Peter's Python Programming Pages

Python is a freely available programming language, available on Windows, Linux and MacOS. There is a Beginners' Guide, and I have found searching on Google for specific questions very handy.

Biopython

Biopython is an add-on module which provides support for lots of Bioinformatic work:

Dealing with numerous forms of biological data (including Fasta and Genbank sequence files, and PDB structure files).
Handy functions like translating nucleotide sequences into amino acids.
Calling (standalone) NCBI BLAST to search for sequence matches.
Calling (standalone) ClustalW to do alignments.

I started using Biopython in 2004 for my PhD, and then began contributing to the project. By the end of my PhD I was one of the core developers and was lead author on the Biopython application note publication. Why not have a look at the Biopython Tutorial and Cookbook, or some of my examples.

On a related note, Thomas Mailund's Newick Tree module is very handy for dealing with phylogenetic trees. This file format is used by used by PHYLIP, TREE-PUZZLE, PROTML, and several other programs including Clustal. Biopython 1.30 did not include any code for dealing with tree files - but check out the new Nexus module in BioPython 1.40b onwards from Frank Kauff and Cymon Cox, which is a nice alternative.

The Molecular Modelling Toolkit (MMTK)

The Molecular Modelling Toolkit (MMTK) is a python library providing a range of tools for molecular simulations (the numerically intensive parts are actually written C for speed).

MMTK also has visualisation capabilities, including the ability to use Visual Python or VMD for output. I wrote an example now included in MMTK which loads a PDB file of insulin and displays it using a space filling model.

I did some work with MMTK for my second MOAC MSc mini-project, and wrote a good chunk of the Windows installation instructions for MMTK.

RPy (R from Python)

R is a language and environment for statistical computing and graphics, available for free from The R Project. R's rich libraries for statistics and graph creation can be called from within a Python program using RPy (R from Python), and is used in several of my examples below.