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Metagenomic Analysis of Silage

Metagenomic Analysis of Silage

Published on: January 13, 2017

Web Resources for Metagenomics Studies.

Pravin Dudhagara¹, Sunil Bhavsar¹, Chintan Bhagat¹

¹Department of Biosciences, Veer Narmad South Gujarat University, Surat 395007, India.

Genomics, Proteomics & Bioinformatics

|November 26, 2015

Summary

This summary is machine-generated.

Next-generation sequencing (NGS) generates vast data, challenging bioinformatics tools. This review evaluates 12 online metagenomics analysis tools, highlighting their features and performance with simulated data.

Keywords:

Metagenomes Metagenomics Software tools Synthetic metagenome Web resources

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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

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Bioinformatics and Computational Biology
Genomics and Genetics
Microbiology

Background:

Next-generation sequencing (NGS) has led to an unprecedented volume of biological data.
Existing bioinformatics tools face scalability challenges in analyzing this massive data.
Metagenomic sequence analysis is complex due to the intricate nature of the data.

Purpose of the Study:

To review and evaluate commonly used online tools for metagenomics data analysis.
To assess the quality and detail of analysis provided by these tools using simulated data.
To provide a comprehensive overview of contemporary challenges and prospects in metagenomics from a bioinformatics perspective.

Main Methods:

Review of 12 publicly available online metagenomics analysis tools.
Assessment based on web link, annotation pipelines, clustering methods, user support, and data storage.
Evaluation of five top-performing tools using synthetic metagenome data.
Comparative analysis of tool features and performance.

Main Results:

Detailed description of 12 online metagenomics tools, including MGRAST, IMG/M, and METAVIR.
Rating and screening of tools to identify preferential options.
Evaluation of five selected tools using synthetic metagenome data to assess their capabilities.
Identification of strengths and weaknesses of various bioinformatics pipelines for metagenomics.

Conclusions:

The study provides a comparative analysis of online metagenomics tools, aiding researchers in selecting appropriate platforms.
Highlights the need for scalable and robust bioinformatics solutions to handle the growing volume of NGS data.
Offers insights into current challenges and future directions in computational metagenomics research.