TY - JOUR
T1 - Julia for biologists
AU - Roesch, Elisabeth
AU - Greener, Joe G.
AU - MacLean, Adam L.
AU - Nassar, Huda
AU - Rackauckas, Christopher
AU - Holy, Timothy E.
AU - Stumpf, Michael P.H.
N1 - Funding Information:
We thank all attendees of the Birds of a Feather session Julia for Biologists at JuliaCon2021; D. F. Gleich for allowing us to run an experiment on his servers; and R. Patro for discussions about Rust. E.R. acknowledges financial support through a University of Melbourne PhD scholarship. A.L.M. acknowledges support from the National Science Foundation (DMS 2045327). T.E.H. acknowledges NIH 1UF1NS108176. The information, data and work presented herein was funded in part by the Advanced Research Projects Agency—Energy under award numbers DE-AR0001222 and DE-AR0001211, as well as National Science Foundation award number IIP-1938400. The views and opinions of the authors expressed herein do not necessarily state or reflect those of the US Government or any agency thereof. M.P.H.S. acknowledges funding from the University of Melbourne Driving Research Momentum initiative and Volkswagen Foundation Life? program grant (grant number 93063), as well as support through an Australian Research Council Laureate Fellowship.
Publisher Copyright:
© 2023, Springer Nature America, Inc.
PY - 2023/5
Y1 - 2023/5
N2 - Major computational challenges exist in relation to the collection, curation, processing and analysis of large genomic and imaging datasets, as well as the simulation of larger and more realistic models in systems biology. Here we discuss how a relative newcomer among programming languages—Julia—is poised to meet the current and emerging demands in the computational biosciences and beyond. Speed, flexibility, a thriving package ecosystem and readability are major factors that make high-performance computing and data analysis available to an unprecedented degree. We highlight how Julia’s design is already enabling new ways of analyzing biological data and systems, and we provide a list of resources that can facilitate the transition into Julian computing.
AB - Major computational challenges exist in relation to the collection, curation, processing and analysis of large genomic and imaging datasets, as well as the simulation of larger and more realistic models in systems biology. Here we discuss how a relative newcomer among programming languages—Julia—is poised to meet the current and emerging demands in the computational biosciences and beyond. Speed, flexibility, a thriving package ecosystem and readability are major factors that make high-performance computing and data analysis available to an unprecedented degree. We highlight how Julia’s design is already enabling new ways of analyzing biological data and systems, and we provide a list of resources that can facilitate the transition into Julian computing.
UR - http://www.scopus.com/inward/record.url?scp=85151918702&partnerID=8YFLogxK
U2 - 10.1038/s41592-023-01832-z
DO - 10.1038/s41592-023-01832-z
M3 - Article
C2 - 37024649
AN - SCOPUS:85151918702
SN - 1548-7091
VL - 20
SP - 655
EP - 664
JO - Nature Methods
JF - Nature Methods
IS - 5
ER -