Me too
Would like to weigh in on the side of rskr. As a biologist employed by hard core computer scientists, they do their thing and I do mine to complete our deliverables. True, we mostly find each other's science mostly boring and I admit I am not even motivated to learn Linnux or shell scripting or much else that would contribute to making me a practicing bioinformaticist. I strongly believe biology totally needs core computational scientists as allies to provide us their perspectives and facilitations to add to and progress our own field. Sometimes the realizations I arrive at here about biological scientists are shameful= we do not yet have a global, uniform and systematic way to name new genes and to mention practicing the use of a gene ID number over some letter symbol is talking to the wall, for example.
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Originally posted by ETHANol View PostWhy must you be such a troll.
Normally, I would ignore such blatant trolling but being a confused molecular biologist not so long ago I thought it was important for this guy to understand what we are dealing with here, i.e. trolling and not useful information.
rskr, I'm done you win. All interpreted languages are without merit and you cannot be a good scientist if you use them. People that program in C++ are superior.
So I would like an apology if you have the time, since many of the things you said were not true and offensive to me.
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Why must you be such a troll.
Normally, I would ignore such blatant trolling but being a confused molecular biologist not so long ago I thought it was important for this guy to understand what we are dealing with here, i.e. trolling and not useful information.
rskr, I'm done you win. All interpreted languages are without merit and you cannot be a good scientist if you use them. People that program in C++ are superior.
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Originally posted by ETHANol View PostThat's it you think it is all about the program and never about the biology. Wake up and realize that a lot of people have other interests then you, which can be better served by learning one of these languages you despise so much. I am a wet lab scientist, I would be a total wast of my time to learn C++, while some Unix, Perl and R are extremely useful.
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That's it you think it is all about the program and never about the biology. Wake up and realize that a lot of people have other interests then you, which can be better served by learning one of these languages you despise so much. I am a wet lab scientist, I would be a total wast of my time to learn C++, while some Unix, Perl and R are extremely useful.
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Originally posted by ETHANol View PostHow many Cell, Science and Nature papers have been published using reused Perl scripts and Bioconductor packages? Has that all been a waste of time?
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Rskr, you are into pushing the state-of-the-art on the computing side. Some people are much more interested in the biology and find the computing side really boring. There is limited time in life and limited brain space (maybe not yours). So a lot of us learn what we have to to answer the biological question we are interested in.
I could go to a hot-spring and find a bug with a more efficient polymerase for PCR or I could just use one that is currently available to do interesting research. Some people have made their life's work the former, many more have focused on the latter. Some people learn C++ and come up with better algorithms some use the existing ones and write perl scripts to do interesting research. Why do you have a problem with that? How many Cell, Science and Nature papers have been published using reused Perl scripts and Bioconductor packages? Has that all been a waste of time?
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Originally posted by ETHANol View PostHere is some advice, anyone that says language xxxx is garbage and a waste of your time is blind (unless you are talking about some language of yesteryear).
Rskr, you ask, why piece together a bunch of algorithms that someone else wrote that are totally sufficient to answer the biological question you are addressing when you can make your own? Because it saves a lot of time, you'll publish your project sooner, which usually means in a higher impact journal, which means better career options.
We could blow our own pipets in the lab from glass, but that wouldn't make us better scientists.
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Here is some advice, anyone that says language xxxx is garbage and a waste of your time is blind (unless you are talking about some language of yesteryear).
Rskr, you ask, why piece together a bunch of algorithms that someone else wrote that are totally sufficient to answer the biological question you are addressing when you can make your own? Because it saves a lot of time, you'll publish your project sooner, which usually means in a higher impact journal, which means better career options.
We could blow our own pipets in the lab from glass, but that wouldn't make us better scientists.
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Originally posted by ETHANol View Postrskr does not understand
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rskr does not understand that there are a lot of biologists that are more interested in biological questions then hard core computer science. He likes to try to diminish anyone and any work that uses an interpreted language. He has a lot of work ahead of him. We use the tools that allow us to answer our questions with the least work.
This is the path I would suggest:
1. You will not learn anything unless you are actively and currently using it for something. So come up with a project you will use this stuff.
2. Learn some Unix
3. Learn some Python and/or Perl (Python is structured more like R so it can help with the next step, but I know Perl better).
4. Learn some R.
5. Keep working on what interests you.
If after all this you decide you want to mostly give up molecular biology and become a hard core computer scientist, then you can move onto C++.
Great place to start:
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To rskr's point, yes C is a good choice if you plan on doing a lot of fundemental algorithm development or are at a point in in your life where you are interested in programming from a mostly academic perspective and wouldn't be hampered by it's (or Java's) longer development times. If on the other hand you need to be quickly productive and are mostly interested in piecing together and interpreting NGS and other high-throughput data using the vast amount of open source analytic programs available, I believe (having worked in industrial bioinformatics for years) you would be much better off with Perl (my favorite) or Python, neither of which are fads.
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Originally posted by dpryan View PostMy 2 cents:
- The Unix CLI: This includes common commands such as awk, sed, and cut. I would also include shell scripting in this. This bullet point is required to put together any sort of basic analysis pipeline.
- R: Inevitably, you end up needing to crunch number in R, so go ahead and get at least a passing familiarity with it. This may include various bioconductor packages, depending on what you're doing
- Python or Perl: It doesn't really matter which one. You can do pretty much anything in these languages, though they have their limitations.
- C/C++/Java: If you get to the point of writing more "heavy duty" programs that require any significant performance then you'll need one of these. You would generally learn one of these last.
It's probably best to learn things in that order, possibly swapping the order of R and Python/Perl.
Totally agree - and swapping Python/Perl before R
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In a forward looking manner I wouldn't bother with Perl/Python/Java they are mostly just fads and any location you might want to work is just as likely to use the one you don't know, for no other reason than the CEO liked the monty python jokes or coffee. These scripting languages are easy enough to pick up if you know how to program in C, and most cool molecular dynamics simulators are in C for obvious performance reasons. Unix command line utilities are very handy for getting things done, and PERL and Python both draw heavily on the conventions so if you encounter a script done in either of these you should be able to figure out what it does(knowing linux that is).
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My 2 cents:
- The Unix CLI: This includes common commands such as awk, sed, and cut. I would also include shell scripting in this. This bullet point is required to put together any sort of basic analysis pipeline.
- R: Inevitably, you end up needing to crunch number in R, so go ahead and get at least a passing familiarity with it. This may include various bioconductor packages, depending on what you're doing
- Python or Perl: It doesn't really matter which one. You can do pretty much anything in these languages, though they have their limitations.
- C/C++/Java: If you get to the point of writing more "heavy duty" programs that require any significant performance then you'll need one of these. You would generally learn one of these last.
It's probably best to learn things in that order, possibly swapping the order of R and Python/Perl.
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