The META tool optimizes metagenomic analyses across sequencing platforms and classifiers.

Player, R. A.; Aguinaldo, A. M.; Merritt, B. B.; Maszkiewicz, L. N.; Adeyemo, O. E.; Forsyth, E. R.; Verratti, K. J.; Chee, B. W.; Grady, S. L.; Bradburne, C. E.

doi:10.1101/2021.07.29.454031

bioRxiv · 10.1101/2021.07.29.454031

The META tool optimizes metagenomic analyses across sequencing platforms and classifiers.

Player, R. A.·Aguinaldo, A. M.·Merritt, B. B.·Maszkiewicz, L. N.·Adeyemo, O. E.·Forsyth, E. R.·Verratti, K. J.·Chee, B. W.·Grady, S. L.·Bradburne, C. E.

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Abstract

A major challenge in the field of metagenomics is the selection of the correct combination of sequencing platform and downstream metagenomic analysis algorithm, or classifier. Here, we present the Metagenomic Evaluation Tool Analyzer (META), which produces simulated data and facilitates platform and algorithm selection for any given metagenomic use case. META-generated in silico read data are modular, scalable, and reflect user-defined community profiles, while the downstream analysis is done using a variety of metagenomic classifiers. Reported results include information on resource utilization, time-to-answer, and performance. Real-world data can also be analyzed using selected classifiers and results benchmarked against simulations. To test the utility of the META software, simulated data was compared to real-world viral and bacterial metagenomic samples run on four different sequencers and analyzed using 12 metagenomic classifiers. Lastly, we introduce META Score: a unified, quantitative value which rates an analytic classifiers ability to both identify and count taxa in a representative sample.

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Player, R. A., Aguinaldo, A. M., Merritt, B. B., Maszkiewicz, L. N., Adeyemo, O. E., Forsyth, E. R., Verratti, K. J., Chee, B. W., Grady, S. L., Bradburne, C. E.. 2021-07-29. The META tool optimizes metagenomic analyses across sequencing platforms and classifiers.. https://doi.org/10.1101/2021.07.29.454031

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