Abstract

Viral identification for terabyte-scale metagenomic data is limited by scalability and computational resources. We present vOMIX-MEGA, an end-to-end viral metagenomic framework that overcomes performance bottlenecks by significantly improving parallelisation and memory usage in critical steps. Benchmarked on empirical datasets, it completes processing in up to 50 minutes with 24 GB of RAM, bypassing four other state-of-the-art pipelines that require 7 hours (383 GB) to 14 days (32 GB). vOMIX-MEGA is on average 21% and 13% more accurate when benchmarked on mock and experimental data, and is available via https://github.com/holab-hku/vOMIX-MEGA.