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Gene Prediction in Metagenomic Fragments with Deep Learning.

ABSTRACT: Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features) and using deep stacking networks learning model, we present a novel method (called Meta-MFDL) to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.

SUBMITTER: Zhang SW 

PROVIDER: S-EPMC5698827 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Gene Prediction in Metagenomic Fragments with Deep Learning.

Zhang Shao-Wu SW   Jin Xiang-Yang XY   Zhang Teng T  

BioMed research international 20171108

Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features) and using deep stacking networks learning model, we present a novel method (called Meta-MFDL) to predict the meta  ...[more]

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