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Comment · 8 years ago
I completely agree that there are many scientific libraries in python which scale up. I was addressing the article which showed a more advanced way to use python with the purpose of making it applicable to large datasets. If you were to implement a method from scratch or scale up to a larger dataset then you'll end up with using numba, numpy and dask. This is completely from a lower level programming perspective to implement and integrate methods rather than pipelining methods from higher level scientific libraries.

Just for some context: https://www.scipy.org/about.html https://www.scipy.org/topical-software.html

Comment · 8 years ago
The content in the article is a bit dated. Jupyter + Numba + Dask is the direction scientific computing (in Python) is taking. Ipyparallel is not really scalable in my experience.
Comment · 8 years ago
http://crispr.mit.edu/about

Designing a good gRNA is quite straightforward if you compare it to any other bioinformatics tasks. Even designing a degenerate primer is more complex than this.

Comment · 8 years ago
Do you mean the effect of the actual gene knockout, crispr specificity or off-spot mutations?
Comment · 8 years ago
Synthego custom crispr kit seems like a very useless service. Guide RNA design for knockout is extremely trivial. You just need a couple of 20bp+NGG sequence which is unique to the gene.. Who would pay to do that?..

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