Comparison of Metagenomic Tools in Gut Microbiome Analysis of COVID-19 Patients
Muhamad Rifki Ramadhan, Ramadhan
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Coronavirus disease 2019 or COVID-19 is originated in Hubei province of China in late 2019. Since then, it spread worldwide and caused a worldwide pandemic through most of 2020 and 2021. Gut microbiota has a key role in human health through its protective, trophic, and metabolic actions. Alteration of gut microbiota or gut dysbiosis is found in other virus infection (e.g., hepatitis B, HIV, and influenza). This raises a possibility that COVID-19 might also influence the gut microbiota. Metagenomic analysis is a powerful method to analyze microbiome composition in an environment. In this review, five studies that used metagenomic sequencing to analyze gut microbiome composition in COVID-19 patients were compared. The methods compared include taxonomic profiling, functional annotation, and differential abundance analysis. Based on the approach and tools used, one study differs substantially from the other four studies. This study used protein to protein BLAST for taxonomic profiling, manual alignment to various databases for functional annotation, and non-parametric test of Kruskal-Wallis H and Wilcoxon rank-sum test for differential abundance. In contrast, all four other studies used the biobakery pipeline (MetaPhlAn for taxonomic profiling and HUMAnN for functional annotation) and MaAsLin for the differential abundance analysis of the microbial taxa and pathway. The dissimilarity of the methods between these studies is reflected in the results. The results from each of the four other studies share more agreements with each other even though they are quite different as well. Besides the methods and tools used, the results of these studies are affected by their experimental design (e.g., sample size, sample collections, patients’ comorbidities), and the gut microbiota itself is influenced by a lot of other factors (e.g., diet, lifestyle, and medication). It is very difficult to point out whether the differences in the results are caused by the different tools used.