Reducing Errors in Whole Genome Sequencing: It Starts With Library Prep
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Advances in whole genome sequencing (WGS) have opened the door to a deeper study of the human genome, leading to a better understanding of diseases and disorders as well as their potential treatments. As advancements in technology make WGS faster, more accessible and cost-efficient, the scientific community is seeing tremendous potential for applications in disease diagnostics as well as therapy and drug development. I’ve witnessed this trend first-hand, too. Increasingly, researchers come to Thermo Fisher Scientific to accelerate, simplify, and scale their sequencing experiments.
Going one step further, sequencing is now being used to generate preventative insights. For example, in line with the idea that prevention is the best treatment, biotech companies such as Genenius Genetics are tackling healthcare issues at their root with the aim of improving global health.
Traditionally, genome sequencing has been a focus in the research sphere, but scientists have begun to find more clinical applications. In addition to research labs and academic research centers, more and more clinical institutions are turning to high-throughput WGS projects, and healthcare providers are looking to make it a part of routine care in the future.
Towards stronger collaborations between researchers and clinicians
This shift toward preparation for clinical adoption is encouraging stronger collaboration between researchers and clinicians. Historically, for example, even medical programs closely affiliated with teaching hospitals would rarely work closely with research labs. Today, however, MD and PhD students often partner to develop more comprehensive sequencing projects rooted in research insights yet geared toward clinical application; a trend indicative of the direction the industry as a whole is moving in.
This highly complex process comes with a high cost of error, however. For scientists conducting genome sequencing, one pipetting or mixing mistake can lead to costly errors downstream. When working with multiple samples, error-free sample handling and proper mixing becomes even more critical. The first step in the process to prepare the sample for sequencing, library prep, is a vital step that, if not done correctly, can lead to flawed data and costly delays.
What is library prep?
In simple terms, library prep is the process of getting the DNA or RNA genomic material in the right format so you can sequence it.
Think about a whole human genome, which consists of 3 billion base pairs. Sequencing the entire chain would take a very long time. But if we were to chop up the genome at certain points, sequence all the pieces simultaneously and then assemble the data back together, we would drastically reduce processing time. Library prep helps to get the sample into a state that’s manageable and can be read by the sequencing system.
Library prep: It's make or break
As the first step in the process, library prep can potentially make or break your experiment. If it’s not done well, even if all subsequent steps in the workflow are conducted perfectly, the data produced will be inaccurate. Given that library prep can take anywhere between one day to a week and sequencing will then take a few days, following which the large amount of data produced will need to be reassembled and analyzed, it could be over a month until the researcher realizes there’s been a mistake.
Beyond the extensive loss of time and money, this also means that sample insights may be lost. Many samples are precious and do not contain enough DNA or RNA to repeat the entire experiment. This is particularly difficult when samples are obtained from biobanks with informed patient consent for research purposes.
Unsurprisingly, one of the most common pain points in genome sequencing is the need to eliminate operator error. Researchers simply can’t afford to jeopardize success by waiting until data is ready for analysis to realize there has been an error in the library prep process.
How can we solve this problem?
To address this issue, researchers need a system that flags any errors in real time. That’s why when we designed the new InvitrogenTM CollibriTM DNA Library Prep Kits for IlluminaTM systems we included a tracking dye to quickly and easily visualize library prep progress. The tracking dye demonstrates that reagents are thoroughly mixed when the solution changes to one homogeneous color, while any incomplete addition or mixing of reagents is indicated by a lack of color change.
Though a simple idea, it can have far-reaching benefits for scientists. Most directly, they no longer have to wait until they get the data from their experiment or conduct trace analysis to realize that something went wrong; even then, not knowing exactly what or when. By ensuring immediate and easy detection of errors, these kits help speed up and streamline library preparation. Subsequently, valuable resources – time and people – can be redeployed from the experimental stage to the data analysis stage, easing the bottleneck currently common in the informatics phase and increasing the focus on insight discovery as facilities look to scale capabilities.
Older library prep technology did not achieve consistency in conversion of molecules to insights. Consistency is critical in development of tests which may offer health insights in the future. Genenius Genetics, the biotech company using sequencing to develop preventative insights, found consistency and reproducibility of WGS data to be a key challenge in its development of preventative genetic tests and a multi-cancer early detection product. The company turned to the Collibri library prep kits to reduce the error rate of sequencing projects and, as a result, the library prep success rate has increased significantly even for DNA inputs less than one nanogram.
Simple innovations to prevent complex errors
Though a tedious process, library prep is an essential aspect of WGS. As the technology for sequencing evolves and its applications grow, even simple innovations such as tracking dyes can help eliminate avoidable operator errors with costly downstream repercussions and allow scientists to focus less on the technicalities of their experiments and more on discovering valuable insights.
