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Integrative workflows for metagenomic analysis.

Efthymios Ladoukakis1, Fragiskos N Kolisis1, Aristotelis A Chatziioannou2

  • 1Laboratory of Biotechnology, Department of Chemical Engineering, School of Chemical Engineering, National Technical University of Athens Athens, Greece.

Frontiers in Cell and Developmental Biology
|December 6, 2014
PubMed
Summary

Next Generation Sequencing (NGS) has transformed metagenomic analysis, generating vast datasets. This review assesses bioinformatic pipelines for managing, processing, and annotating this complex data, highlighting cloud computing solutions.

Keywords:
bioinformaticscloud computingdistributed computingmetagenomicsworkflow engines

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Next Generation Sequencing (NGS) technologies have revolutionized metagenomic analysis.
  • NGS generates massive datasets, posing significant data management and storage challenges.
  • Traditional Sanger sequencing methods are costlier and less efficient than NGS.

Purpose of the Study:

  • To review and critically assess bioinformatic solutions for metagenomic data analysis.
  • To evaluate automated pipelines for data management, quality control, and annotation.
  • To address the computational resource demands of large-scale metagenomic analyses.

Main Methods:

  • Review of existing literature on metagenomic bioinformatic pipelines.
  • Critical assessment of automated tools for data management, quality control, and annotation.
  • Discussion of cloud computing infrastructure for handling large-scale data.

Main Results:

  • Identified key bioinformatic challenges in NGS-based metagenomics, including data volume and processing complexity.
  • Evaluated various automated pipelines and their suitability for different metagenomic tasks.
  • Highlighted the necessity and benefits of cloud computing for efficient analysis.

Conclusions:

  • Integrative bioinformatic solutions are crucial for effective metagenomic data analysis.
  • Automated pipelines streamline complex workflows from raw data to annotation.
  • Cloud computing provides essential scalability and resources for modern metagenomic research.