This guide is also made from me (or some of the me from a couple years back). I haven't read the whole thing yet and it's probably clearly stated at some point (though one can deduce it with the beginning already) but the surprise for me was that this field is highly statistical. Before starting I had the (very) naive view that it was possible to read the genome as one reads a file and look at what's going on. But the sequencing technics (and accompanying algorithms) only allow to statistically read the genome. So variants/mutations found are only found with a given statistical certainty. If the sample wasn't well prepared for example it could be that this certainty is ultimately not high enough to do a proper analysis/diagnostic. It's a fascinating field (try to watch a video on sequencing by expansion, to feel how sci-fi this field actually is) that is very hard to approach with only high-school biology level and this guide is really well done to sort of bridge this first gap.
If you're an engineer and want to go deeper into the core algorithms behind genomics, there's a book / course called Bioinformatics Algorithms. It was a punishing read when I was going through it a few years ago (but rewarding). It's probably much better now given the state of AI.
Its like this was made for me haha ! I've been reading books about epigenomica to get an understanding. This is cool, will definitely spend my weekend going through it
Love the guide, out of curiosity, what is your background and what inspired you to create this?
> This Guide is written specifically by and for computer scientists and engineers. The underlying biology in cancer genomics can be exceedingly complex and requires years of study.
This looks like a great guide to read.
But I think before diving deeper and reading the rest of the guide, which granted it is from employees working in a lab inside of a hospital, I'd like to get the expert opinion of a geneticist or an expert biologist with years of experience in genomics to iron out any issues in the guide or give an additional proof-reading review.
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All one needs to do is look at the Claude Science thread here last week and note how many comments were surprised that it appeared to be a statistical/analysis tool.
Biology is often an intensely statistics-heavy field. A remarkably large part of statistics was developed to study issues in biology, particularly dealing with evolution and ecology.