Question: What are the most common bioinformatics programs?

Ian Simpson answered on 17 Jun 2014:

Looks like there have been some good answers on here already. Like all science the method you use depends on the question you are asking.

There are a core set of skills that people who work in the field of Bioinformatics would normally have or look to acquire quite quickly. These are generally aligned with the common tasks. This is a generalisation, but broadly :-
1.) acquiring data
2.) processing/quality control
3.) data storage
4.) data analysis
5.) presentation/visualisation

People would normally be able to write scripts in one of several common languages that Bioinformaticians use. The two most common are Perl and Python. These can be used in all of the above steps thanks to large software libraries that have been developed (and are mostly open source, so free) by scientists around the world (BioPerl, CPAN and BioPython are examples). Increasingly a lot of the quality control and analysis is being carried out in a statistical programming language called ‘R’ which is also open source and also has a lot of software libraries held in two repositories; CRAN and Bioconductor.

As Sergey alluded to, there are many ‘command line’ programs. These are nearly always specialist tools that have been developed to carry out very specific types of data analysis and are often written in C, C++ or Java and are commonly faster and more powerful than graphical tools (though that need not always be true). I would say that the majority of Bioinformaticians will script and use command line tools and the reason for that is that most experiments are special or different in some way and require a bespoke solution. A lot of these tools can be executed in different ways and these change the nature of the results produced, so often a good degree of experimentation is required to get the best analysis from your data. It’s an important point to make that Bioinformatics is an experimental science ! I carry out lots of experiments every week without ever entering a wet lab 😯

As Vicky very nicely described we have to store a lot of information and would like to be able to query that and compare it with other similar data. Most data in Biology is stored in MySQL and Postgres relational database systems. These are the most common DB systems in the world and are used by pretty much every website you’re ever likely to visit (including this one).

If your question was motivated by what you might want to learn to get a feel for some of these software tools. I would throw you right in at the deep end and suggest you start playing with ‘python’ and ‘R’ and their associated software packages. In the last few years these two have strongly emerged as the workhorses of a lot of Bioinformatics projects and there are some great guides out there to get you started :-

Quick R
http://www.statmethods.net/
http://www.bioconductor.org/

Learn Python
http://www.learnpython.org/
http://biopython.org/wiki/Main_Page

Finally in visualisation there are lots of widely used software packages to make diagrams and visually-interactive tools. Many of these are accessible from within Python, Perl and R so you don’t have to leave the ‘R’ analysis environment to make nice visualisations. Some popular examples are iGraph (for drawing networks), circos (for genome-scale visualisation), iTOL (visualising evolutionary trees). Many are also scripted as web applications using things like D3-Ajax-JSON http://bl.ocks.org/mbostock/4062045 (please click the link, it’s very cool !).

So in summary you might have expected a list of programs that are “most used”……BLAST, MUSCLE, Clustal, PHYLIP etc.. but in reality you need to select the ones best suited to what you’re trying to do and you do that by reading the literature, talking to other Bioinformaticians and above all experimenting. A great foundation is to start learning a scripting language (I’ve suggested Python) and an analytical language (I’ve suggested ‘R’).