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

Area of Science:

• Clinical diagnostics
• Bioinformatics
• Laboratory automation

Background:

• Supply chain shortages of SARS-CoV-2 real-time (RT)-PCR reagents necessitated innovative solutions.
• Increased demand on technical staff highlighted the need for efficient laboratory workflows.

Purpose of the Study:

• To develop an end-to-end data automation strategy for SARS-CoV-2 sample pooling and singleton analysis.
• To implement a modular software application stack using open-source tools.

Main Methods:

• Utilized open-source software: Linux, bash, R, RShiny, ShinyProxy, and Docker.
• Developed a modular application stack to manage preanalytical, analytical, and postanalytical processes.
• Integrated 11 devices, network storage, Linux servers, and the laboratory information system.

Main Results:

• Operationalized sample pooling for 81 days, processing 5320 pools (approx. 21,280 samples).
• Successfully released 17,580 negative pooled results in bulk.
• Processed 236,109 samples for singleton analysis post-pooling, preventing over 610,000 transcriptions.

Conclusions:

• An end-to-end data automation strategy was successfully implemented.
• The developed software stack effectively managed both pooled and singleton SARS-CoV-2 testing.
• The automation strategy significantly reduced manual data entry and enhanced laboratory throughput.