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  • #31
    Originally posted by Joann View Post
    Hi Samanta,
    Two very good links, thanks for the posts.

    You are welcome !!
    http://homolog.us

    Comment


    • #32
      Originally posted by chadn737 View Post
      I don't think the majority of people really care. I know I don't. I'm first and foremost a biologist. Sequencing is just a tool. Bioinformatics is just a tool. The real scientific question is the biology, not which is the best programming language. 10 years from now C++ and most of the bioinformatics will be outdated and lie unused, sequencing will be completely different, but the biology will remain. I think most of the hard core computer scientists here get that and certainly the biologists do. For most of us it is a waste of time writing new programs or rewriting old ones in a different language. It is far far smarter spending an extra hour of my time reusing a slightly slower program written by someone else in perl or python or java and getting my answer that week than spending a year trying to develop something completely new and then getting scooped by the guy who focused on the biology.

      I've collaborated with enough computer scientists to know that it typically goes one of two ways:

      1) They reuse tools already out there, which would be no different than what I could do on my own.

      or

      2) They want to develop something completely new and then I don't get my answer for 6 months, when I could have had it within the week and begun doing the follow up experiments.

      So I have come to the conclusion that if I am going to collaborate to have that nice new program written in C++, I'd rather do my own work and get that published and let the Computer Scientist develop a program around already published data. Because if I get scooped waiting around that long, I'm the one whose screwed.

      Geez !! What a warped view of the world.

      Computer science has two components - (i) algorithm development and (ii) coding the algorithm into some programming language. A new algorithm is a mathematical discovery that sometimes takes decades to develop, but once it is in place, if can revolutionize all aspects of science and non-science, including your beloved sequence analysis. Here is the development history of one chain of algorithms -



      You may notice that when Myers and Manbar were working on the concept of suffix arrays, they had no clue about how the future of sequencing technology would develop, yet two important lines of programs for short read analysis (Bowtie/BWA and String graph assemblers) rely on mathematical constructs developed by them.

      Just like you have a reward structure regarding quick publication of your sequence-related paper, computer scientists have a different reward structure related to development of new algorithms. Historically it has been found that their reward structure contributes more to biology than another incremental biology paper. So biologists themselves (those more knowledgeable than you) encourage discoveries of new algorithms.
      http://homolog.us

      Comment


      • #33
        Originally posted by samanta View Post
        Geez !! What a warped view of the world.

        Computer science has two components - (i) algorithm development and (ii) coding the algorithm into some programming language. A new algorithm is a mathematical discovery that sometimes takes decades to develop, but once it is in place, if can revolutionize all aspects of science and non-science, including your beloved sequence analysis. Here is the development history of one chain of algorithms -



        You may notice that when Myers and Manbar were working on the concept of suffix arrays, they had no clue about how the future of sequencing technology would develop, yet two important lines of programs for short read analysis (Bowtie/BWA and String graph assemblers) rely on mathematical constructs developed by them.

        Just like you have a reward structure regarding quick publication of your sequence-related paper, computer scientists have a different reward structure related to development of new algorithms. Historically it has been found that their reward structure contributes more to biology than another incremental biology paper. So biologists themselves (those more knowledgeable than you) encourage discoveries of new algorithms.
        Please don't read into it something I did not say. I am not criticizing the work of computer scientists. I specifically criticize the attitude of rskr that is condescending and dismissive of any biologist or computer scientist that doesn't create a new program from scratch in his language of choice.


        While the continued development of new algorithms and programs are of great use to Biologists, their development is the primary concern of the specialists, not the biologist. Even for the computer scientists, it makes no sense to develop new programs from scratch for everything. It also make no sense for the biologist to spend the vast amount of time necessary to learn C for simple and mundane applications if they already know Perl and can implement it in Perl in a shorter amount of time, even if it takes an hour longer to run. I can spend those extra few hours of runtime doing wet lab experiments. And since I don't know C, I can actually get more done using Perl then all the time it would take me learning a new language and going through the hassle of implementing it.

        And frankly, why am I going to send my data to someone else to analyze if they are simply going to use the exact same tools that already exist and which I already know how to implement? Why should I wait 6 months or a year for my results while they create a completely new program when I can get the results in a week reusing tried and true programs?

        If the computer scientist wants to create a new algorithm, then they are doing their job and that is sufficient for a paper in itself. Besides it is better for the computer scientist because then he gets the credit rather than having to be a co-author on a paper where the program takes second place to the data.

        They have their own careers to look after, I have mine. I understand that bioinformatics takes time to develop and I applaud those who develop it. But I am not seeking a career in the development of bioinformatic tools, nor do most biologists. We just use them and then its on to the next step. If there is no preexisting tool, then I'll take the time to work with the computer scientist and wait for them to develop one and then use it to get to the question in hand. But otherwise, I see no reason for the biologist not to take advantage of pre-existing tools and it is absurd to be dismissive of them just because they use a pre-existing tool or a language they are more comfortable with.

        I'm not dismissing anyone here or their relative contributions. I'm just being frank about the fact that there are more practical concerns.
        Last edited by chadn737; 06-11-2012, 02:58 PM.

        Comment


        • #34
          Originally posted by chadn737 View Post
          Please don't read into it something I did not say. I am not criticizing the work of computer scientists. I specifically criticize the attitude of rskr that is condescending and dismissive of any biologist or computer scientist that doesn't create a new program from scratch in his language of choice.

          ......
          I'm not dismissing anyone here or their relative contributions. I'm just being frank about the fact that there are more practical concerns.
          Sorry, I misunderstood your original comment. You make all valid points. You need to look after your self-interest (which is to get the best biological insights from your data) irrespective of which computer programs were used to get there. So, if you are competent enough to code/install and run computer programs, I see no reason for wasting time with a computer scientist trying to be in your shoes. The computer scientists, on the other hand, look after their self-interests of finding best algorithm.
          http://homolog.us

          Comment

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