tetano
Editor, Senior Moderator
Sci Rep
. 2020 Dec 10;10(1):21759.
doi: 10.1038/s41598-020-78942-7.
Reliable and accurate diagnostics from highly multiplexed sequencing assays
A Sina Booeshaghi[SUP] 1 [/SUP], Nathan B Lubock[SUP] 2 [/SUP], Aaron R Cooper[SUP] 2 [/SUP], Scott W Simpkins[SUP] 2 [/SUP], Joshua S Bloom[SUP] 2 3 [/SUP], Jase Gehring[SUP] 4 [/SUP], Laura Luebbert[SUP] 5 [/SUP], Sri Kosuri[SUP] 2 [/SUP], Lior Pachter[SUP] 6 7 [/SUP]
Affiliations
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.
. 2020 Dec 10;10(1):21759.
doi: 10.1038/s41598-020-78942-7.
Reliable and accurate diagnostics from highly multiplexed sequencing assays
A Sina Booeshaghi[SUP] 1 [/SUP], Nathan B Lubock[SUP] 2 [/SUP], Aaron R Cooper[SUP] 2 [/SUP], Scott W Simpkins[SUP] 2 [/SUP], Joshua S Bloom[SUP] 2 3 [/SUP], Jase Gehring[SUP] 4 [/SUP], Laura Luebbert[SUP] 5 [/SUP], Sri Kosuri[SUP] 2 [/SUP], Lior Pachter[SUP] 6 7 [/SUP]
Affiliations
- PMID: 33303831
- DOI: 10.1038/s41598-020-78942-7
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.