Programming
Organisation
Bioinformatics
The project I am working on at present is focused on building an automated system for tracking the movement of honeybees in an observation hive, filmed using a grayscale camera and infrared light. My recent attempt at this has involved extracting features (or keypoints) from the frames and then describing them using the SIFT (Scale Invariant Feature Transform) algorithm, implemented in the OpenCV imaging library. There are a number of other (potentially faster) feature detection algorithms available (SURF, ORB, etc), however as SIFT has been traditionally regarded as the most robust, I thought I would start with it. SIFT works first by extracting what it deems to be robust keypoints/features. For each of these, it will then compute a unique feature descriptor based on the 16×16 pixel area of the feature. By extracting and describing these features, you can try to compare images as shown below.
I commenced my PhD in bioinformatics at the Australian National University about a month ago and thought I’d share some of the tools I’ve found absolutely essential. All of these tools are free to use (some do have paid plans, however at this point I haven’t had the need to sign up for them).
This tutorial is a brief overview of what you can achieve using the Python BioPython module. Although I’m hoping to write up some more articles on this site for beginners when time permits, this post will assume that you have experience programming in Python and have a bit of an understanding of basic biological concepts such as DNA, restriction enzymes etc. If you’re still interested once you finish reading, feel free to consult the BioPython documentation, it will help give you a bit of an idea of how massive (and awesome) this module really is.
So to start I’ll show you how to install the BioPython module. While on Linux systems it can be as simple as typing ‘sudo apt-get install python-biopython’ or going to the Software Center, you can manually install a module by going to PyPI, downloading and extracting the file, opening the command-line or terminal and navigating into the root directory of the folder you just extracted and running the command ‘setup.py install’.
You will need to download two modules to install BioPython, each of which are hosted on their own site. The first is SciPy and the second is BioPython. Once you have installed these you’re ready to get into using BioPython.
Pyral (Python + Viral) was the name of a project I worked on in Dr Joanne Macdonald’s lab between September 2012 – January 2013 (although I am still providing tech support for the code and helping manage the server to this date). Throughout this time I wrote a lot of Perl and Python code to run on the university’s Linux server. The aim of these programs were as follows: