  Increasing emphasis on data and quantitative methods in the biomedical
sciences is making biological research more computational. Collecting,
curating, processing, and analysing large genomic and imaging data sets poses
major computational challenges, as does simulating larger and more realistic
models in systems biology. Here we discuss how a relative newcomer among
computer 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 to "gifted amateurs". We highlight how Julia's design is
already enabling new ways of analysing biological data and systems, and we
provide a, necessarily incomplete, list of resources that can facilitate the
transition into the Julian way of computing.
