A statistical reference-free genomic algorithm subsumes common workflows and enables novel discovery

Kaitlin Chaung, Tavor Baharav, Ivan Zheludev, Julia Salzman

Abstract:

We introduce a probabilistic model that enables study of myriad, disparate and
fundamental problems in genome science and expands the scope of inference currently possible.
Our model formulates an unrecognized unifying goal of many biological studies – to discover
sample-specific sequence diversification – and subsumes many application-specific models.
With it, we develop a novel algorithm, NOMAD, that performs valid statistical inference on raw
reads, completely bypassing references and sample metadata. NOMAD's reference-free approach
enables data-scientifically driven discovery with previously unattainable generality, illustrated
with de novo prediction of adaptation in SARS-CoV-2, novel single-cell resolved,
cell-type-specific isoform expression, including in the major histocompatibility complex, and de
novo identification of V(D)J recombination. NOMAD is a unifying, provably valid and highly
efficient algorithmic solution that enables expansive discovery.