I will be doing NGS in the course of my research work and I will like to learn a programming language which is compatible with most bioinformatics tools or software. I basically want to do de-novo assembly, map reads, align reads, and expression analysis. Recommendations welcomed.
Hello,
I agree with most of the previous conversations, probably Unix Shell scripts, Perl, or Python and R can be the best options.
+) Perl:
Advantages: Flexible, with a global repository (CPAN), so it is trivial install new modules. It has Bioperl (http://www.bioperl.org/wiki/Main_Page), one of the first biological module repositories that increase the usability from, for example, change formats (Bio::SeqIO) to do phylogenetic analysis. There are some biological software that uses Perl such as GBrowse (http://gmod.org/wiki/GBrowse) so may be an interesting language if you need to interact with it. Good test modules (Test::More).
Disadvantages: Sometimes it is not a clear language. Probably there is as many ways to use Perl as programers are, so it can be very simple (just scripting like the manual) or very complex (object programming using Moose). Some Bioperl modules not always works.
+) Python:
Advantages: Clear language. Usually there is only one way to program with Python. "The right one". It is simple and stable. Biopython doesn't have as many modules as Bioperl, but they work (http://biopython.org/wiki/Main_Page).
Disadvantages: It doesn't have a central repository, so sometimes you need to visit two or three webpages before you can install a module.
+) R:
Advantages: It is an statistical programming language, so it opens an universe of analysis, from t-test to PCA and clustering. If you are going to do RNAseq analysis may be essential if you don't want to use paid software because 75% of RNAseq statistical packages are from Bioconductor (biological software repository for R). For example, CummeRbund is a R package to analyze the results from Cufflinks (a program to calculate expresion for RNaseq experiments). It has central repository (CRAN) so install packages is easy. Graphs, graphs are simply great with R. It has R-studio (http://www.rstudio.com/) that is an environment software to use R in a Matlab fashion.
Disadvantages: The indentation system during the writting.
So, summarizing... if you are going to do RNAseq,my recommendations would be 70% R (statistical analysis, graphs), 20% Perl/Python (change formats, do simple scripts to do some stats) and 10% Unix bash scripting (to create simple pipelines with RNAseq programs).
One last thing, we have given some bioinformatic classes (a little bit about Linux, R, Perl and SQL (databases)). So, you can access to the presentations and classes (some are better, some are worse) at: http://btiplantbioinfocourse.wordpress.com/core-program/
Good luck,
The ones that I think tend to be used the most are R(& BioConductor), PERL and Python.
Python is a more up to date and better structured language. However, Perl is still the standard in the field and you will likely need to know Perl if you are doing this in collaboration with other bioinformatics groups or you want to take advantage of most of the code already written by others.
R is specifically for statistical data analysis.
The other one you will find useful is UNIX shell.
It depends on what type of NGS study you are doing. If you are doing RNAseq, R is essential and the others not terribly useful, especially if you know some UNIX already. However if you are doing assembly and annotation, UNIX is essential, Perl useful and R not so much.
My research is focused on the same topics and I use Perl in my everyday work. For me it is the best for string processing and the most powerful with regular expressions. In the end, DNA and RNA molecules can be seen as just strings of 4 letters. Perl is the classic programming language in Bioinformatics but it is true that Python is gaining more adepts every day and it is probably a bit easier to learn. (I still find AWK useful today but most people would say it is old-fashined and for sure it is not as powerful as Perl or Python.)
I agree that you will need some knowledge about UNIX (Shell environment in particular) together with the programming language you finally choose.
R will be essential to analyze your data.
The good news are that once you spend the time to learn one programming language learning a new one is much easier.
Hey!
I use extensively Python for all my analysis. It is really easy to learn, with a nice and clean syntax and has libraries that cover almost everything that you need.
These are the libraries that I use everyday for my analysis:
- Numpy and Scipy: Matrices, Algebra, Statistics and Combinatorial
- Bio-python: really useful library for bioinformatic analysis
- Scikit-learn: All you can dream and want from Data Mining and Machine Learning fields in a simple library (SVM, regression tree, random forest, clustering, dimensionality reduction, feature selection)
- GHMM: Hidden Markov Models (really fast)
- Pylab: awesome plotting library
- Pysam: wrapper for samtools
- Pybedtools: wrapper for bedtools
Good luck with your research!
p.s.: R is also a nice alternative :)
It really depends upon the users requirement and interest, to chose the the languages. And the user must that language, in which he feel comfortable, if he is going to write a script for the first time.
There is some similarity in writing the scripts of various languages.
I think.. we should get good knowledge of a particular language.. on that basis... we can learn other languages of our interest...
:)
Thank u Luca and everyone else, for giving us clear description of some software..
Hello,
I agree with most of the previous conversations, probably Unix Shell scripts, Perl, or Python and R can be the best options.
+) Perl:
Advantages: Flexible, with a global repository (CPAN), so it is trivial install new modules. It has Bioperl (http://www.bioperl.org/wiki/Main_Page), one of the first biological module repositories that increase the usability from, for example, change formats (Bio::SeqIO) to do phylogenetic analysis. There are some biological software that uses Perl such as GBrowse (http://gmod.org/wiki/GBrowse) so may be an interesting language if you need to interact with it. Good test modules (Test::More).
Disadvantages: Sometimes it is not a clear language. Probably there is as many ways to use Perl as programers are, so it can be very simple (just scripting like the manual) or very complex (object programming using Moose). Some Bioperl modules not always works.
+) Python:
Advantages: Clear language. Usually there is only one way to program with Python. "The right one". It is simple and stable. Biopython doesn't have as many modules as Bioperl, but they work (http://biopython.org/wiki/Main_Page).
Disadvantages: It doesn't have a central repository, so sometimes you need to visit two or three webpages before you can install a module.
+) R:
Advantages: It is an statistical programming language, so it opens an universe of analysis, from t-test to PCA and clustering. If you are going to do RNAseq analysis may be essential if you don't want to use paid software because 75% of RNAseq statistical packages are from Bioconductor (biological software repository for R). For example, CummeRbund is a R package to analyze the results from Cufflinks (a program to calculate expresion for RNaseq experiments). It has central repository (CRAN) so install packages is easy. Graphs, graphs are simply great with R. It has R-studio (http://www.rstudio.com/) that is an environment software to use R in a Matlab fashion.
Disadvantages: The indentation system during the writting.
So, summarizing... if you are going to do RNAseq,my recommendations would be 70% R (statistical analysis, graphs), 20% Perl/Python (change formats, do simple scripts to do some stats) and 10% Unix bash scripting (to create simple pipelines with RNAseq programs).
One last thing, we have given some bioinformatic classes (a little bit about Linux, R, Perl and SQL (databases)). So, you can access to the presentations and classes (some are better, some are worse) at: http://btiplantbioinfocourse.wordpress.com/core-program/
Good luck,
Python is great for starts. The syntax is clean and simple. Further, as you become more advanced in your programming skills, you can interface with more advanced libraries such as scipy, numpy, and matplotlib as well as with other programs such as R and various databases. Further, parallelization is relatively simple with built in modules (e.g., multiprocessing) and speed optimization can be had by integrating C or Fortran code through shared libraries or through interfaces such as cython. It's a very versitile language.
I think python is simple and easy to work with. and It's suggested that R is a great statistical programming language. by the way you can use available toolboxes in MATLAB.
I think it is MATLAB, beside there is a good bioinformatics toolbox
As it has been said, Python or Perl are a must as they come handy to process text data and there is a lot in bioinformatics. I use PERL the most, but I'm looking to learn more Python as I'm tired of the contextual behaviours of variables in PERL (disadvantage_. Regular expressions are really strong (advantage).
R is also a must as data scales up, the need to properly analyze them scales up too. R is also a really powerful graphical tool when properly handled and scripting your data into graphical output is really easy going.
Unix shell scripting is still useful, but, in my honest opinion, I'd rather use PERL, even for shell automation tasks (This is why I chose Perl over Python as a scripting language, but both could do fine).
On my side both Python and Perl have plenty of bio-packages such as Bioperl or Biopython.
On the other hand I am convinced that R and Bioconductor integrate textmining, statistics, graphs and several bioinformatics packages are now available.
If you have to start from scratch my suggestion is R.
The arguments go since as long as I remember and beyond. Most likely since the day more than one language became available and a lazy student first asked why should one learn two if only one can be used at a time and one is enough to land on a job. One is still enough if all you want to do is one monotonous mundane job with a strictly limited area of application. Needless to say, an unlikely career in Bioinformatics. The only way to stay on top of this fast and sharp-turning game is to learn the ART AND CRAFT OF PROGRAMMING rather than specific languages. More general languages (and never just one language) are better for didactic purposes. Specialized languages learned before general ones tend to leave young skewed, limping and lisping for years, if not permanently specialized. In my humble experience Python-bruised students struggle with memory management and generally resource-limited and high-performance programming. Perl students have limited understanding of discipline and structure, be it object-oriented or just simple housekeeping; the sheer number of shortcuts offered by the language is so overwhelming for young minds that scripts quickly become unreadable, then non-modifiable, non-portable and crush under own complexity beyond couple hundreds lines. Students of R and Matlab tend to see any problem as a statistical test or a differential equation, helpless outside the shiny toolbox.
The point I want to make is not that those languages are bad or the new generation of Bioinformaticists any worse than the previous. Rather opposite. I would recommend not to jump to "the best" language straight. The real best might be something least practical to feature in your resume. Learn Pascal - the best introductory language. Step up to C and C++, then make excursions to Java, Python and in the opposite directions of Fortran and Assembler. Finally, learn more advanced application of those popular Python-Perl-R languages everybody seems happy to advice, maybe some other depending on the job requirements and USE THEM ALL TO YOUR ADVANTAGE.
I must admit that Andrey's answer is the best I've seen. The more you know, the more tools you have. Starting with fundamental languages won't kill anybody. I would struggle with memory management in most languages of I had not learned C at first.
Bioperl programming is the best and easy language for Bioinformatics
hi all, I am a biologist on origin, and I hadn't time to study all languages, Finally my experience was to enter bioinformatics world from a door (in my case was perl) and improving day by day possible making the language working for my small biological questions... from there running to R was a big big effort. But now both work for me. For biologists who principal work is some time wet-lab work we do not have all the time to pass trough many languages and finding one that fit our needs could be enough to solve the problems we have.
You can start with the basic UNIX scripting, will help you on execute and running bioinformatic software. Then you can try on Perl and Python. R will help in Statistical work. If you plan to be programmer C/C++ is the best.
Current trends in Bioinformatic is MultiCore, CUDA code also the best choice.
I agree more with Andrey with the learning curve to programming. What he just suggested might take a few months to years depending on the learner's zeal. For someone who is doing a research work that has much time to study, that is the best for him/her. For someone who wants to deliver fast and still learn coding a recommendation will be to take a class on programming. I will recommended one of the free online classes from University of Stanford, on coursera.org.
Programming generally is like understanding French, English or German etc. If you know one, you can know others if bother to learn them. How fast you will learn depends on you and the tools available to you and most importantly trying it yourself.
I will recommend you start with Python because it is easy to learn (etc as mentioned by others above) and in addition you can finish learning and start right to apply it to Bioinformatics. I will not recommend you start to apply it to Bioinformatics application at the beginning to ease your learning. I have seen good codes/libraries written in Python and used for a Robot operating system (ROS) application despite that it is called a "simple" language. The most important part of programming is the logic of your implementation. If you can get it to work in any language, more experienced programmer can tune up your application for memory, portability and other issues or even convert to better/preferred languages like C.
You can only be limited by your imagination when it comes to programming. Start anywhere and you can get somewhere. Good luck with your work.
I would simply say.
A language like C will bring in the conceptual understanding of programming. If one needs to go into complex programming Java can replace it. But Still I would recommend C.
The Next would be PERL programming language which can greatly simplify understanding programming for Bioinformatics specifically.
The above said two languages will form the base for learning the subsequent requirements in advanced programming.
I'm very happy with Python. It's easy, yet powerful and fast. Plus, plenty bioinfo packages is natively supported.
From the programmer perspective, R is a crappy language. It is a useful tool, however, though I think you can get better results with other packages. Learning to use R is a steep climb no matter your previous experience. Perl or Python or Ruby, you are probably going to need to know some Perl; don't write any more of it; use Python or Ruby instead. Ruby has some very nice features not found in other languages. C/C++ will be useful; both produce faster code than that produced with Perl or Python; probably true for Ruby as well. CUDA is the wave of the future, and it is at the door just now.
Mathematica is actually a very powerful language. It is fairly straightforward and provides everything from symbolic calculations to graphics to numerical data analysis. Many universities and labs have site licenses. You should also be able to download a test version from www.wolfram.com. The link to Mathematica contains many examples and demonstrations. I have been using it for about twenty years, I use it daily for almost any mathematical and organizational problem. Most of my papers rely on Mathematica-based calculations and graphics.
I have always been impressed with the speed of Perl for cranking thru large data sets.
I would suggest Perl as the programming language. Moreover you can also look into Matlab (as it has Bioinformatic tools attached) and also R which may require no much deep programming as Perl. U just need to know the functions which you have to use.
The choice of programming environment should depends on the level and kind of work. When you are majorly interested in analysis kind of work i would suggest go for MATLAB. Because in such an environment you would only need to use some functions and little programming and will end up with best interactive results. This may manage you time and work in a much effective way.
I have just started learning C language, since it is the basic for all the programming language. Can I learn PERL, BIOPERL or PYTHON without knowing C language?????... Please suggest.....
I don't think learning C language is a requirement for learning Perl, Python etc. For a beginner Perl wont be difficult at all. Same applies to Python also. If you have used Matlab or R, then no probs at all. All the best for learning.
R, Matlab if you can get license.
Python would be another choice, than Java, C/C++
Personally I have found R studio to be very usefull, because it is free, there are already a lot of functions available and, most importantly for a beginner (as I am also), google searches for "how to do something in R" always give extremely helpful tips! Having no background in informatics, I have found that for other programs the "explaining language used by people" is a bit hermetic, while for R is much more understandable...
Also in R, you can make reports of all your analysis, including code, text and images (sweave reports), which are extremely helpful to share with your collaborators.
Learning languages like PERL, BIOPERL or PYTHON doesn't need a prior knowledge of C or any other programming language. The only advantage with a prior hand on any programming environment is that it adds up a helping hand in concept development.
Use BioPerl. BioPerl is a collection of Perl modules that facilitate the development of Perl scripts for bioinformatics applications. Find more here: www.bioperl.org/
Perl and Python would be the best and easiest to learn for beginners, then go onto the other ones.
I took a look at Python to see how it would compare to Perl to handle a 20mb file. It took a long time to just open with Python whereas with Perl the opening and line by line processing (which included 6 iterations thru the file) took about a minute. I used pyscripter and also tried IDLE with Python. Perl wins out for me.
BTW - I use Padre as an IDE and it uses Strawberry Perl. R for the calculations I was doing would take over an hour.
-For basic scripting, bash or python or pearl is enough.
- For manipulating biological data, python (+biopython library) is probably the easiest to use.
- For computing statistics, R is the best, but python with scipy library can also handle basic statistics.
- For implementing an algorithms (for large data or with large time complexity), C++ is a good option (it is better than C because many C++ classes / library for handling biological data are availlable, and it is much easier to use thanks to the std and the boost libraries)
The answer of Aureliano Bombarely is quite appreciating. I want to add one more language "Java" with lots of good capabilities like a variety of large-size data types, multithreading, network-based programming, good support for string & character handling, interactive GUI design support, etc. Today we have BioJava package (http://biojava.org/) which gives support of biological data processing. Most of the bioinformatics tools today has been developed using Java language.
Do you mean, 'best programming language for a programming beginner'? If you need to learn a new programming language to do bioinformatics, then a scripting language such as Perl, Python or Ruby is probably the best start. Good summaries of what these languages offer have been given in this thread already. I gave Python classes for life scientists for the past four years, and people usually learn very fast how to write programs. Perl is as powerful, but the syntax is not as friendly. C and Java are compiled language, which means you need to learn more than the syntax to actually have a working program.
If you know how to program, and are a bioinformatics beginner, then whatever tool will be good; you probably know how to glue technology stacks together already. In Python for example I routinely call R and Java functions whenever I need a specialized algorithm that have a good implementation in these languages. Today Python and Ruby are the trendier languages in the field, with Perl being more of a legacy language.
It really depends on what is what you want to do. For example if you want to perform a task that involves heavy statistical analysis then probably R would be the best option. If you want to perform some work with serious support in terms of sequence manipulation then Perl should probably be your choice. However if the goal is to do some work done while learning how to program then it would be better to use some other language with easier syntax. Personaly I use now Ruby after many years of Perl because I love the simplicity and how clean the code looks- much easier to understand and maintain. Python seems to be an alternative in term of simplicity althouh never went further than using it for a few examples. Both have some bioinformatics libraries that can solve many although not all the problems (at least for bioruby). Hope this helps.
I'm also suggest Perl language. Because it is very nice language for pattern matching and regular expression. many light weight objects are available for processing the large amount of text as well as strings.
Basically, R is used for statistical analysis in large dataset. So perl is the best for pattern matching , regular expression and file operations with light weight objects.
Step - By -Step Guide
1) Learn Unix
2) Learn basic of C
3) Learn Python
4) Learn R
5) mysql
You can solve anything and everything with these steps.
Nidhan's answer is surprisingly to the point. Indeed, those are the elements most data analysis pipelines are based on.
Some people will ask about the place of Perl, or of their favorite RDBMS, but I think these five technologies are the basics of any modern data analysis pipeline, on top of what everything else is built (or can be derived).
I agree with Nidhan's answer. This road will probably work for someone who wants to do bioinformatics analysis.
If you need to develop tools that will be used by others some C++ or Java woul need to be added
I agree with most of what people have raised previously. I just want to add my opinion on R programming. Having programmed a lot in Perl and some in Python, I'd say that R scripting is horrible as a programming language for starters. It is full of inconsistencies and typing is a nightmare.
Despite coming from a Perl environment, I'd recommend Python which is much easier to learn and understand (and produces much more readable code. However, one should bear in mind that it is easy to get Python-addicted and therefore never learn Perl, and there's still LOTS of bioinformatics software around based on Perl.
Learning C would of course be good, but I'd say it is overly complicated for most tasks. Then again, at some point you'll find that Perl's (and Python's) memory handling sucks and you need to turn to more low-level languages (i.e. C).
All in all, my recommendation for a complete beginner would be Python. And to stay away from R programming – R is great but not as a programming language for starters!
C / C++ are the good programming language to build softwares in bioinformatics. The famous package EMBOSS built with this programming language only. Even DOCK software also built with C programming language.
If you require programming skills for general scripting, which is so often required when dealing with large NGS datasets, I'd recommend Python. It is relatively easy to learn and you won't be bogged down with learning overly-complicated syntax. Note that learning to work in a Linux environment (i.e., using the command line) is of paramount importance. This operating system contains many built-in features that are useful for manipulating large NGS datasets (e.g., SED, grep, AWK, etc.), and many bioinformatics tools require a Unix/Linux OS to run.
John Wiedenhoeft: I wouldn't discard Java, actually. Far from it.
It is true that Java is not the language (most) people think about when developing command-line bioinformatics tools. It is still, however, the best choice for anything requiring a graphical interface. Almost all bioinformatics tools I can think of that have a graphical interface use Java (FastaQC, Cytoscape, Mauve, MEGAN...). It just works, without the hassle of having users install Python (or other) based UI toolkits such as wxPython, Tcl/Tk, etc. You also get goodies such as Java Web Start or Java Applets to further ease the access to your software.
If the Java language itself is not something you want to invest on, other languages can be used on the same underlying platform (the JVM, or Java Virtual Machine). Python is one (Jython), as well as Ruby (JRuby). Clojure has gained quite a traction recently, and offers the power of Lisp and functional programming.
I moved from "wet lab" to the "dry lab" at the beginning of my Ph.D. and because first time bioinformatics are hard to understand for a "wet lab" mind, I recommend Python and a bit of R. The best thing is that Python and R are objected oriented, then they have a lot of similarities. An of course, Linux is needed.
I suggest PERL for data mining (string handling) and R for statistical work. If you are interested in free PERL or R script in biology/chemistry the visit http://osddlinux.osdd.net/bscripts.php
I appreciate all info given by all the answers, that give a better view and practical experiment in their context. Thank you very much to all skilled persons.
I'm also for Python. I see it as the most flexible and rapidly evolving environment, so it suits well to NGS.
As for central repository, there is PyPI (https://pypi.python.org/pypi) where you can find most of popular modules:)
Perl found to be a good programing language for beginners. After expertise with perl, one might use python for complex functionality.
I would say it depends on the background. I started off with Java, since it is very easy to port. A lot already exists in Java as well ! (BioJava etc). Our group almost exclusively develops tools written in Java. It all depends on what you need and know though :)
Hello,
i think SQL .
This is a great place to start learning about databases structures and languages. http://www.w3schools.com/sql/
Good luck
Hello Oluwaseyi Shorinola,
There are notable programming languages that beginners in Bioinformatics can learn and master easily and quickly. This however, depends on the assimilation capacity and capability of such individual.
Programming languages such as Python, Perl and R are very important in Bioinformatics and Computational Biology research. Python is easier to learn, understand and master. Perl is also good. R is a statistical language for the analysis of biological or genomic-related data.
Kindly check the link below to download a very important research paper:
https://www.researchgate.net/publication/259147772_An_Analysis_of_Scripting_Languages_for_Research_in_Applied_Computing
This paper provides further answers to your question.
The research paper is titled" An Analysis of Scripting Languages for Research in Applied Computing"
Regards
Olugbenga Oluwagbemi
Conference Paper An Analysis of Scripting Languages for Research in Applied Computing
I also agree that Python would be easy to learn and very powerful for bioinformatics (many good bio-libraries exist already). For further statistical analyses, R is very good. Some people advise Ruby too :-))
FIRST: What are your current programming skills?
Advanced: C/C++/FORTRAN (where I come from) - fast, lots of libraries, can tailor exactly to your needs OR (what I am doing right now) JAVASCRIPT/NodeJS. Designed for working with browsers and the web, only half as fast as C/C++, but every bit as capable and certainly the language of the future.
Intermediate: Python. Note that the way it is used by most people, R isn't a programming language, it's a collection of packages that are used blindly and can do what you want ... and you hope the packages are accurate/correct. Python is easy, R has oddities but a fanatical and helpful community.
Beginner: Python via Codeacademy. Got to start somewhere and this is an easy language to learn but challenging to master (also it plays pretty well with the Web and browsers ... but it's sloooowwww)
Want to be unique? Consider Julia (julia.org). Very cool syntax, _very_ fast but also very new and bleeding edge.
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