31–40 of 48 posts
Love the guide, out of curiosity, what is your background and what inspired you to create this?
I've seen it mentioned by one of their people in a recent whoishiring thread and found it neat.
The course was mentioned in a recent whoishiring thread. Sounds like it could be a purposeful place to work.
I'm not affiliated in any way, just found it interesting.
Earlier quoted context omitted.
Just a couple days ago I argued with an HN poster who quipped that biology is stamp collecting. A non-negligible number of "mathy" engineer types (not actual mathematicians, those usually understand the complexity of biology and even gladly contribute to the field) seem to think all biologists are quirky eccentrics dedicating 30 years to a single protein or a species of ants in the Kalahari desert. (Not that these do…
The rub is of course it is actually far harder to work on your rare species of ant than to do something more "noble" like human genetics. You may have to build the reference genome yourself working on your ant before you can begin with other work. Collecting your ant samples and processing them eventually into raw sequence reads. You may have to optimize this library preparation process yourself if you are really in…
I'm not a maths person; that's just what I've observed.
> 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 e…
As a software developer who spent nearly 10 years founding and building a genomic startup, this is a good start, but does have a lot of vast oversimplifications and a few inaccuracies. The people making this know what they're doing, so I'm sure these are known shortcomings they likely deemed necessary for a quick introduction. You'd need a further study at the end to start being able to do some real-world work.
As a software developer who spent nearly 10 years founding and building a genomic startup, this is a good start, but does have a lot of vast oversimplifications and a few inaccuracies. The people making this know what they're doing, so I'm sure these are known shortcomings they likely deemed necessary for a quick introduction. You'd need a further study at the end to start being able to do some real-world work.
https://news.ycombinator.com/item?id=33734846
2022 (310 points, 82 comments)
As a software developer who spent nearly 10 years founding and building a genomic startup, this is a good start, but does have a lot of vast oversimplifications and a few inaccuracies. The people making this know what they're doing, so I'm sure these are known shortcomings they likely deemed necessary for a quick introduction. You'd need a further study at the end to start being able to do some real-world work.
The definition of haplotypes is definitely incorrect am I misunderstanding anything?
1. Everything, and I mean everything, is stochastic. There is nothing in biology that is a guaranteed "if X then Y," there's nothing in biology that is a guaranteed "X is used for Y," "X is only used for Y," or "only X is used for Y." Even stuff that seems like it _should_ be that way isn't. RNA folds into useful shapes, the codon table varies between organisms, enzymes will target and modify multiple substrates, and metabolic pathways can and will run in both directions depending on the circumstances. Understand this, internalize this.
2. Biology is a physics problem, not an informatics problem. There's no API boundaries between different "layers," because everything is molecules jostling against other molecules. This means things like the geographic distribution of molecules within a cell can and will have serious effects on gene expression and biological processes. What's more, that "no API boundaries" extends to the cellular level - the "cell wall" is a thing cells use both sides of, bacteria regularly swap genes and genetic material, and metabolic pathways will pass between unrelated organisms.
Basically, everything we've ever done to turn engineering into a tractable problem does not exist in nature. Nature grabs whatever happens to be right at hand and shoves it into use. Consequently, it is _devilishly_ complicated to model, because every simplifying assumption you want to make has exceptions that stack to "your model works perfectly exactly once on a Tuesday at 3pm, but only if the humidity is over 72%." This is also why you'll notice your lab biologists are the kind of superstitious that would make a pagan soothsayer say "oh come on, it's not _that_ bad."
It's an awesome, amazing field, and there's huge contributions you can make as an engineer, but step one is shut the hell up and listen to the scientists, and step two is to learn that every time you hear "X does Y," your next questions should be "under what circumstances?" "how often?" "when doesn't it?" and "what happens then?"