BillionToOne | Staff Software Engineer | SF Bay Area | Hybrid or Remote
BillionToOne has developed a DNA molecular counter that increases cfDNA diagnostic resolution by over 1,000x. BillionToOne's first product, UNITY, is the first and only non-invasive prenatal test that directly screens an unborn baby for the most common and severe genetic disorders using only a single tube of blood from the pregnant mother without the invasiveness of amniocentesis.
BillionToOne is ranked at the top 5% of Y Combinator companies and has raised $300M+ in funding from prominent VC firms.
We are hiring a Staff Software Engineer to scale up compute-intensive bioinformatics workflows and build infrastructure tools that enable scientists, bioinformaticians and other technical teams at BillionToOne to robustly write and deploy code.
Tools we use include: python, Django, AWS, Terraform, Sentry, and Datadog.
BillionToOne (YCS17) | Staff/Senior Software Engineer | Remote (US) or Onsite (Menlo Park CA) | www.billiontoone.com
BillionToOne develops innovative diagnostics tests that can affect the lives of millions of patients. Our QCT technology platform improves the resolution of cell-free DNA testing by >1000x fold and enables novel tests for both prenatal and oncology care. We've raised over $30M and have launched multiple clinical products in the past 2 years, including an FDA authorized COVID-19 test.
We are hiring a senior engineer (5+ years experience) to build internal APIs, bioinformatics procecessing pipelines, laboratory automation tools, and help manage CI/CD. We use python, django, rabbitmq, circleci, dbt, postgres, heroku, aws, and a variety of other tools.
If you have experience in full stack web development, love seeing your work positively affect your colleagues, and thrive in a fast-paced entrepreneurial environment, this could be a great opportunity for you.
I completely agree with the point about integrated REPL/IDE, and wanted to share some of the combinations I have used in the past, since it can be a concrete getting started point for those who are curious. Some of these are not literally repls, but IMO give a similar experience.
- ClojureScript with Figwheel and the web browser
- Clojure with Emacs Cider, Clojure with Cursive
- R and Rstudio
- Matlab
- ipython jupyter notebook
- Pycharm debug breakpoints that are triggered by unittests (Running the unittest to initiate a python repl at the breakpoint)
A turn around time of 24-36 hours should be easily achievable for the performing lab, depending on the time of day the specimen arrives (morning vs afternoon). It takes about 1 hour for the initial RT-PCR amplification, and sequencing takes about 12 hours.
Our technique would still be affected by shortages in specimen collection (like swabs).
Purely speculative, but I think if swabs remain an issue for too long, alternatives could start coming online, such as even using qtips + saline (no idea if it works, it's just an example). The current swab + Universal / Viral Transport Medium combo is optimized for flexibility; it is designed to work across a very broad range of viruses and bacteria that have different viral loads and shedding characteristics. The current pandemic is pretty much COVID-19 only, so I think it's a priori feasible that a specimen collection procedure can be found that uses common materials. We did try early on to see if saline or other buffers affected the performance of the assay, and it worked fine in those conditions.
We use fairly standard chemicals. I haven't heard from our suppliers about shortages for the chemicals we use. Chemicals and enzymes tend to be relatively fast to scale up for bulk manufacturing.
There's always manufacturing risk that a product will not work as expected. In fact, the first COVID-19 test developed by the CDC did not work as expected, and this delayed testing by several weeks. We de-risk this as much as possible by performing experiments as early as possible, akin to the fail fast mentality of checking for the highest risk failure modes first. Since we don't have a national healthcare system in the US, the manufacturer takes on the vast majority of the risk of a defective product.
My guess is that self-swabbing is allowed in the Seattle SCAN study because it is a research study. The SCAN study is super fascinating because it would be crazy unusual under normal times for a research study to return results back to patients; I'm very happy they are able to do that, and it speaks to the severity of this pandemic.
Thanks for the question! The goal of skipping RNA extraction is to decrease the amount of labor necessary for processing samples and also to eliminate a dependency on RNA extraction reagents that have recently become difficult to find. The FDA is very strict about the specific brand and model of kit you use, so showing that you can swap out one RNA kit for another is actually very useful because you will have alleviated some of the supply chain strain (although I agree at high enough load both supply chains will then become limiting).
The way currently available COVID-19 testing works is by detection of viral RNA. Since the amount of viral RNA in a patient sample is too low to detect directly, we first need to amplify it by PCR. However, this viral RNA is packaged within all sorts of proteins and lipids that could make it inaccessible to amplification unless they are first purified away. Furthermore, the sample is shipped in "viral transport medium", which is essentially a cocktail of chemicals designed to preserve the virus. Unfortunately, these preservatives often have the side effect of interfering with PCR amplification, so these too need to be purified from the sample.
However, since RNA extraction is usually the most laborious part of the assay, there has been a lot of interest in optimizing the amplification so that it is resilient to all of these impurities. The preprint referenced in our manuscript (https://www.biorxiv.org/content/10.1101/2020.03.20.001008v1) gave us the initial idea that this could be possible, and much of it comes down to the choice of amplification method (e.g. choice of enzymes and buffers) that you choose.
However, even when you choose a "good" enzyme and buffer, you will still suffer an amplification penalty, and this will cause you to return a false-negative on some affected samples because there was so little virus in the sample to begin with. The innovation we have is to spike-in a correspondingly low level of DNA to the reaction mixture. That way, if you see the low level of DNA without seeing any viral signal, you can be assured that the amplification still worked and that there truly is no virus in the sample.
The Abbott machines are point-of-care devices that typically sit in doctor's offices. One really interesting use case I've heard of for the Abbott machines is to test all OB patients who are coming in to the clinic for routine care to make sure that they are COVID-19 negative. This allows the clinical staff to conserve PPE and use less burdensome precautions.
I think that where the Abbott machines might hit a wall is that they are one at a time, and they require Abbott's consumable test cartridge and device to run (think printer ink / printer). I don't have any firsthand knowledge, but I would anticipate that it is difficult to scale-up manufacturing of the devices rapidly enough to keep pace with the pandemic growth.
We absolutely need contact tracing to find everybody who needs to be tested. We're not working on scaling up contact tracing, but I think several people in the tech community are working on making that easier to perform at scale.
"When you’re fundraising, it’s AI
When you’re hiring, it’s ML
When you’re implementing, it’s linear regression"
The core of our machine learning is Ax=b :grins:
More seriously, the main reason why traditional sanger sequencing can't be used for COVID-19 testing is because it would be unclear whether a lack of signal is truly due to lack of virus, or if it is just because the assay failed (happens all the time!)
What we've done is introduce a reference sequencing signal that is biochemically very similar to viral RNA, but produces a distinct vector of electrical signals that is different from the signals emitted by viral RNA. Since we know what both the reference and viral signals look like, we can perform linear regression analysis to fit the linear combination of viral and reference signals that best match our data.
3) is almost right--these sanger sequencing instruments are already widely available across the country for research use. Here in the Bay Area, I can choose from at least 4 different Sanger sequencing services that will run 10,000 samples at $2/sample in 24 hours. For example, see: https://www.mclab.com/DNA-Sequencing-Services.html
Sample collection and accessioning (accessioning is unpacking test tubes one by one and aliquoting them into plates in the lab) is definitely going to require a lot of manpower. I'm hopeful that patients "self swabbing" can help alleviate some of the manpower needs. (Self-swabs are not allowed currently under FDA guidance).
This is a test to see if someone has a current COVID-19 infection. The antibody tests (serological) tests are also important, but since it is estimated that only ~1% of the US has previously contracted COVID-19, it will be a while before serological testing becomes useful at a population level.
Our initial data show no false-positives and no false-negatives out of all specimens assayed. However, it is early days still and none of the leading tests have real-world data on false-positive and false-negative rates. The crucial parameter here to compare test performance is limit of detection (LOD). We showed we could detect as few as 10 molecules of virus, which is on par with the best RT-qPCR tests.
Cost is definitely an important consideration for roll-out of a widespread test. We anticipate that the cost will be about $15 per test.
I'm the co-founder and CTO at BillionToOne. I'm happy to answer any questions here. I've also posted a slightly more technical explanation of how the test works and why it can scale here: https://twitter.com/dtsao/status/1247642005510873088?s=21
Edit: Since our site seems to be overwhelmed at the moment, here's a recap:
We’ve been working hard at BillionToOne on a new COVID-19 test that scales testing to everyone in the US. Our test (1) re-purposes existing infrastructure, (2) eliminates time-consuming RNA extraction, and (3) enables a distributed system for COVID-19 testing.
The first thing we figured out is how to run COVID-19 tests on existing automated Sanger sequencers. One sequencer can process up to 3840 samples per day. There are hundreds of sequencers of excess capacity because they were built for the Human Genome Project over 20 years ago.
It would take only 2 sequencers to surpass the current test capacity for all of California. There are far more than 2 sequencers in California (some individual labs have 10 or more).
We tweaked the protocol so COVID-19 could be detected from sequencing data using linear regression. Basically, we add ~100 copies of a known DNA sequence to help us calculate how much virus nucleic acid is in the specimen. It works just as well as gold-standard RT-qPCR.
Lab workflow for COVID-19 testing is traditionally 1. Specimen accessioning, 2. RNA extraction, 3. RT-qPCR 4. Reporting. RNA extraction, in particular, has been a huge bottleneck in terms of reagent shortages and labor-intensiveness.
We showed that we can skip RNA extraction entirely without affecting test sensitivity and limit of detection.
By skipping RNA extraction and using automated Sanger sequencers, we think we can get to an additional 200,000 samples per day test capacity in existing clinical labs.
A distributed system is often the only way to operate at massive scale. A fully distributed system could have different sites and labs responsible for each process and dynamically re-allocate resources based on availability and capacity.
The Broad institute COVID-19 lab has already started doing this. They are asking for specimens to be submitted in a standardized tube format and pre-barcoded. They have essentially distributed the specimen accessioning work.
Because there is a highly developed service industry for Sanger sequencing with <24 hour turnaround, there is an opportunity to further scale up testing by distributing the work to their (currently) idle sequencers.
Distributed testing could scale from 200k to >1 million tests per day, but would require a change in regulations that currently prohibit it.
Thanks to the BillionToOne team for pulling this work together! Next step is to start manufacturing test kits and obtain Emergency Use Authorization from the FDA. We’re eager to work with clinical Lab Directors and contract kit manufacturers.
BillionToOne has developed a DNA molecular counter that increases cfDNA diagnostic resolution by over 1,000x. BillionToOne's first product, UNITY, is the first and only non-invasive prenatal test that directly screens an unborn baby for the most common and severe genetic disorders using only a single tube of blood from the pregnant mother without the invasiveness of amniocentesis.
BillionToOne is ranked at the top 5% of Y Combinator companies and has raised $300M+ in funding from prominent VC firms.
We are hiring a Staff Software Engineer to scale up compute-intensive bioinformatics workflows and build infrastructure tools that enable scientists, bioinformaticians and other technical teams at BillionToOne to robustly write and deploy code.
Tools we use include: python, Django, AWS, Terraform, Sentry, and Datadog.
Apply here: https://boards.greenhouse.io/billiontoone/jobs/4203247005