From the earliest days of the pandemic that shocked the world in 2020, researchers at Fred Hutch Cancer Center tracked the rapid evolution and spread of the virus that causes COVID-19 by studying how its genomic sequence — the order of its genetic building blocks — changed over time.
Computational biologist Trevor Bedford, PhD, and evolutionary biologist Jesse Bloom, PhD, became go-to media sources, providing expert information that influenced consequential policy decisions about what to shut down and when.
Researchers have now amassed an enormous database of genomic sequences of SARS-CoV-2 and its many variants, sampled from more than 16 million patients around the world over the last five years.
But the sheer volume of genomic sequences — a dataset that is orders of magnitude bigger than what’s available for any other pathogen — has overwhelmed the capacity of common analytical methods to make sense of it in a timely and practical manner.
Two recently published papers from postdoctoral researchers in the Bedford and Bloom labs showcase new tools invented at Fred Hutch that provide researchers traction to make that mountain of data more manageable.
