A very classic question in bioinformatics is, which programming language is the best for a bioinformatician? Discussions like this never end with a conclusive answer. Interestingly, people find this question as a piece of cake and jump at it with whatever they have in their hands! The result is, you get a nice rainbow of choices, right from "C" to "PHP"!
Each programming language has its own perks and disadvantages. For example, "C" has an incredible speed in execution but it is equally code-intensive in writing even a simple program. Python and Perl on other hand make the same program code-lite but with a mediocre speed of execution. Apart from these performance issues, every language is blessed with a varying degree of third party modules/libraries.
Python has provided interfaces to many system calls and libraries, giving direct access to the shell of an operating system (modules like os, subprocess let you call unix commands directly from the python terminal). Python is also usable as an extension language for applications written in other languages that need easy-to-use scripting or automation interfaces. More than 15 coding projects have started to establish a platform where python can be integrated with other programming languages like C, Java, Perl, PHP, R, Fortran etc.
These hybrid platforms are either available as python modules which can easily be imported, like we do for general (numpy, maths, random etc) modules or accessible from a parent language (i.e. Jython, python implemented in Java)
A detailed list of these hybrid platforms are accessible from here.
Some fascinating platforms I couldn't resist to mention here are:
Each programming language has its own perks and disadvantages. For example, "C" has an incredible speed in execution but it is equally code-intensive in writing even a simple program. Python and Perl on other hand make the same program code-lite but with a mediocre speed of execution. Apart from these performance issues, every language is blessed with a varying degree of third party modules/libraries.
Python has provided interfaces to many system calls and libraries, giving direct access to the shell of an operating system (modules like os, subprocess let you call unix commands directly from the python terminal). Python is also usable as an extension language for applications written in other languages that need easy-to-use scripting or automation interfaces. More than 15 coding projects have started to establish a platform where python can be integrated with other programming languages like C, Java, Perl, PHP, R, Fortran etc.
These hybrid platforms are either available as python modules which can easily be imported, like we do for general (numpy, maths, random etc) modules or accessible from a parent language (i.e. Jython, python implemented in Java)
A detailed list of these hybrid platforms are accessible from here.
Some fascinating platforms I couldn't resist to mention here are:
It is interesting to note that every platform mentioned here was somebody's dream. Since shifting to a new language might deliver new exciting features but at the same time it takes away what you loved the most about the previous one. Following are the words from the creator of PyPerlish,
"I've used perl for several years, and been very impressed with its ease of use. When you need to do something new, chances are there is an idiom which lets you do it in a few keystrokes. I didn't want to lose that in moving to python. Somehow I wanted to get the benefits of perl's idioms with the robust scalability and maintainability of python. So the idea is to emulate perl idioms, no matter how we implement the python code under the covers." -- Harry George
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