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Posted by yreg 4 days ago

Introduction to Genomics for Engineers(learngenomics.dev)
171 points | 25 comments
maaaaattttt 6 hours ago|
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.
jwgarber 2 hours ago||
I'm working on a project in malaria genetics this summer, and I was shocked to find out that the entire analysis toolkit is entirely based on math and statistics (and some non-trivial stuff too, e.g. hidden Markov models to predict CNV). Genotype likelihoods throw an extra wrench into the process, since even basic stuff like predicting allele frequencies requires a maximum likelihood estimator instead of simple counting. This whole area was quite eye-opening, and I'm still amazed that reading billions of base-pairs in DNA sequencing reliably works.

Also gotta shout out to these incredible molecular animations by WEHI: https://www.youtube.com/watch?v=7Hk9jct2ozY

jghn 4 hours ago|||
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.
throwaway676712 2 hours ago||
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 don't exist or aren't worthy of respect, but they don't score high in the sophomoric 'hardness scale' of fields that these mathy types still subscribe to)
jltsiren 3 hours ago|||
And the foundations of those statistical approaches are built on heuristics and shortcuts.

For example, sequencing instruments include base quality strings in the output. Base qualities are estimates how likely the instrument got each sequenced base right. But most people don't want to store that much noise, especially when the actual data is highly compressible. So the base qualities get quantized using more or less principled methods that seem to work well empirically.

Read aligners make similar estimates of how likely they got the correct alignment for each read. Those estimates are typically based on simplistic models and a number of assumptions. There are two main components in the estimate. One is based on comparing the primary alignment the aligner chose to the secondary alignments it also found. Another is an estimate that the aligner didn't find the correct alignment, because that part of the sequenced genome is too different from the reference. The latter is obviously handwavy. And the aligner cheats in the former. Because people don't want to wait 10x or 100x longer for better results, the aligner gives up early and estimates how good secondary alignments it might have found if it had actually done the work.

And then there is variant calling. At some point, the state-of-the-art callers were statistical. But then people got better results with neural networks. Or at least the results were empirically better.

grey413 5 hours ago|||
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.
throwaway676712 2 hours ago||
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cyberax 4 hours ago||
To expand this a bit, most sequencing methods are exact, and have a low error rate (except nanopores).

But they produce short reads, and because DNA is full of repetitive fragments, it's not always clear where the read came from.

We also have two copies of genes, which also further complicates matters.

The first startup where I worked, developed synthetic long reads on top of Illumina's hardware. We could stitch together 50kbp reads, which really helped with de-novo sequencing.

fabian2k 4 hours ago||
One part that people from the software side tend to underestimate is how fuzzy and analog everything in biology is. Genomics look more predictable and organized at first, but even these parts are quite fuzzy and subject to all kinds of physical effects.

I'd strongly recommend in reading up on the parts of cell biology that come after this. Otherwise you'll get the wrong impression of how messy biology actually is.

throe9394ir 4 hours ago|
And most of genomics is still stuck in 1930ties. Many people believe gender is somehow related to genes, which is objectively not true! Or that genes are somehow related to your religion!
fabian2k 4 hours ago|||
Scientists tend to understand that part, that's more of a political/cultural thing (I'm ignoring the language part here entirely about which terms to use for which concepts here).

There's the X and Y chromosomes, those produce a binary result (unless you have a genetic anomaly). And after that comes the messy and fuzzy parts I mentioned, where those genes trigger changes in hormone levels and development. And those parts are analog, very complex and contain a lot of different parts. So the outcome is not binary anymore.

altruios 2 hours ago|||
...well actually...

There are more combinations than merely having only X, or an XY combination. And there is more fuzziness even in the Y and X expression, as you said. It's fuzzy all the way down. The tale of Binary results has always been from compression of reality: Always has been.

grey413 1 hour ago||
As it happens, in humans there is a single gene on the Y chromosome, named SRY, that typically switches on male-linked traits.

But you're right, the full range of biological possibilities is very fuzzy . SRY itself a just a regulatory switch that other sex-linked traits are conditionally dependent on. If the switch gets broken, you develop as female. If genes that support the switch break, you might develop as female. If a sex-linked trait downstream from SRY mutates, then pretty much anything can happen. And other species do sex determination completely differently. Hell, a lot of bacterial sex basically involves throwing pseudo-viruses at each other.

almostjazz 2 hours ago|||
Can you point to where these ideas have been confirmed as objectively not true?
offbynull 5 hours ago||
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.

[1] https://cogniterra.org/course/64/info

dwa3592 3 hours ago||
This is very very nice. when you are reading this, just keep this in the back of your mind - inside a cell- things are floating around constantly at a very high speed. those things do not have any crisp shape or boundary. so how do we tell them apart? they are phase separated. if you put an oil drop in water, you can still see the oil drop and water and tell them apart. that's a very high degree of phase separation. inside a cell the degree of phase separation is much lower. just putting this out here so that you could appreciate the complexity of the biology that you are reading. my wife educated me on this a bit.
devlovstad 3 hours ago||
I've worked for a year in a lab doing cancer genomics and had to learn everything from scratch, since my background is in computer science.

It's definitely possible to learn enough to be productive within a few months, but to actually comprehend and understand the underlying biology takes much, much longer. I still don't understand much of what is presented by people from other labs outside of my specialty.

celltalk 1 hour ago||
Super!

Maybe a section on RNA degredation and DNA stability and how it would affect sequencing would be nice.

Also, down stream analyses are largely missing e.g. differential analysis, pathway enrichment. Not to mention newer single cell techniques and their up/down sides. But good start!

shnksi 6 hours ago||
Love the guide, out of curiosity, what is your background and what inspired you to create this?
murzynalbinos 2 hours ago||
One area that might be worth expanding in future sections is how these concepts scale when moving from single genes to whole-genome analysis and polygenic traits.
ramon156 6 hours ago||
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
engineer_22 1 hour ago|
Waiting for an enterprising hacker to develop mosquito gene drive in their garage. You could probably develop a thriving recurring income stream if you develop something that works
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