Dual-Database Improvement of Metagenomic Viral Read Classification: A Respiratory Virus Case Study Open Access
Decker, Charlie (Spring 2024)
Abstract
The COVID-19 pandemic has underscored the necessity for precise identification of viral pathogens to inform clinical and public health responses effectively, especially with respiratory viruses with overlapping clinical presentations. Metagenomics, a powerful tool for the genetic profiling of complex microbial communities, has emerged as a promising solution. Utilizing high-throughput sequencing, metagenomics enables the unbiased identification of pathogens in clinical samples, offering a broad-spectrum diagnostic approach that transcends the capabilities of targeted PCR tests. This study introduces a metagenomic pipeline designed to enhance the detection and classification of viral samples, employing a combination of Kraken for initial viral read classification and BLASTN for subsequent validation. This project’s objectives were twofold: first to develop and test the dual database approach, and second to assess the efficacy of this pipeline in identifying known respiratory viruses in samples previously tested negative for COVID-19 using BinaxNOW antigen tests. The results revealed that the pipeline successfully identified the presence of various respiratory viruses in the samples, including parainfluenza viruses 2 and 3, rhinoviruses A and C, and influenza B, showcasing its superior performance over traditional diagnostic methods. Notably, the pipeline reduced false classifications, a critical advantage in the clinical setting where accurate pathogen identification directly influences treatment decisions and infection control measures.
Table of Contents
Introduction 1
Methods 6
Results 12
Discussion 16
References 18
Table 1 22
Table 2 24
Table 3 25
Table 4 28 Figure 1 29
Figure 2 31
Figure 3 32
Figure 4 33
Figure 5 34
Figure 6 35
Figure 7 36
Figure 8 37
Figure 9 38
Figure 10 39
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