D
Daniel McDonald
Researcher at University of California, San Diego
Publications - 157
Citations - 84726
Daniel McDonald is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Microbiome & Biology. The author has an hindex of 46, co-authored 128 publications receiving 64433 citations. Previous affiliations of Daniel McDonald include University of Colorado Boulder & University of California, Berkeley.
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Proceedings ArticleDOI
Porting and optimizing UniFrac for GPUs.
TL;DR: In this paper, the authors describe steps undertaken in porting and optimizing Striped UniFrac to GPUs, which reduced the run time from over one month on the CPU to less than 2 hours on the V100 and 9 hours on an NVIDIA RTX 2080TI GPU.
Journal ArticleDOI
Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype
Cameron Martino,Daniel McDonald,Kalen Cantrell,A. H. Dilmore,Yoshiki Vázquez-Baeza,Liat Shenhav,Justin P. Shaffer,Gibraan Rahman,George Armstrong,Celeste Allaband,Se Jin Song,Rob Knight +11 more
TL;DR: Two new methods to calculate differences that combine features of past methods are introduced, specifically being able to take into account the principles that most types of microbes are not in most samples, that abundances are relative rather than absolute (compositionality), and that all microbes have a shared evolutionary history (phylogeny).
Proceedings ArticleDOI
Porting and optimizing UniFrac for GPUs: Reducing microbiome analysis runtimes by orders of magnitude
TL;DR: Computing UniFrac on a larger dataset containing 113k samples reduced the run time from over one month on the CPU to less than 2 hours on the V100 and 9 hours on an NVIDIA RTX 2080TI GPU (with minor loss in precision).
Posted ContentDOI
Revisiting microbe-metabolite interactions: doing better than random
James T. Morton,Daniel McDonald,Alexander A. Aksenov,Alexander A. Aksenov,Louis-Félix Nothias,Louis-Félix Nothias,James R. Foulds,Robert A. Quinn,Michelle H. Badri,Tami L. Swenson,Marc W. Van Goethem,Trent R. Northen,Trent R. Northen,Yoshiki Vázquez-Baeza,Yoshiki Vázquez-Baeza,Mingxun Wang,Mingxun Wang,Nicholas A. Bokulich,Aaron Watters,Se Jin Song,Richard Bonneau,Pieter C. Dorrestein,Pieter C. Dorrestein,Rob Knight +23 more
TL;DR: It is shown that the proposed correlation and proportionality are outperformed by MMvec on real data due to their inability to deal with sparsity commonly observed in microbiome and metabolome datasets.