Programming in biology

Modern experiments produce huge amounts of data, hard to store, move, analyse and understand. Artificial intelligence and machine learning are changing research too. So biotechnology students increasingly need some programming and statistics, to retrieve, analyse, visualise and model data themselves.

Most successful bioinformatics software was developed on, and runs on, the Linux operating system. The language Perl has long been at the heart of handling sequence data.

Popular languages and platforms:

• Python, created by Guido van Rossum in 1991: a high-level, object-oriented language that runs on Unix, Mac and Windows. Biologists like its clear, readable style and its many ready-made libraries. It is used for analysing sequences and structures and for building evolutionary trees (phylogenetics).
• R, named after the first letters of its creators' names: free and open-source. Its many statistical packages make it ideal for large-scale analysis, for drawing graphs, and for simulating genomes and pathways.
• MATLAB: another powerful platform for calculation and modelling.

Some newer languages are designed to describe living systems themselves: GEC (Genetic Engineering of living Cells, from Microsoft), and Kera, developed at the University of Kerala, which stores information about genomes, proteins and cells in a library called Samhita.