> CRISPR requires some manual steps in the lab to make it work well. You can design the protein and guide RNA and spacers until you're blue in the face, but if you can't find a specific enough cutting site on the genome, or one that is close enough to your target, or something like that, you'll wind up with a lack of specificity in that cut, which can lead to unwanted mutations.

I am not a biologist, but nevertheless I found out something interesting today regarding the problem of designing good guide RNA via machine learning.

The work that I learnt about today addresses 2 key questions via machine learning approaches: off-target effects of CRISPR (http://biorxiv.org/content/early/2016/10/05/078253) and optimization of the guide RNA sequence to maximize on-target activity and minimize off target effects (http://www.nature.com/nbt/journal/v34/n2/full/nbt.3437.html).

I learnt of this stuff from one of the authors who works at MSR New England (http://nicolofusi.com/).

Also of possible interest is their project page https://www.microsoft.com/en-us/research/project/crispr/, where they are planning to release a cloud-based service for end-to-end guide design by incorporating the approaches developed in the above work.