A next-generation dynamic programming language Julia: Its features and applications in biological science - Pal, Soumen; Bhattacharya, Manojit; Dash, Snehasish; Lee, Sang-Soo; Chakraborty, Chiranjib (Journal of advanced research, 2024)
[Display omitted] •Julia is a high-level dynamic programming language that launched in 2012. The language is very appropriate for computational programming as well as mathematical and statistical analysis.•Julia can be applied in biological science because of the packages and their integration into Biology, which is also illustrated.•The review discusses significant features of the programming language, parallel and distributed computing.•Julia’s benchmarking, performance analysis, comparison with other programming languages, future directions, and challenges were also illustrated. The advent of Julia as a sophisticated and dynamic programming language in 2012 represented a significant milestone in computational programming, mathematical analysis, and statistical modeling. Having reached its stable release in version 1.9.0 on May 7, 2023, Julia has developed into a powerful and versatile instrument. Despite its potential and widespread adoption across various scientific and technical domains, there exists a noticeable knowledge gap in comprehending its utilization within biological sciences. This comprehensive review aims to address this particular knowledge gap and offer a thorough examination of Julia's fundamental characteristics and its applications in biology. The review focuses on a research gap in the biological science. The review aims to equip researchers with knowledge and tools to utilize Julia's capabilities in biological science effectively and to demonstrate the gap. It paves the way for innovative solutions and discoveries in this rapidly evolving field. It encompasses an analysis of Julia's characteristics, packages, and performance compared to the other programming languages in this field. The initial part of this review discusses the key features of Julia, such as its dynamic and interactive nature, fast processing speed, ease of expression manipulation, user-friendly syntax, code readability, strong support for multiple dispatch, and advanced type system. It also explores Julia's capabilities in data analysis, visualization, machine learning, and algorithms, making it suitable for scientific applications. The next section emphasizes the importance of using Julia in biological research, highlighting its seamless integration with biological studies for data analysis, and computational biology. It also compares Julia with other programming languages commonly used in biological research through benchmarking and performance analysis. Additionally, it provides insights into future directions and potential challenges in Julia's applications in biology.