Author: Kille, B.; Liu, Y. X.; Sapoval, N.; Nute, M.; Rauchwerger, L.; Amato, N.; Treangen, T. J.; Ieee,; Liu, Y.
Title: Accelerating SARS-CoV-2 low frequency variant calling on ultra deep sequencing datasets Cord-id: 3gu112a4 Document date: 2021_1_1
ID: 3gu112a4
Snippet: With recent advances in sequencing technology it has become affordable and practical to sequence genomes to very high depth-of-coverage, allowing researchers to discover low-frequency variants in the genome. However, due to the errors in sequencing it is an active area of research to develop algorithms that can separate noise from the true variants. LoFreq is a state of the art algorithm for low-frequency variant detection but has a relatively long runtime compared to other tools. In addition to
Document: With recent advances in sequencing technology it has become affordable and practical to sequence genomes to very high depth-of-coverage, allowing researchers to discover low-frequency variants in the genome. However, due to the errors in sequencing it is an active area of research to develop algorithms that can separate noise from the true variants. LoFreq is a state of the art algorithm for low-frequency variant detection but has a relatively long runtime compared to other tools. In addition to this, the interface for running in parallel could be simplified, allowing for multithreading as well as distributing jobs to a cluster. In this work we describe some specific contributions to LoFreq that remedy these issues.
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