ArticleBMC bioinformatics2013
Gene prediction in metagenomic fragments based on the SVM algorithm.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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39 citing papers in PubMed.
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- LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion.International journal of molecular sciences · 2020Article
- PlasGUN: gene prediction in plasmid metagenomic short reads using deep learning.Bioinformatics (Oxford, England) · 2020Article
- Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method.Frontiers in cell and developmental biology · 2020Article
- Multiomics Study of Gut Bacteria and Host Metabolism in Irritable Bowel Syndrome and Depression Patients.Frontiers in cellular and infection microbiology · 2020Article
- GPRED-GC: a Gene PREDiction model accounting for 5BMC bioinformatics · 2019Article
- CNN-MGP: Convolutional Neural Networks for Metagenomics Gene Prediction.Interdisciplinary sciences, computational life sciences · 2019Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundMetagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms from various environments, such as human body, without isolation and cultivation. Accurately identifying genes from metagenomic fragments is one of the most fundamental issues.
resultsIn this article, we present a novel gene prediction method named MetaGUN for metagenomic fragments based on a machine learning approach of SVM. It implements in a three-stage strategy to predict genes. Firstly, it classifies input fragments into phylogenetic groups by a k-mer based sequence binning method. Then, protein-coding sequences are identified for each group independently with SVM classifiers that integrate entropy density profiles (EDP) of codon usage, translation initiation site (TIS) scores and open reading frame (ORF) length as input patterns. Finally, the TISs are adjusted by employing a modified version of MetaTISA. To identify protein-coding sequences, MetaGun builds the universal module and the novel module. The former is based on a set of representative species, while the latter is designed to find potential functionary DNA sequences with conserved domains.
conclusionsComparisons on artificial shotgun fragments with multiple current metagenomic gene finders show that MetaGUN predicts better results on both 3' and 5' ends of genes with fragments of various lengths. Especially, it makes the most reliable predictions among these methods. As an application, MetaGUN was used to predict genes for two samples of human gut microbiome. It identifies thousands of additional genes with significant evidences. Further analysis indicates that MetaGUN tends to predict more potential novel genes than other current metagenomic gene finders.
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