deepvariant

Yeah, the way they talk about this is admittedly confusing. The best way for me to think about it (from [1]) is to mentally add quotation marks around the word “image” whenever the methods mention it. Mathematically the data is stored in higher-dimensional tensors with one dimension representing genomic coordinates, another dimension representing the sequence depth (i.e. one row per sequence read), and additional dimensions representing features of the sequence (such as nucleoside identity, read direction, error probability, match/mismatch with the reference, etc). I use almost the same kinds of tensors for work that has no tangible relation to images or visualisation. It’s simply the most straightforward way of representing this data as a tensor. But the similarity to images is so tantalising that even the paper’s methods [2] fall prey t

Interesting!! I did not know that, so I was wrong - but why does the GitHub repo then talk of pileup images? https://github.com/google/deepvariant Edit: I think I get it now, this notebook shows it: https://github.com/google/deepvariant/blob/r0.7/docs/visuali... The channels are easily representable as images, but the network doesn't use the images directly.

Yeah, definitely interested ;) Any chance the course has room for the other side of the coin? Namely, how neuroevolution and genetic strategies inform deep reinforcement learning? Am also interested in learning about the state-of-the-art in cloud based packages. I noticed recently Google released a tool called DeepVariant for use on their genomics platform. https://github.com/google/deepvariant Creating a universal SNP and small indel variant caller with deep neural networks https://www.biorxiv.org/content/early/2018/01/09/092890