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Megascale microbiome analysis with DartUniFrac
Jianshu Zhao1, Daniel McDonald1, Igor Sfiligoi2
1Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
Abstract:
UniFrac measures phylogeny-aware differences between microbiome samples but scales poorly with modern dataset sizes. We introduce an algorithm, DartUniFrac, and a near-optimal implementation with graphics processing unit acceleration that is up to three orders of magnitude faster than UniFrac and scales to millions of samples (pairwise) and billions of taxa. DartUniFrac connects UniFrac with weighted Jaccard similarity and exploits sketching algorithms for fast computation.
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