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With recent advances in sequencing technology it has become affordable andpractical to sequence genomes to very high depth-of-coverage, allowingresearchers to discover low-frequency variants in the genome. However, due tothe errors in sequencing it is an active area of research to develop algorithmsthat can separate noise from the true variants. LoFreq is a state of the artalgorithm for low-frequency variant detection but has a relatively long runtimecompared to other tools. In addition to this, the interface for running inparallel could be simplified, allowing for multithreading as well asdistributing jobs to a cluster. In this work we describe some specificcontributions to LoFreq that remedy these issues.

Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets