Ryan is a Ph.D candidate in the Bioinformatics and Integrative Genomics (BIG) program at Harvard Medical School, and is a member of the Talkowski lab at Massachusetts General Hospital and The Broad Institute. He is supported by an award from the Graduate Research Fellowship Program (GRFP) through the National Science Foundation (NSF).

His research focuses on the structure of the human genome, and how changes to genome structure contribute to human diversity and disease. He has led and contributed to numerous large-scale sequencing studies, such as the Genome Aggregation Database (gnomAD), one of the most widely adopted reference catalogs of human genetic variation in the world.

Click here to read more about Ryan's research interests and publications, or to see his CV.

Structure Meets Function (Blog)

Strength in numbers: genetic sequencing of large populations is shaping the future of medicine

Recently, I wrote a short peice for a great local science news & media outlet, Science In The News (SITN), to explain how population-scale genetic sequencing is driving major advances in precision medicine.

The article is targeted primarily for the lay public (i.e. non-specialists), so it's mostly jargon-free. It gets even better for those among us who are more visually inclined learners: a fellow PhD student at HMS, Brad Wierbowski, put together some great graphical figures to explain the core concepts (thanks Brad!).

While not a dedicated,...

Read more about Strength in numbers: genetic sequencing of large populations is shaping the future of medicine


  • RyanLCollins13
    RyanLCollins13 @NicoChatron Definitely — I should have clarified, we only compared PacBio & Illumina here (no ONT) & also just SVs (setting aside differences in base accuracy & short variants). ONT cost/benefit seems more competitive. Looking forward to large datasets for apples-to-apples benchmarking!
  • RyanLCollins13
    RyanLCollins13 @mike_schatz @DrGeneUK @nanopore @illumina Impressive! Didn’t realize ONT got prices down that far for large-scale commitments For the analyst like me, competition between platforms is great. Data are data, and the switch to long reads is inevitable. Looking forward to larger datasets for more variant benchmarking
  • RyanLCollins13
    RyanLCollins13 @mike_schatz @DrGeneUK And again—all of this is as of 2020 in Boston. Fully expect this will change in the near future. Excited by the prospect of large-scale long-read WGS in humans! E.g. DECODE’s Nanopore project But sample sizes affordable with illumina for most of us are still pretty nice option
  • RyanLCollins13
    RyanLCollins13 @mike_schatz @DrGeneUK Cheaper sequencing would be great! For context, a few local factors in my 8:1 estimate above: - here, economy of scale currently favors ILL WGS - big $ diff between 30x PacBio CLR vs HiFi - ONT not evaluated here But no complaints here about price competitions b/t seq techs!