Data is more than just a hot topic; everyone is focusing on how they can better gather, handle and manipulate large data sets. FLG catches up with the CEO, Bill Moss, and CSO, Brandi Davis-Dusenbery from Seven Bridges to give their insight into what the future looks like for big data.
FLG: Hi Brandi, can you introduce yourself and tell us how you got into the field?
Brandi: In 2012, next-generation sequencing had reached a price point that was relatively attainable for larger-scale projects, I was working at the Harvard Stem Cell lab where we were just starting to run some RNA sequencing studies on a variety of cell types to try to understand the underlying mechanisms of ALS.
At that time, sequencing was still expensive enough that every run was critical, and, even more importantly, the samples that we were sequencing were extremely precious so, it was important that the data we generated could be as widely used and as useful as possible. But doing this caused all sorts of challenges — even with access to incredible local computational resources, analyzing our data was time consuming and creating the structures to ensure it could be well-preserved so that all of the analytics were reproducible was extremely burdensome.
So, with this experience, I felt acutely the problems that Seven Bridges was trying to solve and saw firsthand how enabling their suggestions could reduce the data logistics and orchestration challenges. In the six years since joining Seven Bridges, both sequencing and cloud costs have fallen dramatically, which not only creates more opportunity for researchers but also makes it dramatically more difficult to effectively ‘DIY’. Not only this, but we’re seeing today that many of our customers are looking to address much more than data management and orchestration, and Seven Bridges can be a huge accelerant in addressing diverse research challenges.
FLG: What are some of the biggest challenges you find people are struggling with?
Bill: We find that our customers struggle with the transition from basic bioinformatics to cataloguing and comparing patients, exploratory in silico experimentation via iterative process in order to optimize the processes of discovery, commercialization and market optimization through the expansion of indications per discovery. All of which ultimately leads to making more precision medicines available to those who need them at an accelerated rate, at more attainable price points and at continual increases in scale.
FLG: Which projects and collaborations are you most excited to be working on that you think could have the biggest patient impact?
Brandi: Like most of the research community today, we’re closely examining how our technology and expertise can accelerate understanding, prevention and cure for COVID- 19. In particular we’ve been exploring ways that our Graph Genome technology can improve strain detection from NGS without the time-consuming step of viral assembly. We’re also of course in close discussions with a variety of organizations to facilitate collaborative environments to speed dissemination of results in a secure manner.
In general, I’m extremely passionate about ensuring that the absolute maximal impact can be achieved from every piece of data. Patients do a great service to the community to participate in large efforts like the UK Biobank, as well as clinical trials conducted at large and small biotech. This allows us to not only accelerate basic scientific discoveries and the identification of novel drug targets but also identification of the right cohort for a particular drug trial. Furthermore, as the size and dimensionality of data sets increases, it’s important that individuals with diverse expertise can work together seamlessly — enabling this ‘team science’ is also front and center in our projects across the public sector.
FLG: What major shifts do you anticipate your clients making in the next 1-5 years?
Bill: First, we expect to see technologies to enable the most accurate and unbiased complex exploratory bioinformatics for new drug discovery via in silico genomic analysis, including tools like our Graph Genome Suite, to enhance standard reference for specific populations and analytic objectives.
Second, tertiary analysis tools like Seven Bridges ARIA to draw scientifically insightful conclusions from individual variants and patterns of variants across the entire human genome, which can be compared to in-depth longitudinal phenotypic and healthcare outcomes, across populations of hundreds of thousands or even millions of patients.
In my opinion, such capabilities will set the stage for an impactful application of AI/Machine Learning to further accelerate breakthroughs.
FLG: What advice would you give to pharmaceutical companies looking to improve the way in which they handle their data?
Bill: In order to do this most effectively, democratize access to the federated data, enabling R&D teams to analyze combinations of proprietary and federated data seamlessly through a single pane of glass, bringing the analysis to the data in a secure, compliant and interoperable manner.
FLG: Can you tell us a bit more about what is in the pipeline from Seven Bridges that may help Pharma to deliver on their goals?
Bill: At Seven Bridges, we believe that accelerating scientific innovation will drive the evolution of bioinformatics in precision medicine. And, increasing the accuracy and completeness of highly targeted custom analytic experiments within the iterative exploratory process will be the key to meaningful discoveries and ensuing breakthroughs. Seven Bridges is transforming the foundational approach to such analyses with our implementation of directed acyclic graphs for genomic analyses, which enables researchers to rapidly enhance the linear reference to remove inherent biases, more completely represent targeted populations, and empower researchers to more aggressively target the specific objective of their experiments. Combined with ARIA, these advancements will enable a greater number of precision medicine solutions to be made available to an ever-increasing number of highly targeted patient populations, resulting in transformative advancements in life sciences and a healthier global population.