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Related Experiment Video

Updated: Mar 3, 2026

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

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

Published on: June 17, 2012

From Gene Annotation to Function Prediction for Metagenomics.

Fatemeh Sharifi¹, Yuzhen Ye²

¹School of Informatics and Computing, Indiana University, 150 S. Woodlawn Ave., Bloomington, IN, 47405, USA.

Methods in Molecular Biology (Clifton, N.J.)

|April 29, 2017

Summary

This summary is machine-generated.

This study introduces Fun4Me, a new pipeline for the functional annotation of metagenomic data. It identifies microbial genes and predicts their functions, aiding in understanding microbial community roles in health and disease.

Keywords:

Function prediction Gene Ontology (GO)Metabolic pathway Metagenomics Similarity search

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Last Updated: Mar 3, 2026

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

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

Published on: June 17, 2012

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

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Published on: August 15, 2019

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Area of Science:

Microbiology
Bioinformatics
Genomics

Background:

Microbes are crucial in various life aspects, including human health and disease.
Metagenomics, powered by sequencing advancements, rapidly expands genomic data for previously uncultured microbes.
Metagenomic analysis reveals both species composition and functional capabilities of microbial communities.

Purpose of the Study:

To develop and report a computational pipeline for the functional annotation of metagenomic datasets.
To provide tools for predicting protein-coding genes and assigning functional annotations to metagenomic sequences.

Main Methods:

The pipeline integrates custom-developed programs for metagenomic sequence analysis.
Includes a protein-coding gene predictor optimized for short reads or contigs.
Features a fast similarity search tool for efficient annotation.

Main Results:

The pipeline successfully annotates metagenomic datasets, identifying putative protein-coding genes.
Provides functional annotations in Gene Ontology (GO) terms and Enzyme Commission (EC) numbers.
Predicts potential metabolic pathways encoded within the metagenome.

Conclusions:

The Fun4Me pipeline offers a comprehensive approach to metagenomic functional annotation.
Facilitates deeper insights into the functional roles of microbial communities.
The tool is available for download, supporting further research in metagenomics.