What programming language is best for a bioinformatics beginner?

probably Unix Shell scripts, Perl, or Python and R can be the best options.

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1-python

2-R

3-perl

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I would say, Python AND R. Although, just python would be sufficient already, it has great capabilities (including bioinformatics libraries, as indicated above - biopython).

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I would say go see your research group's grad students and use the scripting language they use (either Perl or Python) and learn R.

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R is better because of bioconductor powerful libraries

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Start with Bioperl/Python and then go to R‘s Bioconductor.

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Attending to a Python crashcourse and R workshop helped me alot in the beginning.

Pythons is fairly easy to code even for starters and has a huge userbase.

R will be probably inevitable, a plethora of packages are in Bioconductor

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R, Python, and bash

In summary, for wet-lab people who want to add bioinformatics to their toolbox, focus on learning R first and applying it to your own work. For people who want to focus on bioinformatics as a career and make their own tools too, I would actually recommend learning the trifecta of R, Python, and Bash, though you could get away with choosing between R and Python as long as you still learn Bash too. I can go into more depth on any of these topics or give an introduction to any of these languages if you let me know in the comments.

Other programming languages

There are many other languages out there, so before I end here I’m going to give a brief reason why these are not recommended for bioinformatics, beginners, or anyone at all in some cases.

C and C++

C or C++ are great for making super optimized command-line tools like aligners and variant-callers, but you will have a much easier time learning Python first and then going to these high-performance languages for a particular problem in the future, since they are harder to learn, more finicky, and take a lot more code to do the same thing.

Perl

Perl is still what a lot of people use, but it is fading out of use because Python accomplishes the same tasks and is easier to write code for, especially for beginners.

Ruby

Ruby is one of those hot languages right now, for good reason largely because of the power of Ruby on Rails for making database-driven web applications like blogs or twitter. Ruby however is not great for bioinformatics because it lacks the community support in terms of packages that R and Python have, so you would be better off learning Python instead of Ruby.

JavaScript or PHP

JavaScript and PHP are great languages for web applications, but bioinformatics web applications should never be your first project. You could make a computational method in Python or R and then later make it into a web application, but that is not a project for a beginner. HTML and CSS by the way are not programming languages, but actually markup and styling languages that you will use along with JavaScript and PHP for that web application someday.

Java

Java is a popular language that most people have heard of. In bioinformatics, a notable example is the genome browser IGV. However, I would not recommend for beginners to learn Java due to many issues including memory management and that Python and R have many more bioinformaticians who build packages and answer questions online.

That’s all I have to say about bioinformatics programming languages for now. If you want to see more videos like this about bioinformatics, then make sure to subscribe on YouTube and sign up for updates below to get new videos, guides, and scripts about bioinformatics delivered to your email inbox every week.

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Be this as it may – for me, this test showed that python and R are still a pragmatic choice of programming language for everyday Bioinformatics tasks

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For biological background graduates, Python and R should be the best languages to learn.

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No, nothing of sorts could ever happen.

1. R has more advanced statistical functionality than Python will ever have - the packages that you list implement a tiny subset of what already exists in R
2. R has better visualization capabilities than Python will ever have
3. R has a better cross platform compatibility than Python will ever have
4. R has better automated package installation than Python has (and likely will ever have)
5. The userbase for R as a statistics language is gigantic compared to the number of users that use Python for data analysis

The downside of R is that it is both eclectic and byzantine.

Python is a generic programming language and it is great at that. But it is not a data analysis platform nor are the lead developers focusing on addressing the issues above.

And I am saying this as someone that uses Python almost exclusively for data analysis and most of my work.

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Python (with NumPy, SciPy, and StatPy) already has a big share among data analysis software. You can almost find equivalent functionality to Matlab/Octave and R, however those Python tools are still a little bit in their infancy. I mean, R and Matlab/Octave exist for many decades and have been originally geared to those data analysis functions...and of course they developed over the years to become even better. Python's data analysis capabilities are quite new, and it might take a while until they are on the same level, or become even better.

But I am very optimistic that Python will evolve to be one of the best data analysis packages one day. The Python community is very enthusiastic, creative, and productive, and in my opinion it is just a matter of time. However, I think R and Matlab/Octave will never cease to exist. They will find their niche, just like Fortran & Co.

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If you implement a new bioinformatics/biostatistics algorithm I think Python gives much more flexibility in programming. It is easier to implement those algorithms in Python since it is a general purpose language and it has a nice syntax, lots of useful language construction. R is pretty bound to table data manipulations but the base of statistics algorithms in it is really impressive.

So people often use combination of R/Python (like here http://cistrome.org/Cistrome/Cistrome_Project.html). When they use Python for algorithm implementations, input/ouput manipulations and R for plotting, running statistics or for Bioconudctor packages.

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I believe Python will take over. It won't be easy as there is a lot convincing to do and algorithms to port. The no so secret weapons Python has are Pandas and the IPython Notebook.

Take a look at this video introduction and see if you agree with me.

10-minute tour of pandas

Feel free to follow on using the available notebook viewer.

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Moreover, there are instance database that configure flawlessly with it, for instance, Adventure Works database. That is all I have toward say around bioinformatics languages for the time being.

Read more at: https://www.freelancinggig.com/blog/2017/07/19/best-programming-languages-bioinformatics/

REF

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