Optimization of Metagenomic Library Construction for Influenza A Virus and SARS-CoV-2: Systematic Comparison of rRNA Depletion Strategies and Fragmentation Orders

Background/Objectives: RNA virus metagenomic sequencing is a core technology for emerging infectious disease prevention and control, as well as for rapid pathogen identification. However, two major bottlenecks hinder its clinical application: the low fraction of informative sequencing reads caused by host rRNA contamination, and insufficient viral genome coverage. This study aimed to optimize the experimental parameters of RNA virus metagenomic sequencing, address the above bottlenecks, and establish a standardized workflow. Methods: Forty-five clinically positive samples (20 influenza virus-positive; 25 SARS-CoV-2-positive) were investigated in three parallel comparative experiments: rRNA depletion versus no depletion; probe-mediated RNase H digestion versus rRNA blocking; and two fragmentation timing strategies (fragmentation before versus after reverse transcription). Sequencing was performed on the GeneMind platform, and key performance metrics were systematically analyzed. Results: Following rRNA depletion, the host sequence proportion in the influenza virus and SARS-CoV-2 samples decreased from 39.5 to 90.5% to 3.6 to 32.2%, while the 10× genomic coverage increased from 0 to 99.4% to 98.1 to 100.0%. The proportion of host sequences captured by probe capture depletion (0.3–16.2%) was significantly (p < 0.05) lower than that captured by rRNA blocking module (14.3–92.3%). No significant differences were observed in the 10× genomic coverage (96.5–100.0%) or the fraction of effective viral reads between the two fragmentation strategies (p > 0.05). rRNA depletion is key to improving library quality, with post-capture probe digestion being optimal. Conclusions: The suggested optimization process will enhance sequencing efficiency and support the standardization of clinical RNA virus identification.