Comments on “The anatomy of successful computational biology software”

Hi, just a few comments from this note,

Firstly, I think that for a tool to have a big success, it should:

* Initial user base

* Vacuum in terms of tools, it is hard to compete with already existing tool.

* Mathematica/Statistical interpretation

* Generic, all the mentioned tools are very generic or they are in the starting point of any analysis pipeline

* Simplicity, simple interface, simple commands

* Multiplatform

But I strongly disagree in some points:

Gentleman: I have found that real hardcore software engineers tend to worry about problems that are just not existent in our space. They keep wanting to write clean, shiny software, when you know that the software that you’re using today is not the software you’re going to be using this time next year. At Genentech (S. San Francisco, California), we develop testing and deployment paradigms that are on somewhat shorter cycles.

For me, he is talking about prototypes and not real softwares. He is worried about small prototypes softwares or even scripts.  Even Li says it:

Li: People not doing the computational work tend to think that you can write a program very fast. That, I think, is frankly not true. It takes a lot of time to implement a prototype. Then it actually takes a lot of time to really make it better.

There is also another problem:

Taylor: I don’t think there are good incentives for contributing to and improving existing software instead of inventing something new. The latter is more likely to be publishable.

Some args that Software developing is not science. You have to prove that you are doing science there.

A very important points are:

Trapnell: [..]  The computational folks need to learn more about statistics. The biology folks need to understand basic computation in order to even be able to communicate with the biostatistics crowd.


Krzywinski: In terms of data visualization, the idea that we can show all the data that we are collecting is long gone. We now need to look at the differences in the data sets, and help the user focus on the things that are important.

(Like EpiExplorer does)

By the way, did you realize that there is not epigenetic software in the list? (Only a critique on the tools for finding peeks)

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