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
The opening paragraph of this resource states its absolutely not about being a comprehensive introduction to genomics. I strongly disagree with the sentiment its naive or oversimplified. It's trying to give someone with no knowledge a working mental model to begin to dig into building a comprehensive view. A framework of analogy for many people is an extremely helpful device for learning, frequently left out by comprehensive scientific or engineering texts.
> Explore the secret of life through the basics of biochemistry, genetics, molecular biology, recombinant DNA, genomics and rational medicine.
It's really well done and genomics is the focus. I took many dozens of edX and Coursra courses over the years, this is one of the top 5% of the courses there I would say.
I don't understand the phrase "from a programmer's perspective", or "for Engineers" in the title on top.
As a programmer whos studied CS but also took numerous life science courses throughout my life. You want to learn biology you study biology, what does a "programmer's view", or an engineer's, have to do with it? You use the correct tool for the job, and having a background in both, I don't see this working out well, more like the opposite actually.
The point of looking at biology for an engineer or programmer should be to broaden ones horizons, not to use ones internal models build for a completely different field in another one that really is not like that at all. IMO it's best to forget all computer metaphors here.
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 vaccine - turning a classically biology problem, into a software problem, reducing the need for them to bring the virus in house.
2) Illumina has a cancer screening test called Galleri, that can identify a bunch of cancers from a blood test. It identifies mutated dna released by cancer cells. This is huge, if we can identify cancer before someone even starts to show symptoms, the chances of having a useful treatment dramatically go up.
Disclaimer: I work for illumina, views my own.
I wrote some more about why genomics is cool from a technical point of view here (truly big data, hardware accelerated bioinformatics) : https://dddiaz.com/post/genomics-is-cool/
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 differentiate into new liver cells.
Then the implications for things like drug synthesis with the ability to programmatically control enzyme levels to compile more or less arbitrary biosynthetic pathways into fast growing photosynthetic algea, turning CO2, water and sunlight into medicine.
It's still a long way off being at that level of applicability, but man oh man it's gonna change everything.
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.
There are two main flavors of jobs. For one you’ll want to be a phd in something like physics or math. For the other Amy standard software engineering background will do.
TBH I'm surprised how hard Illumina is already pushing Galleri as a product. Current ctDNA/cfDNA are imperfect for advanced cancers which should have a lot of shedding to begin with. Additionally CHIP is and outstanding issue. DNA methylation sequencing has promise but I feel more data would be needed to truly make diagnostic findings. So to see Illumina market it as a ready to go product is quite worrying. It may burn a lot of people
Really glad to see this, but it reminds me of the earlier HN post that said engineers don't go into genomics because it doesn't pay and requires a lot of investment in learning biology.
I worked in genomics, left this year because you’re underpaid and often disregarded “IT-help” that assists wildly over-educated and underpaid people driving the actual research in 95% of cases.
Thats why you stay though, the people are interesting and the work is meaningful and you directly see the fruits of your labors whilst contributing to a codebase that is by default open source.
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!
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 anything out of some random letter
Working with genomics technology is too far away from the money to become rich from. There are too many middlemen in-between technology and application.
But it's a fun subject, and as the technology develops, middle layers will disappear and then the money from expertise will become better.
The number of people that are both capable software developers and has a good understanding of cellular biology are quite few and will probably remain so for the foreseeable future.
In biotech, the end goal is a physical product or a service performed by a doctor or another highly paid professional. Those don't scale as well as software. The ratio of users to developers is also low. You are likely developing software for many niche tasks, which does not scale either.
And if you are considering roles in the academia, your productivity is not going to be high enough to justify a competitive salary. Productivity, in monetary terms, is defined by the amount of money you can bring in. Either directly on indirectly. In the academia, that usually means grants. You may be able to argue successfully to a funding agency that one software engineer is worth two postdocs, but not four.
The people studying yeast metabolism in grad school were always the ones with the best beer (especially the ones that created mutant strains). I think the two might be related.
There are a lot of starry eyed individuals who are ready to “sacrifice” stable welll paid career to “make a difference” by working on fields like biology.
It's not quite the same because the helpdesk person is probably paying a sizeable chunk of their salary on tuition. Presumably working in science would scratch that itch for you, so number to compare against is whatever you'd make elsewhere, minus what you spend on tuituon, minus however much it's worth to you to be able to focus on the science and not have to balance your time with some unrelated job.
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.
Starts with “ This Guide is written specifically by and for computer scientists and engineers”
And yeah it shows - contrived example after another, and honestly not a great description of anything.
If you want to truly understand genomics you have to understand how biology works. And honestly it’s great info for anyone even if you’re not getting into genomics or whatever.. why would you not want a working model of how life is put together? In that case I’d just recommend dusting off a biochem or cell bio text book and reading just the first 5-8 chapters. Typically they lay it out very simply from basic principles and the authors have far more experience and understanding and writing help than this weird tutorial course thing.
Do you have an example of a contrived example and explanation of why it is contrived, for the non-biologist to see why it is contrived?
I once tried reading a few chapters of a bioinformatics book explaining DNA, RNA, protein creation, etc. The basic idea seems very simple but to my mind they explained it non-systematically with too many words. There seems to be an internal information structure in these RNA- and DNA- related processes that was not being concisely presented and it seemed that if the writers presented the material in terms of computer-science concepts, so much time could be saved.
You can't present it as computer science concepts because it's not computer science.
For example, the central dogma of DNA transcribed to RNA translated to protein seems simple, but it's not.
In almost every instance, there are vague 'rules' and many many exceptions to these rules. For example, often coding regions in genes start with an ATG, but sometimes they don't. Sometimes splice sites (where the non-coding parts of transcripts called introns are chopped out) can be predicted, but a portion of the transcripts are not spliced at predicted sites for no obvious reason. Sometimes the predictions are just wrong. Sometimes the generated proteins are modified at specific locations which impacts their function, but again, sometimes not. Even whether the gene itself is 'switched on' (i.e. able to be transcribed) is impacted by many many things, such as unidentified transcription factors, or whether the chromosomal location itself is accessible or not. There are many many other things that impact the process.
There is no simple underlying concept as the system is not designed, it evolved and is quite different among different organisms, and even in different tissues or timepoints in the same organism. As long as it works and provides enough benefit to avoid negative selection, that's enough.
It starts by defining a cell as a bakery. First of all, what exactly is more systematic in comparing a cell to a bakery? Other than the fact that both things produce crap the analogy has no real substance. And there are so many wrong facts in that one paragraph (many of our genes are present as more than two copies in our genome, for one).
You are absolutely correct, there’s an information theoretical underpinning of genomics and systems biology that’s rarely if ever tackled in text books but (a) neither does this course tackle it, and (b) you can’t just skip on biochem basics and Jump to that. That’s like trying to become a physicist without learning math.
There's nothing about sequencing by synthesis, how blocking nucleotides are added one after another, pictures of the fluorescent nucleotides on the flow cells are image analysed, etc.
I think perhaps this (learngenomics.dev) resource is a little too shallow on some levels, but has interesting depth in odd places. I think there is a need to get users up to speed with things like the SAM format, which is very fundamental to 99% "dna sequencing" projects, but it's an odd format in some ways because it's quite low level, so trying to get people to understand how the basics of biology interact with it is worthwhile. I did my own attempt in this sometimes-updated blog post https://cmdcolin.github.io/posts/2022-02-06-sv-sam
I didn't get this from skimming the first page - but what will this let me do? If I take this course will I be able to mess with a cell or will I just learn some stuff about biology.
I saw a recent Lex Friedman podcast where the guest talks about "bioelectric patterns" and somehow getting a worm to grow a second head by messing with those patterns. I would absolutely start on this course now if it was a realistic pathway to doing something like that.
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 being extremely challenging, and there's tons of noise, and the cost of each Read, Eval and Print is far higher than in a programming language. Further, the "system" is running 38,000 other "threads" all of which have direct read and write access to your data, some of those threads consider your data to be the enemy and cut it up, while others are just randomly spamming your console with uneccessary debug log messages.
We have actually reached the point where some scientists have synthetically created a novel chromosome, and used a preexisting cell to bootstrap the new genome so that the cell eventually contains only protein from the new genome. To me, that represents a step beyond tinkering: it means we can create synthetic lifeforms with exactly and only the details we want, which makes studying them and engineering them far easier.
Interestingly, even though this tech exists, nobody has found any interesting use for it and it's not even really used to probe biology.
A better example would be gene therapy, which has been developing slowly over decades. A single person died in the a trial in the 90s and stopped development (that's the regulation part you're referring to) for decades. In other trials that don't include gene therapy, patients routinely die and they're just a statistic.
It's difficult to get into this field if you don't have a graduate degree. I was a double major, Computer Science and Biochemistry, with a minor in Biotechnology. I sent my resume to many biotech and pharma companies, but could not even get an interview. A lot of the jobs said you need 0 years experience if you have a PhD, but 10 years experience if you have a Bachelors. Now that I have 10 years experience as a developer, I've forgotten almost everything I learned in my science education, and I've lost interest.
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.
The space of startups cashing in on genomics but making shiny web apps that software engineers need to understand something about human diploid genetic variation is far higher though. Thats where the money and engineers are, not in fundamental algo development for slimemould assembly.
Readit News
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
https://www.edx.org/course/introduction-to-biology-the-secre...
by Professor Eric Lander
> Introduction to Biology - The Secret of Life
> Explore the secret of life through the basics of biochemistry, genetics, molecular biology, recombinant DNA, genomics and rational medicine.
It's really well done and genomics is the focus. I took many dozens of edX and Coursra courses over the years, this is one of the top 5% of the courses there I would say.
I don't understand the phrase "from a programmer's perspective", or "for Engineers" in the title on top.
As a programmer whos studied CS but also took numerous life science courses throughout my life. You want to learn biology you study biology, what does a "programmer's view", or an engineer's, have to do with it? You use the correct tool for the job, and having a background in both, I don't see this working out well, more like the opposite actually.
The point of looking at biology for an engineer or programmer should be to broaden ones horizons, not to use ones internal models build for a completely different field in another one that really is not like that at all. IMO it's best to forget all computer metaphors here.
----
By the way, since there was something about this yesterday, there also is this course: https://www.edx.org/course/principles-of-biochemistry - it too is very good. A good knowledge of organic chemistry is a prerequisite, but there are plenty of equally interesting course resources for that available too, including even Khan Academy (https://www.khanacademy.org/science/organic-chemistry), or to give a(nother) random link, https://ocw.mit.edu/courses/5-12-organic-chemistry-i-spring-...
Biology becomes a lot more fun with this foundation already established in ones head.
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 vaccine - turning a classically biology problem, into a software problem, reducing the need for them to bring the virus in house.
2) Illumina has a cancer screening test called Galleri, that can identify a bunch of cancers from a blood test. It identifies mutated dna released by cancer cells. This is huge, if we can identify cancer before someone even starts to show symptoms, the chances of having a useful treatment dramatically go up.
Disclaimer: I work for illumina, views my own.
I wrote some more about why genomics is cool from a technical point of view here (truly big data, hardware accelerated bioinformatics) : https://dddiaz.com/post/genomics-is-cool/
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 differentiate into new liver cells.
Then the implications for things like drug synthesis with the ability to programmatically control enzyme levels to compile more or less arbitrary biosynthetic pathways into fast growing photosynthetic algea, turning CO2, water and sunlight into medicine.
It's still a long way off being at that level of applicability, but man oh man it's gonna change everything.
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.
"The Amazon CloudFront distribution is configured to block access from your country."
^Most recent discussion I’ve seen.
I worked in genomics, left this year because you’re underpaid and often disregarded “IT-help” that assists wildly over-educated and underpaid people driving the actual research in 95% of cases.
[1] – https://dantelabs.com/
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 anything out of some random letter
But it's a fun subject, and as the technology develops, middle layers will disappear and then the money from expertise will become better.
The number of people that are both capable software developers and has a good understanding of cellular biology are quite few and will probably remain so for the foreseeable future.
In biotech, the end goal is a physical product or a service performed by a doctor or another highly paid professional. Those don't scale as well as software. The ratio of users to developers is also low. You are likely developing software for many niche tasks, which does not scale either.
And if you are considering roles in the academia, your productivity is not going to be high enough to justify a competitive salary. Productivity, in monetary terms, is defined by the amount of money you can bring in. Either directly on indirectly. In the academia, that usually means grants. You may be able to argue successfully to a funding agency that one software engineer is worth two postdocs, but not four.
Then there are also engineers from XKCD 1831 https://xkcd.com/1831
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.
And yeah it shows - contrived example after another, and honestly not a great description of anything.
If you want to truly understand genomics you have to understand how biology works. And honestly it’s great info for anyone even if you’re not getting into genomics or whatever.. why would you not want a working model of how life is put together? In that case I’d just recommend dusting off a biochem or cell bio text book and reading just the first 5-8 chapters. Typically they lay it out very simply from basic principles and the authors have far more experience and understanding and writing help than this weird tutorial course thing.
I once tried reading a few chapters of a bioinformatics book explaining DNA, RNA, protein creation, etc. The basic idea seems very simple but to my mind they explained it non-systematically with too many words. There seems to be an internal information structure in these RNA- and DNA- related processes that was not being concisely presented and it seemed that if the writers presented the material in terms of computer-science concepts, so much time could be saved.
For example, the central dogma of DNA transcribed to RNA translated to protein seems simple, but it's not.
In almost every instance, there are vague 'rules' and many many exceptions to these rules. For example, often coding regions in genes start with an ATG, but sometimes they don't. Sometimes splice sites (where the non-coding parts of transcripts called introns are chopped out) can be predicted, but a portion of the transcripts are not spliced at predicted sites for no obvious reason. Sometimes the predictions are just wrong. Sometimes the generated proteins are modified at specific locations which impacts their function, but again, sometimes not. Even whether the gene itself is 'switched on' (i.e. able to be transcribed) is impacted by many many things, such as unidentified transcription factors, or whether the chromosomal location itself is accessible or not. There are many many other things that impact the process.
There is no simple underlying concept as the system is not designed, it evolved and is quite different among different organisms, and even in different tissues or timepoints in the same organism. As long as it works and provides enough benefit to avoid negative selection, that's enough.
It's a mess, which makes it interesting.
You are absolutely correct, there’s an information theoretical underpinning of genomics and systems biology that’s rarely if ever tackled in text books but (a) neither does this course tackle it, and (b) you can’t just skip on biochem basics and Jump to that. That’s like trying to become a physicist without learning math.
There's nothing about sequencing by synthesis, how blocking nucleotides are added one after another, pictures of the fluorescent nucleotides on the flow cells are image analysed, etc.
This site looks like an ELI5 kind of treatment.
Amusingly that's literally like 80% true. Water is just a really big deal in biochem.
I saw a recent Lex Friedman podcast where the guest talks about "bioelectric patterns" and somehow getting a worm to grow a second head by messing with those patterns. I would absolutely start on this course now if it was a realistic pathway to doing something like that.
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.”
Unfortunately, each step ends up being extremely challenging, and there's tons of noise, and the cost of each Read, Eval and Print is far higher than in a programming language. Further, the "system" is running 38,000 other "threads" all of which have direct read and write access to your data, some of those threads consider your data to be the enemy and cut it up, while others are just randomly spamming your console with uneccessary debug log messages.
We have actually reached the point where some scientists have synthetically created a novel chromosome, and used a preexisting cell to bootstrap the new genome so that the cell eventually contains only protein from the new genome. To me, that represents a step beyond tinkering: it means we can create synthetic lifeforms with exactly and only the details we want, which makes studying them and engineering them far easier.
Interestingly, even though this tech exists, nobody has found any interesting use for it and it's not even really used to probe biology.
A better example would be gene therapy, which has been developing slowly over decades. A single person died in the a trial in the 90s and stopped development (that's the regulation part you're referring to) for decades. In other trials that don't include gene therapy, patients routinely die and they're just a statistic.
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.