Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology · 2025
Some assembly required: Comparison of bioinformatic pipelines for analysis of viral metagenomic sequencing from nosocomial respiratory virus outbreaks.
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Alhaboub Assad, Deschenes Natalie M, Li Xena X, Williams Victoria R, Katz Kevin C, Park So Yeon, Aftanas Patryk, Wong Henry, Sjaarda Calvin, Tozer Kyla, Maguire Finlay, Leis Jerome, Sheth Prameet, Kozak Robert
Аннотация
INTRODUCTION: Metagenomic sequencing (mGS) is a useful tool for identifying pathogens in patient samples. During nosocomial outbreaks of respiratory viruses, mGS allows for the identification of viral strains and provides insight into their genetic relatedness. Multiple bioinformatics analysis assembler are available for processing data, but a comprehensive comparison of their performance in for respiratory virus outbreaks has not been conducted.
METHODS: This study sequenced samples from five separate nosocomial outbreaks of RNA respiratory viruses. RNA was extracted from the samples, and cDNA was synthesized using random hexamers, and then sequenced on an Illumina Miniseq following Nextera DNA Flex library preparation. The data from each outbreak were analyzed using four different assemblers: MEGAHIT, rnaSPAdes, rnaviralSPAdes, and coronaSPAdes, to evaluate their analytical performance.
RESULTS: The mGS confirmed the viral identification and provided accurate strain identification for both coronavirus and parainfluenza virus samples. However, differences were observed between the assemblers in terms of the largest contigs produced and the proportion of the viral genome aligned with reference genomes. Notably, coronaSpades outperformed the other pipelines for analyzing seasonal coronaviruses, generating more complete data and covering a higher percentage of the viral genome.
CONCLUSION: Achieving a higher percentage of the viral genome sequence is crucial for a more detailed characterization, which is especially valuable for outbreak analysis where viral strains may only differ by a few genetic changes. Comparison of assemblers will allow for clinical laboratories to determine the bioinformatic pipeline that is optimal for helping clinicians better manage outbreaks.
METHODS: This study sequenced samples from five separate nosocomial outbreaks of RNA respiratory viruses. RNA was extracted from the samples, and cDNA was synthesized using random hexamers, and then sequenced on an Illumina Miniseq following Nextera DNA Flex library preparation. The data from each outbreak were analyzed using four different assemblers: MEGAHIT, rnaSPAdes, rnaviralSPAdes, and coronaSPAdes, to evaluate their analytical performance.
RESULTS: The mGS confirmed the viral identification and provided accurate strain identification for both coronavirus and parainfluenza virus samples. However, differences were observed between the assemblers in terms of the largest contigs produced and the proportion of the viral genome aligned with reference genomes. Notably, coronaSpades outperformed the other pipelines for analyzing seasonal coronaviruses, generating more complete data and covering a higher percentage of the viral genome.
CONCLUSION: Achieving a higher percentage of the viral genome sequence is crucial for a more detailed characterization, which is especially valuable for outbreak analysis where viral strains may only differ by a few genetic changes. Comparison of assemblers will allow for clinical laboratories to determine the bioinformatic pipeline that is optimal for helping clinicians better manage outbreaks.