AUTHOR=Player Robert A. , Aguinaldo Angeline M. , Merritt Brian B. , Maszkiewicz Lisa N. , Adeyemo Oluwaferanmi E. , Forsyth Ellen R. , Verratti Kathleen J. , Chee Brant W. , Grady Sarah L. , Bradburne Christopher E. 

TITLE=The META tool optimizes metagenomic analyses across sequencing platforms and classifiers

JOURNAL=Frontiers in Bioinformatics

VOLUME=2

YEAR=2023

URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2022.969247

DOI=10.3389/fbinf.2022.969247

ISSN=2673-7647

ABSTRACT=<p>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 <italic>in silico</italic> 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 classifier’s ability to both identify and count taxa in a representative sample.</p>