SPLASH: a statistical, reference-free genomic algorithm unifies biological discovery

Abstract

Today's genomics workflows typically require alignment to a reference sequence, which limits discovery. We introduce a new unifying paradigm, SPLASH (Statistically Primary aLignment Agnostic Sequence Homing), an approach that directly analyzes raw sequencing data to detect a signature of regulation: sample-specific sequence variation. The approach, which includes a new statistical test, is computationally efficient and can be run at scale. SPLASH unifies detection of myriad forms of sequence variation. We demonstrate that SPLASH identifies complex mutation patterns in SARS-CoV-2 strains, discovers regulated RNA isoforms at the single cell level, documents the vast sequence diversity of adaptive immune receptors, and uncovers biology in non-model organisms undocumented in their reference genomes: geographic and seasonal variation and diatom association in eelgrass, an oceanic plant impacted by climate change, and tissue-specific transcripts in octopus. SPLASH is a new unifying approach to genomic analysis that enables an expansive scope of discovery without metadata or references.

DIVE: a reference-free statistical approach to diversity-generating and mobile genetic element discovery

“…A current limitation of DIVE is that it is designed to analyze a single sample at a time. Nevertheless, in certain contexts, it might be of interest to study differential variability across groups, for example [30]. Furthermore, we envision that the assembly process could be better focused by leveraging information from other anchors.…”