Introduction to Genomics for Engineers

https://learngenomics.dev/docs/biological-foundations/cells-genomes-dna-chromosomes/

by yreg • 20 days ago

273 43 20 days ago

learngenomics.dev

shnksi• 17 days ago

Love the guide, out of curiosity, what is your background and what inspired you to create this?

yreg2• 16 days ago

Oh, I'm not the author! This was actually made by St. Jude's hospital.

I've seen it mentioned by one of their people in a recent whoishiring thread and found it neat.

ramon156• 17 days 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

maaaaattttt• 17 days 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.

grey4132• 17 days 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.

jghn• 16 days 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• 16 days 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)

cyberax• 16 days 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.

jltsiren• 16 days 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.

jwgarber• 16 days 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

rvz• 16 days ago

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.

Zie_Mordecai• 16 days ago

Curious, have you thought about writing articles about the things that you are concerned with?

jyounker2• 16 days ago

I wouldn't worry. The guide covers very basic material. It's stuff any undergrad genomics graduate should be able to recite from memory.

These aren't things you get by reading full chapters of an introductory textbook. These are the things you get from reading a few chapter summaries. (Of even just a book's intro.)

offbynull• 16 days 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

fabian2k2• 16 days 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• 16 days 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!

fabian2k2• 16 days 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.

almostjazz• 16 days ago

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

epihelix2• 15 days ago

I'd be extremely surprised if either gender or the propensity to believe in religion are 100% nature or 100% nurture - anything related to the mind is a mix of both.

shevy-java• 16 days ago

In plants and animals, DNA is broken up into a number of large sequences called chromosomes that are tucked into the nucleus.

This is a weird description, because ... it is not really "broken up". Each chromosome could be shuffled and put into different cells in different numbers. Now, it is unlikely that the resulting cell would be viable or useful, but my contention here is the "broken up" part. Chromosomes are just a way to handle the genome set. There are reasons why bacteria do not have chromosomes and this has mostly to do with the amount of DNA. To call this "breaking up" is a very strange description. (Size is not the only reason; duplication of the DNA before cell division is another important factor; bacteria usually have just one origin of replication, eukaryotes have several on each chromosome, otherwise the S-phase in the cell cycle would simply take too long.)

Each genome is a biochemical database that, if properly accessed, can inform how our bodies function.

This is also a very strange description, aka "biochemical database". Not everything in a genome has a role with regards to biochemistry or metabolism. Some is just regulatory RNA; some of this relates to metabolism, but you also have e. g. piwiRNA or silencers of transposons and so forth. That in itself has only very rarely a biochemical function, with some exceptions (e. g. I would classify tRNA as related to metabolism, and many viruses have tRNA or use tRNA as quick-starters, but most of those regulatory RNAs do not have any function for metabolism directly, other than e. g. repurposing energy towards their own reproduction).

To me it seems as if the article was written by an engineer. That's fine, but it also means that the thinking is quite biased. Genetics is not quite so easy to engineer; a good example are leaky promoters used in synthetic biology (just ask the people who use such promoters how to make them un-leaky) or off-target cleavage effects in CRISPR-Cas(9 or whatever is used); I am pretty certain they'll give excuses as to why 100% accurate gene therapy isn't yet ready for the masses. And they'll do that for quite some years to come, I bet, usually hiding behind "it will cost too much" - when in reality, it should cost very little, if it were to work, rather than this just becoming the new meta-milking scheme.

adrian_b• 16 days ago

"Broken up" may not be the best choice of words, but you did not explain which synonym would make you happier: "divided", "segmented", "fragmented", "split", etc.

The DNA of a nucleated (a.k.a. eukaryote) cell is indeed split into multiple chromosomes and the number of the chromosomes and the number of genes on each chromosomes and the sequence of the genes on each chromosome are normally constant for a species and very similar for closely related species. The cells that have an incorrect number of chromosomes (which happens when a cell division does not work correctly) will normally die soon, because their DNA is incomplete.

Only when a cell has all the normal chromosomes, but also some extra chromosomes, it has a chance to survive, even if the extra chromosomes may interfere with some internal processes. Because superfluous chromosomes are much less harmful than missing chromosomes, there have been cases, rare at animals, but frequent at plants, when the entire genome has been doubled, when some cell division has failed, but the descendants of that cell have survived.

There are animals for which the number of the chromosomes and the sequence of the genes on them has remained unchanged for many hundreds of millions of years, though there are also animals where the DNA has been completely rearranged, because some chromosomes have fused into a single chromosome, other chromosomes have split into multiple chromosomes, and on some chromosomes the genes have been shuffled.

Nowadays, it is known that it is very likely that the common ancestor of all animals except comb jellies had 29 chromosome pairs (humans have 23 pairs). In most animal branches there were more chromosome fusions than chromosome splittings, so in the present most animals have fewer than 29 chromosome pairs.

Among the animals with the most conservative genomes are some sponges, some jellyfish, many echinoderms and some of their relatives, i.e. acorn worms, the lancelets, some nemertean worms and some bivalves.

jyounker2• 16 days ago

To me it seems as if the article was written by an engineer. That's fine, but it also means that the thinking is quite biased.

It doesn't read to me as if it's written by an engineer. It reads to me as if it's written for engineers. A well written piece of introduction material bridges the gap between it's audiences, and must leave out significant detail.

epihelix2• 15 days ago

As a molecular biologist / genomics PI, these examples read fine to me. It's meant as an introduction, not a compendium of every possible nuance.

I had a software engineer do a bioinformatics PhD in my group several years ago, and I wish this had existed to show him. It would have saved all of us a lot of time, and him a lot of angst and confusion.

I used to think it would be the other way around, but having supervised grad students from both sides of the divide, I can say with some certainty that it's easier (in general) to teach molecular biology majors coding, than it is to teach software engineers cloning.

(There are exceptions of course, both ways.)

The reality is that to succeed in this field now, you need both sets of skills.

devlovstad• 16 days 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.

egyptianblue• 16 days ago

This is super cool, thanks

dwa3592• 16 days 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.

murzynalbinos• 16 days 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.

celltalk• 16 days 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!

engineer_22• 16 days 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

yreg2• 16 days ago

This website was made by St. Jude Children's Research Hospital. AFAIK they are a non-profit which runs treatment clinical trials on children cancer patients and doesn't bill them for anything.

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.

JangoSteve• 16 days ago

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.

jnpnj• 16 days ago

What was your reading list ? I plan to add more medical / genomic knowledge to my skillset.

dumb1224• 16 days ago

The definition of haplotypes is definitely incorrect am I misunderstanding anything?

whilenot-dev• 16 days ago

How is the definition incorrect? I know haplotype as all the genomic variation on a single gene. And since every gene comes in two copies, two haplotypes define one diplotype.

mellosouls• 16 days ago

Previously:

https://news.ycombinator.com/item?id=33734846

2022 (310 points, 82 comments)

roughly• 16 days ago

There are two things you absolutely must understand if you're going into biology as an engineer:

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?"

ena0autos• 16 days ago

Thanks Bookmarked

larkfofty• 14 days ago

This is great. As someone who was recently diagnosed with cancer, I've found myself quite interested in furthering my knowledge on the topic.

For someone interested in the field with only a computer science background, are there any jobs in the field that could reasonably be transitioned into without any formal biology education?

Introduction to Genomics for Engineers

Related Stories