Which language is more promising for the future of genetics: R or Python? Or maybe something else?

Question

Python

Actually a question in a subject.
I'm going to do DNA sequencing, specializing in bioinformatics and biogenomics.

Answer 1

what language you will use at work is more promising for you personally.
But I'll give you advice, don't put too much effort into DSL languages ​​(special-purpose languages ​​like R).
Roughly speaking, this is a language that is tailored for one task (for example, to count matrices), as a result, if you want to really create products, and your tasks are wider than just counting matrices, then you will have to invest in general-purpose languages ​​anyway, but then you you will learn that in general-purpose languages ​​you can solve all the same tasks (and using libraries, you can even solve them in a similar way), BUT at the same time your capabilities are not limited to just one area, as a result, it will become more convenient for you to solve problems simply by using one tool, instead of learning a separate language for each task.
Good programmers are usually very versatile, tasks and areas migrate, and who has a better base, who is more flexible -> he will win in the long run.
R can already be forgotten, there are no advantages there, it was created when there were no suitable tools in general-purpose languages, now all the tools are there, and even much more.
Another thing is which general-purpose language to choose, and there is a very rich choice.
Now the infrastructure needs are built in this way (very heterogeneous architecture, multi-core processors even on phones, multi-cluster configurations even for simple entrepreneurs).
Modern infrastructure needs put certain requirements on the language in which you can implement the capabilities of iron -> and these are languages ​​​​that move to a higher level of abstraction, languages ​​​​that implement the Functional Programming paradigm, it is well transferred to multi-core, multi-threaded, multi-cluster systems.
The most promising languages ​​​​and really used in business -> these are RUST, SCALA, SWIFT, ES6, they are slightly oriented to different platforms, but as a rule they can be used everywhere on other platforms (for example, on SCALA you can both compile into code for a virtual Java machine, and to native code, as well as to compile for phones or video cards, the same is approximately available from other languages)
the syntax itself is 90 percent common.
P.S.
python is the norm for learning, but it’s still slow and nothing can be done in production, so you shouldn’t bother too much - it’s popular due to the fact that all schoolchildren (and students of non-programming specialties) are taught abroad. Type it is considered that it is simpler though I here in general in an emphasis do not see than it is simpler than any SCALA.
P.P.S.
language is not as important as understanding what you are doing. Concepts and approaches are important.
as the first language you can choose any (even BASIC), in a few years you will better understand which language suits you best.

Answer 2

I am for Python, now it is the universal language of scientific computing

Answer 3

both
Rs are simpler
, but the one that learns first is even simpler

Answer 4

Python has a website for biologists https://pythonforbiologists.com/
There you can buy Python for Bioligist books + courses, including about DNA processing.
Here is one of the reviews:
A person from scratch learned to use Python for DNA analysis.

Answer 5

Are you going to do sequencing? And what are you going to do with pipelines? So Python is needed anyway.
But R is also needed. And also English ;-)
This is worth a look for the future bioinformatics

Answer 6

There are more statistics in R than in Python. I, however, like the algorithms from Matlab more.
They also argue that functional programming is the future. Here is a short course by Dmitry Soshnikov (the examples at the end are quite interesting. Although I like Haskell more than F#)

Answer 7

Check out Ruby. I won’t be surprised if there are already ready-made libraries for you to work with.

Answer 8

historically, the snake has a bunch of ready-made libraries of a scientific and applied nature, and, accordingly, the de facto standard for new developments, for legacy and codebase.

Answer 9

I think Julia is promising. Might be worth looking at Scala for efficient use of SPARK.