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 to this, even though the dimensionality and minor details are wrong. Furthermore, the term “pileup image” refers to a common way of visualising genome–read alignments [3]. The DeepVariant tensor is not a pileup image but it is very close. And the tensor can be converted in to an image [4] but as mentioned this requires some transformations (splitting the channels, and rescaling the values).

[1] https://bioinformatics.stackexchange.com/q/4098/29

[2] I initially claimed the paper didn’t mention this. That’s wrong; apologies.

[3] https://www.google.com/search?tbm=isch&q=genome+pileup

[4] https://github.com/google/deepvariant/blob/r0.7/docs/visuali...