Faster the Code, better the Analysis: Julia

Biology has become interdisciplinary. Incorporation of other disciplines such as physics, chemistry, mathematics and so on have been utilized for long to decipher fundamental principles. Over time biological experiments have evolved to a very complex world, with ample data throwing intriguing questions. Computational tools have paramount role in contributing to such biological puzzles allowing us to develop, simulate and test mathematical models. From fundamental genetic analysis such as QTL (Quantitative Trait Loci) to structural biology components such as cryo-EM or X-ray crystallography, computers have wore many hats.

Programming languages are also tools that instruct computers to get some tasks done. Different languages are specific to different tasks such as Perl to deal with string processing or R for statistical analysis but that are not stringent to its applicability to any future task. It has been observed that computationally intensive studies are designed and prototyped in R, Python or MATLAB followed by its translation into C/C++ or Fortran. Certainly, this two-language problem enhances performance but yields certain limitations also. Julia, the programming language, not your classmate, can overcome this two-language problem which is a good choice for biologists. Speed, metaprogramming, and abstractions are essential qualities that Julia comprise. Here I will discuss the relevance of such qualities that Julia has.

People working on precision medicine know that the usefulness of digital twins is…