
Transcriptic for YC biotech startups - pouwerkerk
http://blog.ycombinator.com/transcriptic
======
jkimmel
This is really remarkable.

I've used Transcriptic in a research setting for a while now. From a user's
perspective, it's like looking into the future, and it's awesome.

From the business side, this makes YC a more attractive option for biotech
startups. The life sciences are still very capital intensive. While the New YC
Deal helps in this department, many businesses still need to look toward an
STTR/SBIR grant from the NIH to get to the stage where they have a product to
show investors.

Moves like this probably won't change that for a ton of companies, but there
are a few on the margin who may be able to pursue an idea through YC with the
benefit of the extra $20K in fuel.

~~~
pw
How does Transcriptic stack up in terms of price? Is it prohibitively
expensive for lots of stuff or is it a viable option?

~~~
jkimmel
We find it to be pretty competitive for our needs (PCR, genotyping, long-term
storage).

PCR is something like ~$1.50/rxn with our standard genotyping protocol, which
works out to ~$0.30-0.40 more than the same reaction run in house. If I recall
correctly, the cost per rxn goes down if you run more in parallel, because
they share the same instrument time.

Setting up that reaction might take me ~0.5 hours base, and 0.05 hours for
each subsequent reaction prepared in parallel.

Grad students are cheap, but even valuing my skilled labor at minimum wage,
it's cheaper to use Transcriptic.

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frisco
Hey, I'm the founder of Transcriptic. We're pretty excited about this. Happy
to answer any questions!

~~~
dnautics
The biggest pain point I see in this market is that experimental
parallelization is an intervention-heavy process. Ignoring the equipment
costs, it's also capital-intensive (unlike say deploying to AWS). And finally,
obtaining usable data still requires experiential knowledge. There's something
about _knowing and feeling the data_ (yeah, that's awfully fuzzy) that is
still an important part about obtaining good results [0]. So for any biologic
process that is parallelizing the operators are going to want to own the
machines anyways.

In order to capture a real market, you're going to have to figure out a way to
offer parallelization services - be given a non-parallel experiment with
certain parameters and scale it up on behalf of the users. So, the user has an
experimental plan and just 'hands it over' to transcriptic. I still worry
about the experiential knowledge part, putting the experimenter one step away
from the experiment is potentially counterproductive.

[0][http://onlinelibrary.wiley.com/doi/10.1002/pro.2339/full](http://onlinelibrary.wiley.com/doi/10.1002/pro.2339/full)
In this paper, the grad student (and lead author) who had spent four years of
her graduate work on a previous paper had a nagging feeling that the data were
strange. By actually looking at the wells, she figured out post-publication
(with nothing to gain) that the protein was sticking to sides of the 96-well
plate and causing the observational data to be artefactual. Then there was the
question of how to do more experiments to _prove_ that was going on. And then
the political problem of convincing her grad advisor to publish a retraction
(well at least it was a retraction worth a 10 page paper and a new citation.
The story has a happy ending; she got a position at a pharma company largely
on the back of her due diligence).

~~~
frisco
I think the opposite is actually true: you get better data when you don't feel
the samples. This is a highly unpopular view right now but I have to wonder
what's going on when things like Amgen getting 11% reproducibility of
foundational papers
([http://www.nature.com/nature/journal/v483/n7391/full/483531a...](http://www.nature.com/nature/journal/v483/n7391/full/483531a.html))
are happening. There are things that are hard to automate because of their
mechanical nature, but I think that for the reproducibility of science
somewhat distancing the human from the process is a good thing. Of course,
this adds short-term costs (which may sometimes be unacceptable) and requires
a lot of behavior change for how people are used to working. I will say that
we put a lot of thought into giving you the fidelity of interaction such that
you can still make "breakthroughs from your errors" on Transcriptic, and
improving those reporting capabilities are an ever-ongoing process.

I'll also say that this challenge is bigger than just one company. Some things
may make more sense to do via Science Exchange, for example if the method
requires some very customized hardware or there are only a few experts in the
world who are sufficiently familiar with an unusual method's sensitivities.
I'm also excited to see what Riffyn comes out with to help labs understand
where reproducibility comes from. We're just getting started, but I can't see
a path forward that puts more humans at benches rather than less. The humans
should be free to do real science.

~~~
dnautics
I have exactly the opposite opinion. Even scientists are captiviated by
scientism - the idea that there is something poisonous about human
subjectivity and imprecision and that removing subjectivity (and gathering
more data) is necessarily a good. Sydney Brenner, for example has a 'money
quote' about the path that biologists take: "low input, high throughput, no
output science". Note that this quote doesn't make sense in an environment
that doesn't faddishly flock to high througput 'big data' solutions.

Your example is greatly flawed. The biggest consumers of highly
parallelizeable workflows is the pharmaceutical industry. Highly parallel
medchem was a big fad and the number of drugs that it produced for its efforts
is disappointing. The fact that 11% of Amgen's results are irreproducible is
if anything a condemnation of parallel scaleup, at least in the context of an
operator with a strong motive for selective interpretation.

Another big problem is that when you bring your numbers up, you 'get what you
are looking for'. Precision optimization can optimize for an artefact. I joke
I like to make is that sloppy science is good, because if you keep seeing the
same result under a noisy platform, what you're seeing is probably real and,
more encouragingly, robust.

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pinot
Any interest in working with expression re fermentation down the line? CROs
working specifically with .25-2L tanks are very tricky to find, price and deal
with, and are not local to many biotech firms in the bay. About 25% of my time
is spent just managing our CRO/CMO, and they only have 4x2L tanks. I used to
work for a place with multiple parallel tanks (30+) that made DoEs expedient
and was a great resource - but sadly not something I can tap into at my small
biotech firm.

Combining what you're building with something with a ambr250 or even just a
bunch of Applikon micros or wellplate fermenters could see a lot of action
(though something like the ambr250 would fit your business model better,
robotics > people).

I'll be contacting you for information about FACS, protein quantitation and
cell viability work. Stuff I definitely want to farm out.

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arca_vorago
So someone would ship samples to you guys, then your LIMS/robotic automation
handles data-flow and actual physical work? Where are you guys storing all the
data?

I have two hesitations to point out:

1) I worked in DNA, and the small little issues that cropped up on every major
platform (MiSeq/HiSeq/iontorrent,454,etc) seems like it would make automation
of fixes difficult. I guess if you are keeping a stricter list of reagents,
parameters, etc, then you could help prevent this, but then people aren't
pushing the edge science quite as much.

2) So much data! My systems used to generate over 200gb per day. Good luck
downloading that via any api if you have anything but fiber. Do you intend to
allow computation to be run on data as a cloud service? If so, I can see this
going big places... as long as you allow full control of the VM for all the
bio-hats and their custom wizjangles.

I'm out of the industry and have one year left on my non-compete, but I wish
you the best of luck! Especially on the LIMS integration: a good LIMS is
freaking expensive!

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steejk
This is the first I've heard of Transcriptic, but it sounds amazing.

Initially I was sceptical of YC working with startups which would have more
conventionally come out of universities etc., but this is the sort of
technology that has the ability to completely revolutionise scientific
research.

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corwinbad
Great move Max!

This is Omri (founder of Genomecompiler.com). I'm always amazed about how many
biologists think their work is pipetting small amounts of liquids and
performing massively low productivity experiments rather then their real work
of increasing our understanding of nature and finding solution to real world
problems (like disease, hunger, aging, running out of civilization critical
commodities, etc) using the best available tools.

Robots aren't taking our jobs - they help us be more productive so a biologist
Ph.D. might in the future get paid like a CS undergrad!

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thearn4
Very cool concept! I guess it's also another point to show that the number of
jobs (in this case, lab assistants) that can't be automated away is getting
smaller and smaller.

Just an observation though, I'm not a luddite about this.

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rjayatilleka
Just to let people know, Transcriptic isn't the first startup in this market.
Emerald Cloud Lab is another, and I'm sure there are others.

