Operationalizing processes is a critical first step for labs running next-generation sequencing (NGS). By defining specific, routine lab workflows and operations, labs can keep scientists and technicians on task while supporting them in delivering quality results efficiently. In addition, setting defined tasks to undertake and complete enables labs to more easily set metrics tied to specific process steps, which they can use to optimize processes based on actual experiences managing samples, running instrumentation, and handling data.
The experts interviewed for our new white paper, “Next-Generation Sequencing Comes of Age: Tips from Industry Leaders,” identified tracking and subsequently measuring the effectiveness of lab processes as a critical capability in conducting efficient NGS. These experts advised NGS labs to treat the implementation of scientific workflows as seriously as they treat the science. By incorporating robust methods of tracking and measuring their work, labs can ultimately become better purveyors of NGS services.
“If you’re doing a large-scale, time-limited project, you can’t afford re-dos because you don’t have the right processes in place,” said the Managing Director of a genetic testing lab interviewed for our white paper. “We strive for best practice and that means having confidence in your instrumentation by verifying and validating it, having checks in place for the samples and reagents that come in and having good quality control.”
Defining metrics means looking past the reams of sequencing data your lab produces to explore the underlying details associated with generating that data. For instance, tracking consistent data about samples enables you to compare what happens to different samples over time or evaluate the efficiency of different workflows. Other necessary information for improving performance and efficiency can come from studying quality control results, analytical and automated robotic instrumentation performance specifications, queue and workflow updates, sequencing run metrics, and bioinformatics pipelining summary statistics.
However, for this data to be digested and acted on, it must be collected and captured under one umbrella. For many labs, this means incorporating a LIMS.
“A LIMS helps users make good decisions as they work, but it also collects all the data in one place so that you can start to be smart about the best way to run processes in the future,” said the Genomics Laboratory Manager and Senior Scientist from a large biopharma company. The end game, according to the genetic testing lab’s Managing Director, is tracking and tracing as much as possible, so that “work proceeds efficiently, with minimal touches, and the team can focus on delivering the results that will change the way medicine is practiced.”
To find out more about how labs are measuring processes to improve efficiency, download our white paper today.