
When Software Eats Bio - srunni
http://a16z.com/2015/11/18/bio-fund/
======
noipv4
One area that's ripe for disruption is Next Generation Sequencing (a high
throughput derivative of Sanger sequencing)

Illumina produces devices called sequencers, which are glorified microfluidics
+ imaging devices, which uses base pair chemistry to build the dna
complementary strands of single stranded dna fragments one base by one using
special dna bases that have washable fluorescent die and are blocking (so that
the complementary strand grows by one base). Once the correct dna base is
stuck to the single strands on the glass device, a picture of the glass device
is taken by shining laser and using a CCD imager. The Nature of DNA makes sure
that the correct complementary base gets stuck. 4 TIFF files are generated
with billions of tiny dots one for each base A,C,G and T. The fluorescent die
is washed, and the last added ddna base is deblocked and the cycle repeats.
The whole software suite (image processing of TIFF files) and hardware specs
(fluid chemistry, etc) is unfortunately closed source :(

~~~
aroch
The next frontier in NGS is long read SMRT (Single Molecule Real Time -
PacBio) combined with Oxford Nanopore's technology. That is, very long reads
combined with massively parallel transcription that doesn't rely on light
emission. Illumina-based tech is not going to be part of the equation.

Illumina is really only related to traditional Sanger sequencing in that its
sequencing by synthesis.

~~~
adenadel
There is no way that this is going to be the case. It's much more likely to be
a combination of Illumina and ONT or ONT alone. PacBio is far too expensive
and low throughput to be a challenger outside of niche applications (like
genome assembly).

~~~
aroch
I would much rather have ONT backed by Pacbio than ONT backed by Illumina. ONT
is well suited for doing WES quickly, with better fidelity than Illumina and
PacBio is much better for supplemental data to do genome assembly using reads
from a short read system

~~~
adenadel
What evidence do you have for ONT having better fidelity than Illumina in any
application? Illumina's error rates are typically <= 1% while on "good" reads
ONT has error rates cited at 15% and overall is even worse[1]. I agree that
PacBio is better for genome assembly, but for many cases we do not need to do
assembly. Resequencing workflows with alignment and variant calling are pretty
good.

1\.
[http://www.sciencedirect.com/science/article/pii/S2214753515...](http://www.sciencedirect.com/science/article/pii/S2214753515000224)

------
Tossrock
Disappointed no one brought up the regulatory hurdles involved when discussing
the resistance of the industry to disruption. When the cost to bring a new
drug to market is over 2 billion dollars [1], it's hard to see start-ups
competing in that environment.

1: [http://www.scientificamerican.com/article/cost-to-develop-
ne...](http://www.scientificamerican.com/article/cost-to-develop-new-
pharmaceutical-drug-now-exceeds-2-5b/)

~~~
adenadel
Considering that a16z said that they are focusing on software around biotech
and not on traditional biotech I think it makes sense that they aren't talking
about regulatory hurdles or the cost to bring a drug to market. Eroom's law is
a serious problem, and it is hard to imagine a disruptive startup that could
compete in that environment.

------
verta
It sure is an interesting time for melding computational advancements with
medical use cases. The reason that the cost decay can promulgate the
previously missing disruption seems to be missing the bigger question of
regulation in these fields.

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raverbashing
The real question is how they will filter for Theanos like companies

~~~
epistasis
Theranos was transparent about the overhyping from the very beginning to
somebody with an understanding of bio. It's more shocking that so much of the
"tech" media bought it.

Imagine a bunch of old-school bankers or Fortune 500 CEOs from the 90s trying
to pick a winning software business. They know "business" so they should be
able to apply their knowledge to the software world easily, would be their
reasoning. Silicon Valley's picks in biotech, like Theranos, have fairly
transparently misapplied "knowledge" in the same way. They look for all the
wrong things, attributes that work well in software but not other places: 1)
confidence bordering on arrogance, 2) dismissal of experts and standard
knowledge disguised as an attempt to "innovate.", and 3) a chance to "hop on"
to a rising founder/CEO before they've proven themselves and become super
expensive. Similarly, be skeptical of other SV health efforts (such as
Google's) unless there are real scientists behind it and also real experience
in biotech (not tech).

Complete Genomics (next to LinkedIn and Google) is an example of what happens
to a biotech company that has really great tech, but runs the business like a
tech company rather than a biotech company. Scale goes completely sideways,
they miss the market, and get sold off to a bigger fish and most likely will
languish.

------
lquist
Can somebody talk about CRISPR and particularly business ideas around it? I
read the recent New Yorker article
([http://www.newyorker.com/magazine/2015/11/16/the-gene-
hacker...](http://www.newyorker.com/magazine/2015/11/16/the-gene-hackers)),
but still have a lot of questions!

~~~
srunni
[http://www.technologyreview.com/news/543181/crispr-gene-
edit...](http://www.technologyreview.com/news/543181/crispr-gene-editing-to-
be-tested-on-people-by-2017-says-editas/)

