Julia for Biologists

TL;DR

Julia is a promising programming language that enhances computational biology by offering high performance, flexibility, and readability, thus enabling biologists to handle large data sets and complex models more effectively.

Contribution

The paper discusses how Julia's design and ecosystem are uniquely suited to meet current and future computational challenges in biomedical research.

Findings

Julia enables faster data analysis and modeling in biology.

Julia's ecosystem supports a wide range of biological data analysis tools.

The language's readability lowers barriers for biologists adopting computational methods.

Abstract

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.