Rong Chen: making sense of big data
What’s the secret to making use of big data in healthcare? Asking very specific questions. That’s exactly what Rong Chen, a one-person whirlwind of bioinformatics innovation has been doing for 20 years.
Big Data. It means many different things to different people. To Rong Chen, it means collecting as much data as he can and putting it to good use. But rather than go fishing for patterns, he uses his data to answer very specific questions to help physicians do their job much more efficiently, and improve patient outcomes. As Director of Clinical Genome Informatics at Mount Sinai, he’s not only developing sophisticated computational tools, he’s speaking to physicians each and every day to make sure integration into healthcare is smooth and easily actionable. We were fortunate enough to catch up with him as he stepped off stage at the Festival of Genomics San Diego.
FLG: What got you interesting in science as a boy?
RC: When I was a kid I always had curiosities that I wanted to explore, and my mom always said I should become a scientist. In terms of data driven genomics, I’ve really been into bioinformatics since my Ph.D. at Boston University. I’ve always been interested in asking questions rather than building tools. In particular biological, or clinical, questions and finding and integrating big data to answer them. I’ve been following what big data is for about twenty years now. I started off looking at thousands of protein structures to build statistical potential then predict how two proteins bind. Then I started to look at millions of microarray data to find disease biomarkers. When NGS came, I started to collate 20,000 genomic papers to develop a tool to generate genetic reports. At Mount Sinai, I began curating every single sequenced genome available there, and using it to build a comprehensive genetic data variant database to support clinical genetic testing.
So even though I change the questions I’m asking, I always follow where the biggest amount of data is. As proteomics data is about to become big, I’m pretty sure I’ll be switching to that as well. Following the data is really what’s been connecting my whole career.
FLG: Your scientific career has taken you to a lot of different organisations over the years. This must have given you an interesting perspective on how genomics, and translational bioinformatics has progressed. What has been the biggest change you’ve seen in the field throughout your career so far?
RC: Definitely NGS. Before NGS, most big data stayed in the academic field, like the Protein Bank. Only a few companies were using this data, but never really on a large scale. Now with NGS, the opportunity for industry really increased, and there is much less distinction between academia and industry when it comes to big data.
FLG: You’ve been described as a one-person whirlwind of bioinformatics innovation. That seems a very apt description. Looking through your CV, it’s hard to pick out one of your placements, as being the one you really made your name at. Do you look back at any of your previous roles as being the one that really launched your career?
RC: It really took off with the publication of ‘Clinical assessment incorporating a personal genome’ in Lancet. Before that, I was essentially an academic working in a very focused field. When the paper came out, it opened the door for more big data based genome reports. That brought the opportunity to launch Personalis, and that helped my career as well. It made me a much better leader, and forced me to think about what would make a good product, rather than just a good paper. Now at Mount Sinai, I have a larger team developing new products as well as my own lab working on papers!
You can read the rest of the interview on page 13 of the latest issue of FLG magazine!