Reliable and accurate diagnostics from highly multiplexed sequencing assays
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.
Additional Information
© 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Received 02 November 2020; Accepted 24 November 2020; Published 10 December 2020. We thank Páll Melsted for assistance with bustools. Author Contributions: A.S.B. and L.P. developed the kallisto|bustools approach to processing and analyzing HMSA data. A.S.B. adapted kallisto and bustools to process SwabSeq, LAMP-seq, covE-seq, and TRB-seq data. A.S.B. performed the analyses and collected results for the paper. N.L. developed the bcl2fastq + starcode processing approach with assistance from A.R.C., S.W.S., and J.S.B. J.G. assisted with technical aspects of the SwabSeq assay and in assessing the kallisto|bustools workflow results. L.L. created Fig. 1 and explored the sample index structures of LAMP-seq, TRB-seq, and SwabSeq. S.K., N.L.B., A.R.C., S.W.S. and J.S.B. developed SwabSeq. A.S.B., L.L., and L.P. wrote the manuscript. Competing interests: A.S.B., J.G., L.L., and L.P. declare no conflicts of interest. S.K., N.L.B., A.R.C., S.W.S. and J.S.B. are employees of Ocant, which developed SwabSeq. SwabSeq is released under the terms of the Octant Covid License20.Attached Files
Published - s41598-020-78942-7.pdf
Submitted - 2020.05.13.20100131v1.full.pdf
Supplemental Material - 41598_2020_78942_MOESM1_ESM.docx
Supplemental Material - 41598_2020_78942_MOESM2_ESM.docx
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Additional details
- Alternative title
- Fast and accurate diagnostics from highly multiplexed sequencing assays
- PMCID
- PMC7730459
- Eprint ID
- 103585
- Resolver ID
- CaltechAUTHORS:20200601-101849395
- Created
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2020-06-01Created from EPrint's datestamp field
- Updated
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2023-06-01Created from EPrint's last_modified field
- Caltech groups
- COVID-19, Division of Biology and Biological Engineering