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https://www.arca.fiocruz.br/handle/icict/70047
ANELLOVIRUS ABUNDANCE AS AN INDICATOR FOR VIRAL METAGENOMIC CLASSIFIER UTILITY IN PLASMA SAMPLES.
Author
Affilliation
Universidade de São Paulo. Faculdade de Medicina de Ribeirão Preto. Programa de Pós-graduação em Oncologia Clínica, Células-Tronco e Terapia Celular. Ribeirão Preto, SP, Brasil.
Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz. Departamento de Zootecnia. Piracicaba, SP, Brasil.
Centro de Vigilância Viral e Avaliação Sorológica. São Paulo, SP, Brasil.
Burnett School of Medical Sciences. College of Medicine. University of Central Florida. Orlando, FL, USA.
Universidade Estadual da Bahia. Departamento de Ciências Exatas e Terra. Salvador, BA, Brasil. / Centre for Epidemic Response and Innovation. School of Data Science and Computational Thinking. Stellenbosch University. Stellenbosch, South Africa.
Universidade de São Paulo. Faculdade de Ciências Farmacêuticas de Ribeirão Preto. Departamento de Análises Clínicas, Toxicológicas e Bromatológicas. Ribeirão Preto, SP, Brasil.
Instituto Butantan. Laboratório de Toxinologia Aplicada. São Paulo, SP, Brasil.
Instituto Butantan. Laboratório de Bacteriologia. São Paulo, SP, Brasil.
Centro de Vigilância Viral e Avaliação Sorológica. São Paulo, SP, Brasil.
Department of Science and Technologies for Sustainable Development and One Health. Università Campus Bio-Medico di Roma. Rome, Italy. / Fundação Oswaldo Cruz. Instituto Rene Rachou. Belo Horizonte, MG, Brasil.
Centro de Vigilância Viral e Avaliação Sorológica. São Paulo, SP, Brasil.
Instituto Butantan. Centro de Vigilância Viral e Avaliação Sorológica. São Paulo, SP, Brasil.
Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz. Departamento de Zootecnia. Piracicaba, SP, Brasil.
Centro de Vigilância Viral e Avaliação Sorológica. São Paulo, SP, Brasil.
Burnett School of Medical Sciences. College of Medicine. University of Central Florida. Orlando, FL, USA.
Universidade Estadual da Bahia. Departamento de Ciências Exatas e Terra. Salvador, BA, Brasil. / Centre for Epidemic Response and Innovation. School of Data Science and Computational Thinking. Stellenbosch University. Stellenbosch, South Africa.
Universidade de São Paulo. Faculdade de Ciências Farmacêuticas de Ribeirão Preto. Departamento de Análises Clínicas, Toxicológicas e Bromatológicas. Ribeirão Preto, SP, Brasil.
Instituto Butantan. Laboratório de Toxinologia Aplicada. São Paulo, SP, Brasil.
Instituto Butantan. Laboratório de Bacteriologia. São Paulo, SP, Brasil.
Centro de Vigilância Viral e Avaliação Sorológica. São Paulo, SP, Brasil.
Department of Science and Technologies for Sustainable Development and One Health. Università Campus Bio-Medico di Roma. Rome, Italy. / Fundação Oswaldo Cruz. Instituto Rene Rachou. Belo Horizonte, MG, Brasil.
Centro de Vigilância Viral e Avaliação Sorológica. São Paulo, SP, Brasil.
Instituto Butantan. Centro de Vigilância Viral e Avaliação Sorológica. São Paulo, SP, Brasil.
Abstract
Background: Viral metagenomics has expanded significantly in recent years due to advancements in next-generation sequencing, establishing it as the leading method for identifying emerging viruses. A crucial step in metagenomics is taxonomic classification, where sequence data is assigned to specific taxa, thereby enabling the characterization of species composition within a sample. Various taxonomic classifiers have been developed in recent years, each employing distinct classification approaches that produce varying results and abundance profiles, even when analyzing the same sample.
Methods: In this study, we propose using the identification of Torque Teno Viruses (TTVs), from the Anelloviridae family, as indicators to evaluate the performance of four short-read-based metagenomic classifiers: Kraken2, Kaiju, CLARK and DIAMOND, when evaluating human plasma samples.
Results: Our results show that each classifier assigns TTV species at different abundance levels, potentially influencing the interpretation of diversity within samples. Specifically, nucleotide-based classifiers tend to detect a broader range of TTV species, indicating higher sensitivity, while amino acid-based classifiers like DIAMOND and CLARK display lower abundance indices. Interestingly, despite employing different algorithms and data types (protein-based vs. nucleotide-based), Kaiju and Kraken2 performed similarly.
Conclusion: Our study underscores the critical impact of classifier selection on diversity indices in metagenomic analyses. Kaiju effectively assigned a wide variety of TTV species, demonstrating it did not require a high volume of reads to capture diversity. Nucleotide-based classifiers like CLARK and Kraken2 showed superior sensitivity, which is valuable for detecting emerging or rare viruses. At the same time, protein-based approaches such as DIAMOND and Kaiju proved robust for identifying known species with low variability.
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