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Updated: May 20, 2026

Metagenomic Analysis of Silage

Metagenomic Analysis of Silage

Published on: January 13, 2017

Functional assignment of metagenomic data: challenges and applications.

Tulika Prakash¹, Todd D Taylor

¹Laboratory for MetaSystems Research at RIKEN, Japan.

Briefings in Bioinformatics

|July 10, 2012

Summary

Metagenomic sequencing reveals microbial functions in diverse environments. Challenges in analyzing vast data are addressed with current tools and strategies for reliable functional predictions.

Area of Science:

Microbiology
Bioinformatics

Background:

Metagenomic sequencing enables exploration of unculturable microbes and their functions in various environments.
Understanding microbial roles is crucial for microbiology, but large datasets pose significant analytical challenges.

Purpose of the Study:

To provide an overview of current functional analysis methods for metagenomic data.
To identify bottlenecks in metagenomic data analysis and propose solutions.
To highlight successful applications of metagenomic functional analysis.

Main Methods:

Review of current computational tools and resources for metagenomic functional analysis.
Discussion of factors influencing analysis, including data volume, sample preparation, sequencing methods, and read length.
Emphasis on homology-based annotation approaches using reference databases.

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Metagenomic Analysis of Silage

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Published on: January 13, 2017

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Published on: December 22, 2014

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Published on: December 7, 2021

Main Results:

Functional analysis of metagenomic data is hampered by large data volumes, requiring substantial computational resources.
Read length significantly impacts sequence assembly, gene prediction, and the confidence of functional predictions.
Homology-based methods are generally the most reliable for functional annotation.

Conclusions:

Despite challenges, advancements in tools and strategies facilitate robust functional analysis of metagenomic data.
Addressing computational and methodological bottlenecks is key to unlocking the potential of metagenomic studies.
Metagenomic functional analysis offers valuable insights into microbial communities and their ecological roles.