Zhencheng Fang1, Jie Tan1, Shufang Wu1, Mo Li1, Chunhui Wang1, Yongchu Liu1, Huaiqiu Zhu1. 1. State Key Laboratory for Turbulence and Complex Systems, Department of Biomedical Engineering, College of Engineering and Center for Quantitative Biology, Peking University, Beijing 100871, China.
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.
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.