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Comment by bognition | Hacker News Reader

I kind of hate articles like this. They feed into the myth that single event cause revolutionary changes in the way the world works, when it reality its thousands of evolutionary events that cause change.

I'm not trying to belittle recent advances in genomics, on the contrary I believe that cheap sequencing and technologies like CRISPR are going to change the world. However, its the insinuation that the last 20+ years of hard work were a fraud that upsets me. We wouldn't be were we are now without the last 20 years of genomics research. /rant

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The article kind of says that though, the very beginning of the article explains how it was assumed that after we sequenced a genome, we'd make fast progress. However what's actually starting to make a difference is the sequencing of greater number of genomes (more data), and the improvement of our filtering (identification and algorithms). Basically, the big difference is a lot of hardwork, and refinement. There's not actually any mention of a single breakthrough discovery.

I think it's not only about genomes.

Actually, cells have a lot of state. See [1] for a basic, slightly old, CS-biased explanation.

That's how a single genome yields an incredibly varied repertoire of cell types. We need to work a lot on understanding those epigenomes to then get a chance to pinpoint genetic causes of disease.

[1] http://ds9a.nl/amazing-dna/

Yes, cell state is really important. My view is that nobody is looking at the big picture of state - cells store state in many different ways, with time periods of milliseconds to years. It's probably my CS background, but I think figuring out all the relevant state of a cell is important and neglected. Epigenetics is a part of this, but not enough.

RNAseq, the measure of abundance of transcribed genes at a particular cell state is one of the most common assays done with next gen sequencing. That's the big one then of course there is also miRs, transcription factors, methylation, histone marks, chromatin conformation capture.

As someone with a CS background who has worked almost 5 years in bioinformatics, one thing to realise is if you think something obvious is not done, either you're missing the right search term on pubmed or there's some horrible lab issue that makes it really hard (and we need a new assay/tech)

I think I get where the rant is coming from though. Even though the article isn't talking about a single discovery, it's more the idea that the world will turn one day and it will suddenly be the age of

I feel like the "And this time it's probably real" is kind of a tongue-in-cheek reference to that sensationalism.

It's just a click bait title really.

Exactly. Imagine being a student given a tome of knowledge called something like, 'Principia Mathematica'. Upon receipt of the book you say, "I will soon be able to make engines efficient, structures solid, understand the planet's rotation, and make mathematical models precise." Then two days later someone asks how to draw up plans for a rocket ship to the moon. You'll get there, but you have to read the text first, then you have to understand it, then you have to experiment with it, and then you will (go to the moon). Nothing in your prediction was unrealistic, but it takes time to learn.

A few decades ago ago we read our first line of source code from a computer from the year 2,000,000,000 - some pretty advanced technology that was just handed to us. Two decades ago we did our very first read-through of an entire human. The reading itself was not particularly productive. We already knew some of the most important lines of code, from the decades before, and we annotated a lot more sections that are critical for basic function. We've even traced a few bugs back to the source code - especially those bugs where 1) the source was unusually easy to read [1], or 2) the bug was really bad [2]. As of this year we've developed compilers to enable us to write and run small, multi-line programs of our own composition [3]. We're doing really well in trying to read, comprehend, manipulate, and act on some of the most sophisticated technology ever presented to us. More sophisticated by orders of magnitude than the silicon in Nvidia's new graphics card (16nm 2d dry feature size [4], vs 0.1nm, 3D wet feature size [5]) . Reverse engineering it takes a bit of time, and yet we've already put that knowledge to significant use.

Further, once we do get our working knowledge up and running there will be a significant outpouring of function from having reverse-engineered such technology. But of course there is a lag time in the millions of lab-hours being poured into understanding that technology before we can actually use it. Not dissimilar to the rapid advances in science-fiction when a race reverse-engineers an advanced alien technology. How long did it take us to build The Machine in Sagan's 'Contact'? We will not just cure cancer, but we will be capable of all sorts of new feats. Purchasing the textbook doesn't make one a master - it's the study, the hard work, and the practice.

[1] https://en.wikipedia.org/wiki/Human_papillomavirus

[2] https://en.wikipedia.org/wiki/BRCA_mutation

[3] https://news.ycombinator.com/item?id=11417689

[4] http://wccftech.com/rumor-nvidia-pascal-gtx-1080-gddr5x-gtx-...

[5] https://en.wikipedia.org/wiki/Resolution_%28electron_density...

For anyone curious the idea that we make slow, discrete progress and that the idea that a few brilliant souls are responsible for most of the advances in human hostory is a bunch of hogwash, Matt Ridley has written an excellent book on the subject, The Evolution Of Everything (amazon.com/Evolution-Everything-How-Ideas-Emerge/dp/0062296000). Coincidentally he's also written a fabulous book on the human genome, either way it's great stuff and absolutely critical to understanding how to we can (as a species) progress most effectively.

The idea of lone geniuses single-handedly bringing upon revolutions in science and tech doesn't help, and it's perpetuated by our culture. Nobody thinks of the leagues of engineers or doctoral students or fellow "no-name" professionals who also do a lot of the work. It's really sort of fascinating how we idolatrize singular people who themselves will admit they were standing on the shoulders of giants.

> a tomb of knowledge called something like, 'Principia Mathematica

freudian slip? ;-)

yes, actually :)

> How long did it take us to build The Machine in Asimov's 'Contact'?

Do I need to dig out this story or do you mean Carl Sagan's 'Contact'?

Man, two Freudian slips before coffeetime... I need to better proof my posts. I even went to the wikipedia page to make sure I remember the name of 'The Machine' correctly.

Thank you for your post. The best talk on CRISPR that I ever heard was ~45 minutes about previous genome editing techniques -- TALENs, Zinc finger proteins, etc -- and ~15 minutes about how CRISPR simplifies the genome editing process. It very much paid tribute to past work while also showing the utility of recent developments (while also noting that CRISPR is useless without extensive knowledge from genomics).

Thanks for the rant! I'm with you. Many people probably would not read an article about incremental advances in science. As an economics topic, genomics may be turning into a bigger influence.

For a scientist working incrementally, accumulating data, sieving it for information, fighting for grants, doing administration while making sure family gets three squares, articles like this may cause a wince. But they probably influence support for more funding. Pro scientists I know do not have time to read these articles and will never see them unless it's from someone else, but budding scientists, career-changers, influencers, all get misinformed.

In short, news on science causes long-term damage for short-term gains.

To be fair, there has to some point in time you choose to claim that the "future has arrived". But I agree that great care should be taken to give credit where credit is due; and that means giving credit to all the failures as well, without which successes could never have arisen.

Yes. I'll start a thread to acknowledge the Bioinformatics programmer heroes of genomics, people who write the workhorse programs that biologists use everyday to assemble and analyze the data to come up with the biological results)

BLAST (Stephen Altschul, Warren Gish, Webb Miller, Eugene Myers, and David J. Lipman)

BWA (Heng Li, Richard Durbin)

Samtools (Heng Li)

GATK (from the GATK Team; DePristo, M., Banks, E., Poplin, R., Garimella, K., Maguire, J., Hartl., C., Philippakis, A., del Angel, G., Rivas, M.A, Hanna, M., McKenna, A., Fennell, T. Kernytsky, A., Sivachenko, A, Cibulskis, K., Gabriel, S., Altshuler, D. and Daly, M. A)

I'm missing quite a few from my more niche field; but feel free to add more.

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