous prokaryotic and phage genomes. DNA Res. 2008; 15(6):387-96. PMC: 2608843. DOI: 10.1093/dnares/dsn027. View