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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

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Published on: June 17, 2012

Vgas: A Viral Genome Annotation System.

Kai-Yue Zhang¹, Yi-Zhou Gao¹, Meng-Ze Du¹

¹Centre for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.

Frontiers in Microbiology

|March 1, 2019

Summary

This summary is machine-generated.

Vgas is a new viral gene finder that improves gene prediction accuracy, especially for small viral genomes. It combines ab initio and similarity-based methods for better viral genome analysis and annotation.

Keywords:

Vgas function annotation joint application of multiple programs novel genes virus gene prediction

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Last Updated: Jan 28, 2026

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

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Published on: June 17, 2012

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Purification of Viral DNA for the Identification of Associated Viral and Cellular Proteins

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Published on: August 31, 2017

Area of Science:

Virology
Bioinformatics
Genomics

Background:

Accurate viral gene identification is crucial for understanding and treating viral infections.
The increasing volume of viral sequencing data necessitates advanced gene-finding tools.
Existing gene prediction systems may have limitations, particularly with smaller viral genomes.

Purpose of the Study:

To develop and evaluate Vgas, a novel system for automatic viral gene finding and function annotation.
To compare Vgas performance against established gene prediction programs.
To assess the utility of Vgas for analyzing diverse viral genomes, including small ones.

Main Methods:

Vgas integrates ab initio and similarity-based approaches for gene prediction.
Performance was evaluated using 5,705 virus genomes from RefSeq.
Vgas includes a module for gene function annotation via BLASTp alignment.

Main Results:

Vgas demonstrated superior average precision and recall compared to Prodigal, GeneMarkS, and Glimmer.
Vgas showed significantly improved performance on small viral genomes (≤ 10 kb).
Combining Vgas with other tools yielded better prediction results than individual programs.

Conclusions:

Vgas is a highly effective tool for viral gene prediction and annotation, outperforming existing methods.
Vgas offers significant advantages for analyzing small viral genomes.
Collaborative use of Vgas with other gene finders presents a promising strategy for enhanced viral genome analysis.