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Published on: September 25, 2021

Exploring variation-aware contig graphs for (comparative) metagenomics using MaryGold.

Jurgen F Nijkamp1, Mihai Pop, Marcel J T Reinders

  • 1Department of Intelligent Systems, The Delft Bioinformatics Lab, Delft University of Technology, 2628 CD Delft, The Netherlands, Kluyver Centre for Genomics of Industrial Fermentation, 2600 GA Delft, The Netherlands and Department of Computer Science, Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.

Bioinformatics (Oxford, England)
|September 24, 2013
PubMed
Summary

MaryGold is a new reference-free algorithm for detecting genomic variation in metagenomes. It identifies variable regions in microbial communities and aids in understanding strain diversity across samples.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Metagenomics

Background:

  • Limited tools exist for studying genomic variation in metagenomes, hindering analysis of complex microbial communities.
  • Existing methods often rely on reference genomes, which is problematic for metagenomes with unknown strain compositions.
  • Metagenomics is crucial for microbiome, environmental, and food/beverage production research.

Purpose of the Study:

  • To develop a reference-free method for detecting and visualizing genomic variation within and between metagenomes.
  • To address the limitations of current algorithms in analyzing metagenomic datasets with unknown strain diversity.

Main Methods:

  • Developed the MaryGold algorithm, implemented in C++ and Python.
  • Utilizes graph decomposition to efficiently detect bubble structures in contig graphs, representing genomic variation.
  • Employs Circos-based visualization for exploring detected variations.

Main Results:

  • MaryGold successfully detected allelic variation in simulated Escherichia coli datasets, improving assemblies.
  • Applied to real-world metagenomic datasets, identifying within-sample variation in kimchi fermentation, infant microbiome, and acid mine drainage communities.
  • Demonstrated utility for between-sample variation detection by comparing time-series data.

Conclusions:

  • MaryGold provides an efficient, reference-free approach for analyzing genomic variation in metagenomes.
  • The algorithm facilitates exploration of strain diversity and evolutionary dynamics within microbial communities.
  • Available for download, supporting diverse metagenomic research applications.