End-to-End Data Automation for Pooled Sample SARS-CoV-2 Using R and Other Open-Source Tools

Author:

Mobini Mahdi12,Matic Nancy13,Gugten J Grace Van Der1,Ritchie Gordon13,Lowe Christopher F13,Holmes Daniel T13

Affiliation:

1. St. Paul’s Hospital Department of Pathology and Laboratory Medicine , Vancouver, BC , Canada

2. Providence Health Emerging Technologies , Vancouver, BC , Canada

3. University of British Columbia Department of Pathology and Laboratory Medicine , Vancouver, BC , Canada

Abstract

Abstract Background Due to supply chain shortages of reagents for real-time (RT)-PCR for SARS-CoV-2 and increasing demand on technical staff, an end-to-end data automation strategy for SARS-CoV-2 sample pooling and singleton analysis became necessary in the summer of 2020. Methods Using entirely open source software tools—Linux, bash, R, RShiny, ShinyProxy, and Docker—we developed a modular software application stack to manage the preanalytical, analytical, and postanalytical processes for singleton and pooled testing in a 5-week time frame. Results Pooling was operationalized for 81 days, during which time 64 pooled runs were performed for a total of 5320 sample pools and approximately 21 280 patient samples in 4:1 format. A total of 17 580 negative pooled results were released in bulk. After pooling was discontinued, the application stack was used for singleton analysis and modified to release all viral RT-PCR results from our laboratory. To date, 236 109 samples have been processed avoiding over 610 000 transcriptions. Conclusions We present an end-to-end data automation strategy connecting 11 devices, one network attached storage, 2 Linux servers, and the laboratory information system.

Funder

Roche Diagnostics

Providence Health Research Institute

Canada Foundation for Innovation

Genome British Columbia

Publisher

Oxford University Press (OUP)

Subject

General Medicine

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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2. Developing Data-Centric Clinical Laboratory Workflow Through the Use of Open-Source Tools;The Journal of Applied Laboratory Medicine;2023-01-04

3. New Guides for Uncertainty of Qualitative Results;The Journal of Applied Laboratory Medicine;2023-01-04

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