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Abstract
Scalable, inexpensive, and secure testing for SARS-CoV-2 infection is crucial for control of the novel coronavirus pandemic. Recently developed highly multiplexed sequencing assays (HMSAs) that rely on high-throughput sequencing can, in principle, meet these demands, and present promising alternatives to currently used RT-qPCR-based tests. However, reliable analysis, interpretation, and clinical use of HMSAs requires overcoming several computational, statistical and engineering challenges. Using recently acquired experimental data, we present and validate a computational workflow based on kallisto and bustools, that utilizes robust statistical methods and fast, memory efficient algorithms, to quickly, accurately and reliably process high-throughput sequencing data. We show that our workflow is effective at processing data from all recently proposed SARS-CoV-2 sequencing based diagnostic tests, and is generally applicable to any diagnostic HMSA.
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1 California Institute of Technology, Department of Mechanical Engineering, Pasadena, USA (GRID:grid.20861.3d) (ISNI:0000000107068890)
2 Octant Inc., Emeryville, USA (GRID:grid.20861.3d)
3 Octant Inc., Emeryville, USA (GRID:grid.20861.3d); University of California, Los Angeles, Department of Human Genetics, Los Angeles, USA (GRID:grid.19006.3e) (ISNI:0000 0000 9632 6718)
4 University of Washington, Department of Genome Sciences, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657)
5 California Institute of Technology, Division of Biology and Biological Engineering, Pasadena, USA (GRID:grid.20861.3d) (ISNI:0000000107068890)
6 California Institute of Technology, Division of Biology and Biological Engineering, Pasadena, USA (GRID:grid.20861.3d) (ISNI:0000000107068890); California Institute of Technology, Department of Computing and Mathematical Sciences, Pasadena, USA (GRID:grid.20861.3d) (ISNI:0000000107068890)