Cell and Gene Therapy Biotechnology Company Case Study
Time is everything for a bioinformatician. With an active R&D group constantly creating new datasets that need to be analyzed, keeping up with their requests is an uphill battle. The newly hired Director of Bioinformatics at a leading biopharmaceutical company specialized in using Natural Killer Cells to treat cancer understood this predicament all too well. He had years of experience building custom bioinformatics analysis pipelines for companies focused on using single cell multi omics approaches to discover new therapeutics in the fight against cancer. His goal was to build a scalable bioinformatics infrastructure to meet the ever increasing analysis needs of this growing company. Even though he recognized that traditionally this could be done by adding headcount as the needs increased, he knew from experience that this approach was difficult to scale in the long term and so set out to find a better way to help bioinformaticians and bench scientists work more efficiently together.
Focused on cell and gene therapies, the bench scientists at his company worked around the clock to discover and develop Natural Killer (NK) Cell therapies to treat cancer. Throughout their development process they created increasing amounts of NGS data across multiple application areas that required analysis. But without a way for scientists to perform routine analysis independently, they were experiencing a significant bottleneck in analysis that was delaying time to scientific insight by several months. According to the Director of Bioinformatics “our primary goal was to enable every non-bioinformatician to become some sort of data scientist. With that vision we started evaluating possible solutions including building our own in-house software and commercially available off-the-shelf solutions.”
He had experience using first generation bioinformatics platforms that offered analysis capabilities, but was disappointed with their low level of adoption. From experience he knew this was largely because bench scientists found the interface too difficult to use. In addition, he realized that they just didn’t scale cost effectively and ultimately the price tag far outweighed the value received. He saw this as an opportunity to challenge the status quo. Instead, his vision was for a user-friendly, point-and-click interface for bench scientists with out-of-the-box core visualization and reporting capabilities that could integrate multiple data sets. “I wanted a platform in which initial analysis could be done by a bench scientist before a bioinformatician would jump in to assist with more advanced methodologies such as machine learning and AI, the objective being to help them gain deeper insights from their data before needing our help. Ideally, this is what a bioinformatician should be doing as opposed to crunching primary or routine analysis data that can otherwise be accomplished by bench scientists running automated pipelines.” His first solution was to run bioinformatics pipelines directly on AWS, but he quickly found that this approach to cloud resources was hard to optimize for cost effectiveness. “It would have required creating a new team at the company with several full time employees to build, maintain, and expand a platform with a user interface that was amenable to bench scientists.” As a result, their scientists were still fully reliant on bioinformatics personnel to run routine analyses. Whatsmore, the bioinformatics team was drowning in analysis requests from R&D and unable to allocate time to solving more complex problems and designing new custom pipelines. Ultimately they wanted the benefits of a SaaS platform without losing control of the cloud resources being used not just to store their data, but also to analyze it. Eventually, he was recommended by a colleague to try Basepair, a next generation cloud-based NGS analysis and visualization platform designed with both the bench scientist and the bioinformatician in mind.
During the evaluation period with Basepair he was pleasantly surprised to see that instead of having to sit through a 3 or 4 day training course, bench scientists only needed an hour or two of training to to run approved pipelines built and deployed by bioinformatics and to interpret the results using the built in interactive visualizations and reports. Moreover, being able to simply plug their own cloud account into Basepair meant they could leverage the existing compute and storage resources in their own cloud account to analyze and store their data, enabling them to benefit from the economies of scale with their cloud provider. He estimated that using Basepair would likely save them up to 50% on compute costs because the platform was able to select optimal instance types for the type of analysis being run and even spin up and down both spot and on demand instances as needed. Moreover, Basepair’s powerful command line interface (CLI) meant the bioinformaticians could interact with the platform in a way that was more conducive to the way they worked, not to mention that the APIs meant that the level of effort needed to get data into and out of the platform would be greatly reduced. Ultimately, he felt this would enable them to focus their time on more valuable tasks such as the use of data lakes to integrate genomic data sets and subsequently leveraging ML/AI approaches to deliver scientific insights.
What is the mission of a bioinformatician? This Director of Bioinformatics believed that by providing automation through Basepair, empowering bench scientists to run pipelines themselves through the user-friendly UI, bioinformaticians could be relieved of running routine repetitive tasks. With this newfound freedom, bioinformaticians could instead spend more time on advanced approaches that ultimately accelerate time to scientific insight. Ultimately Basepair was not brought on to replace the important role that bioinformaticians play in R&D teams, but instead to take away the burden of routine work and data manipulation that all too often they find themselves doing. While growing a team and adding resources will help, it is not enough nor is it scalable. He and his company believed there was a better way. By leveraging Basepair as an NGS analysis platform they gave their bioinformaticians and scientists superpowers and allowed them to use their exceptional capabilities on projects that are worthy of their skill sets.