The META tool optimizes metagenomic analyses across sequencing platforms and classifiers
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 a...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Bioinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2022.969247/full |
| _version_ | 1828070528205717504 |
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| author | Robert A. Player Angeline M. Aguinaldo Brian B. Merritt Lisa N. Maszkiewicz Oluwaferanmi E. Adeyemo Ellen R. Forsyth Kathleen J. Verratti Brant W. Chee Brant W. Chee Sarah L. Grady Christopher E. Bradburne Christopher E. Bradburne |
| author_facet | Robert A. Player Angeline M. Aguinaldo Brian B. Merritt Lisa N. Maszkiewicz Oluwaferanmi E. Adeyemo Ellen R. Forsyth Kathleen J. Verratti Brant W. Chee Brant W. Chee Sarah L. Grady Christopher E. Bradburne Christopher E. Bradburne |
| author_sort | Robert A. Player |
| collection | DOAJ |
| description | 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 classifier’s ability to both identify and count taxa in a representative sample. |
| first_indexed | 2024-04-11T00:39:09Z |
| format | Article |
| id | doaj.art-6b33a67f56b842d08b5c9e4c7c32a6b4 |
| institution | Directory Open Access Journal |
| issn | 2673-7647 |
| language | English |
| last_indexed | 2024-04-11T00:39:09Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj.art-6b33a67f56b842d08b5c9e4c7c32a6b42023-01-06T13:15:07ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472023-01-01210.3389/fbinf.2022.969247969247The META tool optimizes metagenomic analyses across sequencing platforms and classifiersRobert A. Player0Angeline M. Aguinaldo1Brian B. Merritt2Lisa N. Maszkiewicz3Oluwaferanmi E. Adeyemo4Ellen R. Forsyth5Kathleen J. Verratti6Brant W. Chee7Brant W. Chee8Sarah L. Grady9Christopher E. Bradburne10Christopher E. Bradburne11Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, United StatesApplied Physics Laboratory, Johns Hopkins University, Laurel, MD, United StatesApplied Physics Laboratory, Johns Hopkins University, Laurel, MD, United StatesApplied Physics Laboratory, Johns Hopkins University, Laurel, MD, United StatesApplied Physics Laboratory, Johns Hopkins University, Laurel, MD, United StatesApplied Physics Laboratory, Johns Hopkins University, Laurel, MD, United StatesApplied Physics Laboratory, Johns Hopkins University, Laurel, MD, United StatesDivision of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United StatesArmstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, United StatesApplied Physics Laboratory, Johns Hopkins University, Laurel, MD, United StatesApplied Physics Laboratory, Johns Hopkins University, Laurel, MD, United StatesMcKusick-Nathans Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United StatesA 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 classifier’s ability to both identify and count taxa in a representative sample.https://www.frontiersin.org/articles/10.3389/fbinf.2022.969247/fullmetagenomicsmetagenomic classificationtesting and evaluationNGSoxford nanoporeillumina |
| spellingShingle | Robert A. Player Angeline M. Aguinaldo Brian B. Merritt Lisa N. Maszkiewicz Oluwaferanmi E. Adeyemo Ellen R. Forsyth Kathleen J. Verratti Brant W. Chee Brant W. Chee Sarah L. Grady Christopher E. Bradburne Christopher E. Bradburne The META tool optimizes metagenomic analyses across sequencing platforms and classifiers Frontiers in Bioinformatics metagenomics metagenomic classification testing and evaluation NGS oxford nanopore illumina |
| title | The META tool optimizes metagenomic analyses across sequencing platforms and classifiers |
| title_full | The META tool optimizes metagenomic analyses across sequencing platforms and classifiers |
| title_fullStr | The META tool optimizes metagenomic analyses across sequencing platforms and classifiers |
| title_full_unstemmed | The META tool optimizes metagenomic analyses across sequencing platforms and classifiers |
| title_short | The META tool optimizes metagenomic analyses across sequencing platforms and classifiers |
| title_sort | meta tool optimizes metagenomic analyses across sequencing platforms and classifiers |
| topic | metagenomics metagenomic classification testing and evaluation NGS oxford nanopore illumina |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2022.969247/full |
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