Introduction to Genomics for Engineers • Hacker News Board

Re: Introduction to Genomics for Engineers

#21

Post by altruios » Mon, Jul 06, 2026, 3:08 PM UTC

fabian2k wrote: post #13

throe9394ir wrote: post #11

Earlier quoted context omitted.

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!

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 p…

...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.

Re: Introduction to Genomics for Engineers

#22

Post by murzynalbinos » Mon, Jul 06, 2026, 3:09 PM UTC

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.

Re: Introduction to Genomics for Engineers

#23

Post by jwgarber » Mon, Jul 06, 2026, 3:10 PM UTC

maaaaattttt wrote: post #4

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 th…

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

Re: Introduction to Genomics for Engineers

#24

Post by almostjazz » Mon, Jul 06, 2026, 3:29 PM UTC

throe9394ir wrote: post #11

fabian2k wrote: post #10

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.

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!

Can you point to where these ideas have been confirmed as objectively not true?

Re: Introduction to Genomics for Engineers

#25

Post by throwaway676712 » Mon, Jul 06, 2026, 3:31 PM UTC

jghn wrote: post #9

maaaaattttt wrote: post #4

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 th…

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.

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)

Re: Introduction to Genomics for Engineers

#26

Post by throwaway676712 » Mon, Jul 06, 2026, 3:37 PM UTC

grey413 wrote: post #5

maaaaattttt wrote: post #4

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 th…

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.

[dead]

Re: Introduction to Genomics for Engineers

#27

Post by celltalk » Mon, Jul 06, 2026, 3:56 PM UTC

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!

Re: Introduction to Genomics for Engineers

#28

Post by grey413 » Mon, Jul 06, 2026, 3:57 PM UTC

altruios wrote: post #21

fabian2k wrote: post #13

Earlier quoted context omitted.

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 p…

...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.

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.

Re: Introduction to Genomics for Engineers

#29

Post by engineer_22 » Mon, Jul 06, 2026, 4:13 PM UTC

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

Re: Introduction to Genomics for Engineers

#30

Post by asdff » Mon, Jul 06, 2026, 5:53 PM UTC

throwaway676712 wrote: post #25

jghn wrote: post #9

Earlier quoted context omitted.

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.

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 uncharted territory here.

Meanwhile human geneticist doesn't even need to collect any data. Reference genomes are always being improved. People dump their data into public repositories (at least public for other credentialed researchers). A couple emails and filling of approval forms and you too can have access to 10,000 patient samples of some human disease already sequenced for you to an acceptable depth. Of course you will still need to pay for downstream compute needs in money and your time crafting the analysis to suit your reasoning, but still, half the battle is already won when you work on these well trodden paths. So much necessary groundwork has been performed by others for you already.