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Introduction to Genomics for Engineers

learngenomics.dev

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Re: Introduction to Genomics for Engineers

#41

Post by tombakt »

mtlmtlmtlmtl wrote: post #27
dddiaz1 wrote: post #17

I have absolutely loved working in genomics. I am a huge believer that genomics will be a huge part of healthcare in the future, and i have two examples to motivate that point that I think may be interesting to the reader. 1) The Moderna vaccine was made with the help of illumina genome sequencing. They were able to sequence the virus and send that sequence of nucleotides over to moderna for them to develop the vacci…

The thing I'm most excited about long term is biocomputing. Having Turing complete programmatic control over biological systems has an absolutely endless list of transformative applications. Imagine being able to program bacteria that can "infect" the patient and attack tumor cells, or act as fodder to keep autoimmune disease in check. Or let's say we could program stem cells into "liver repair mode" to go and differ…

Sounds great until natural selection kicks in, and because DNA replication is largely a lossy process, suddenly the thing you programmed the organism to do mutates to do something else a whole lot more problematic.

Imagine a software heisenbug, but instead it's a life form that you can't kill -9.

The idea of tailor-made medicines in a vat is awesome, but as far as creating a bacteria to "specially target" certain cells seems like a disaster waiting to happen.

Re: Introduction to Genomics for Engineers

#42

Post by ALittleLight »

dekhn wrote: post #39
pgayed wrote: post #29

Earlier quoted context omitted.

This is the worst outcome of regulation of the life sciences. There is no REPL for the cell. No tinkering allowed. When Marvin Minsky was growing up in New York, neighborhood pharmacists owned fluoroscopes. He said those fluoroscopes were like “great black boxes” to him and that “those kinds of black boxes don't exist for kids anymore.”

Many modern bio experiments are almost exactly a repl. You build a system and then repeatedly interrogate it inputing some data using a Read (IE, you pass in some DNA), which is then Eval'd by the cell (warning: there will be side effects), "printed" in the form of some signal like a fluoresence, and then you loop back to the beginning. This is often called "closed loop laboratory." Unfortunately, each step ends up b…

What is the minimum it would take to run such a REPL at home? What hardware, life form, and knowledge would you need, at a minimum, to start making changes and seeing results?

Re: Introduction to Genomics for Engineers

#43

Post by gravelc »

Don't want to be too disparaging, but this to me doesn't seem to be an 'Introduction to Genomics', but more an introduction to read mapping and variant detection in human (or more broadly diploid) genomes.

Genomics stretches vastly beyond this - assembly and annotation to start with.

I'd argue the most interesting problem space for software engineers is outside of what is covered in the document.

Re: Introduction to Genomics for Engineers

#44

Post by gravelc »

penciltwirler wrote: post #4

Nicee, but I feel like really the only thing you need to know as an eng is DNA -> RNA -> Protein. Sometimes RNA -> DNA via reverse transcriptase. Everything else is just normal Python scripting.

Who knew complex large polyploid genome assembly (i.e. sugar cane) was just a matter of python scripting?

Re: Introduction to Genomics for Engineers

#45

Post by gravelc »

glofish wrote: post #32

Those looking for a proper and comprehensive introduction into genomics from a programmer's perspective should try the Biostar Handbook: https://www.biostarhandbook.com/ I have learned so much from it. It is an introduction into what is like to do genomics in a scientific environment. The content at the link the OP posted appears to be an oversimplified, high level and naive overview

This is indeed a far better resource.

Re: Introduction to Genomics for Engineers

#46

Post by mtlmtlmtlmtl »

tombakt wrote: post #41
mtlmtlmtlmtl wrote: post #27

Earlier quoted context omitted.

The thing I'm most excited about long term is biocomputing. Having Turing complete programmatic control over biological systems has an absolutely endless list of transformative applications. Imagine being able to program bacteria that can "infect" the patient and attack tumor cells, or act as fodder to keep autoimmune disease in check. Or let's say we could program stem cells into "liver repair mode" to go and differ…

Sounds great until natural selection kicks in, and because DNA replication is largely a lossy process, suddenly the thing you programmed the organism to do mutates to do something else a whole lot more problematic. Imagine a software heisenbug, but instead it's a life form that you can't kill -9. The idea of tailor-made medicines in a vat is awesome, but as far as creating a bacteria to "specially target" certain cel…

Those are certainly real problems, and I'm not a cell biologist, but I'm not convinced these problems are insurmountable.

For instance, it might be possible to use ECC to get around transcription errors. It could also perhaps be ensured that any rogue "clinical biocomputer" could be easily treated with antibiotics or specifically engineered bacteriophage virus.

Like I said, the technology is very far off from having real world applications like this. At the moment it feels like we're in the analogue of the 40s and 50s for conventional computing. The field is still just inventing the very basic building blocks. It's going to be very limited in use, wildly dangerous(look up mercury delay lines) and unreliable for decades to come.

Re: Introduction to Genomics for Engineers

#47

Post by faizshah »

One of my favorite books in this space is “BioInformatics Data Skills.” It’s just nice concise coverage of a lot of basic tech skills like git, bash, tmux etc. and then coverage of basic bioinformatics skills.

For me coming from a SWE background the computational skills are very easy to pick up especially if you work with bioinformaticians you can ask questions. It’s the genomics knowledge that is very difficult for an engineer to acquire.

Re: Introduction to Genomics for Engineers

#48

Post by conradev »

glofish wrote: post #40
conradev wrote: post #23

Earlier quoted context omitted.

If you want some personal motivation to get into genomics, you can get your whole genome sequenced for a few hundred bucks and play around with the raw files yourself. I used Dante Labs[1] and they are great. You can even ask them to delete your data and samples! [1] – https://dantelabs.com/

and you will learn almost nothing from sequencing and studying your own genome at best you waste your time, at worst you will find all kinds of things that are not there it is the Silicon Valley hacker mentality that thinks the life is some sort of computer where you can fiddle with parameters learn some biology first, then you can marvel at it and realize just how absurdly simplistic is to think you can read anythin…

I am not out here reading SNPs like tarot cards.

I wanted to play around with BAM files and it is much more engaging to play around with my own BAM file versus downloading one from a website.

It is also about data ownership. The value of a fully sequenced genome is limited, sure, but I still want that value without having to give my genomic data to 23andMe.

Re: Introduction to Genomics for Engineers

#49

Post by joshuahedlund »

penciltwirler wrote: post #4

Nicee, but I feel like really the only thing you need to know as an eng is DNA -> RNA -> Protein. Sometimes RNA -> DNA via reverse transcriptase. Everything else is just normal Python scripting.

That’s what I thought too until I learned about

- the dna that doesn’t code for proteins but makes up the vast majority of human dna

- the intron regions of genes that are translated into RNA but then sliced out of the RNA and not transcribed into protein and are 5x larger than the coding parts

Those two things alone are absolutely critical to understand to interpret a genome sequence. Of course there is much more.

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