<p>Taxonomic and functional analysis outcomes are greatly influenced by the algorithms and databases used by different software. The present study evaluated three widely used software; MG-RAST, MEGAN6 and Kraken2 for the analysis of the shotgun metagenomic data of saffron cormosphere. Kraken2 outperformed other two for taxonomy. It gave significantly higher alpha diversity values, indicating greater taxonomic diversity and evenness compared to MG-RAST and MEGAN6. The limitation of the Kraken2 is that it does not support functional analysis which both MG-RAST and MEGAN6 can do in addition to taxonomical analysis. Additionally, they can analyse sequence data generated by different sequencing methods such as Sanger, Illumina and PacBio. MG-RAST is comparatively easy to use and integrates large number of databases than MEGAN6, however data processing is relatively slow. Additionally, MEGAN6 has a feature of extraction of genes automatically, that allows user to study sub set of specific genes, though in MG-RAST, it can be done manually and the process is cumbersome. The difference in the outcome of these three software can be attributed to differences in the databases, algorithms, and parameters used by the three software. A combined approach using the results from more than one software can be considered to create a more comprehensive taxonomy and functional profile until a factotum software is developed.</p>

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Comparative evaluation of MG-RAST, MEGAN6 and Kraken2 for whole metagenome analysis of saffron corms for bacterial community structure and function

  • Nitika Sharma,
  • Ayushi Verma,
  • Sheetal Ambardar,
  • Sushmeeta Raj,
  • Jyoti Vakhlu

摘要

Taxonomic and functional analysis outcomes are greatly influenced by the algorithms and databases used by different software. The present study evaluated three widely used software; MG-RAST, MEGAN6 and Kraken2 for the analysis of the shotgun metagenomic data of saffron cormosphere. Kraken2 outperformed other two for taxonomy. It gave significantly higher alpha diversity values, indicating greater taxonomic diversity and evenness compared to MG-RAST and MEGAN6. The limitation of the Kraken2 is that it does not support functional analysis which both MG-RAST and MEGAN6 can do in addition to taxonomical analysis. Additionally, they can analyse sequence data generated by different sequencing methods such as Sanger, Illumina and PacBio. MG-RAST is comparatively easy to use and integrates large number of databases than MEGAN6, however data processing is relatively slow. Additionally, MEGAN6 has a feature of extraction of genes automatically, that allows user to study sub set of specific genes, though in MG-RAST, it can be done manually and the process is cumbersome. The difference in the outcome of these three software can be attributed to differences in the databases, algorithms, and parameters used by the three software. A combined approach using the results from more than one software can be considered to create a more comprehensive taxonomy and functional profile until a factotum software is developed.