Understanding Genomic Sequencing Workflows Through Data-Driven Analysis and Pilot Design

Abstract

This study developed and designed a data-driven process improvement pilot for COVID-19 genomic sequencing at the Research Institute of the Puerto Rico Science, Technology & Research Trust, based on workflow mapping and analysis. The workflow includes key stages such as RNA extraction, library preparation, sequencing, and bioinformatics analysis, where variability can propagate across interconnected processes and impact overall performance. A data-driven case study approach was used, combining qualitative and quantitative analysis to evaluate operational performance over a three-month period. Key metrics analyzed included first-pass yield, rework rates, and turnaround time, providing visibility into workflow inefficiencies and performance gaps. Based on this analysis, the study develops a structured framework to identify sources of variability and proposes a data-driven pilot design to evaluate potential process improvements in a controlled environment. This work bridges the gap between process analysis and implementation planning in laboratory environments.