ADAPT: Designing Activity-Informed Viral Diagnostic Assays

I wanted to give a pointer to a new preprint on bioRxiv on developing diagnostic assays for viruses, by (first author) Hayden Metsky (and others!) out of the Sabeti Lab at the Broad Institute (that I've been a bit involved with).  Hayden, who somehow is both a computer science PhD and an expert in virology, has devised a novel software pipeline for developing diagnostics that are designed from the start to deal with genomic diversity (a virus evolves to have many somewhat different variants) and the challenge of false matches (you don't want to get false positives from matching some other different virus) -- also known as sensitivity and specificity.  Algorithmically, he uses machine learning to determine scores for possible tests for matches to small pieces of the genome, or probes, and utilizes locality-sensitive hashing, combinatorial optimization algorithms for submodular maximization, and sharding pattern matching across tries as substages in the overall design.  

I am always excited to see algorithmic ideas being used to solve real-world problems, and this is a deep and difficult example of the "algorithmic lens"  at work.  I am optimistically hopeful that this type of technology will help drive the development of viral diagnostic and monitoring methods forward.     

Some additional pointers:  to code, to some array designs for SARS-CoV-2, and Hayden's twitter thread describing the work.