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I'm the creator of Origami Assays and am happy to answer any questions. Screening for COVID19 is an urgent problem, but the infrastructure for running these as
by pwoolf 7y ago
I'm the creator of Origami Assays and am happy to answer any questions.
Screening for COVID19 is an urgent problem, but the infrastructure for running these assays is limited. While there are important efforts underway to make more tests available, one simple and low cost way to help is to use the assay infrastructure we already have more efficiently.
The idea is that rather than run 1 assay on 1 patient sample, we intelligently pool patients samples and run our limited assays on these pooled samples. It is analogous to data compression, but for assays instead of files.
Nonadaptive pooling designs are well studied branch of applied mathematics and engineering, and are well suited for COVID19 population screening for the following reasons:
(1) Binary assay
(2) Low positive rate
(3) Large number of samples
These three features mean that the data stream coming from COVID19 assays is nicely compressible.
I've put together a series of examples of nonadaptive pooling designs for COVID19 that I'm calling "Origami Assays". These designs provide a few things:
* Concrete examples, with performance metrics for a range of sizes of designs.
* Software infrastructure for decoding designs with error estimates.
* A low cost, DIY paper template system for constructing complex pooling design mixtures by hand.
Advantages:
* Yields up to an 11.9x improvement in patient testing throughput (for the XL3 assay design).
* Can be rolled out immediately, on any existing assay platform (RT-PCR, antibody, LAMP, or qSANGER)
* Pool design can be done by hand without extensive training.
Disadvantages:
* Pooled designs can call false positives if too many positives are present in the population.
* Pooling can dilute samples
* Constructing pooling designs is a mind numbing task for humans.
I'm trying to roll out Origami Assays to all who may benefit from them. Any questions, thoughts, or ideas are welcome!
- tych0 7y agoIsn't the low positive rate kind of an assumption though, because of currently limited testing? If this is much more widespread than we thought, will this mechanism fall over?
- pwoolf 7y agoThe low positive rate is a constraint, but in practice we see that population screens are yielding between 0.5% and 4% positive rate, depending on the sampling population/scenario. There are many use cases where we expect a low positive rate too. For example, an employer screening what appear to be healthy employees. A nice thing about these designs is that if they get overloaded, they call false positives and the decoder can indicate when the design limits are exceeded. In this case, we would need to do a second round of testing for validation--often on a small handful of cases.
- tych0 7y agoOk, basically a Bloom Filter for test results. Pretty cool :)
- pwoolf 7y agoYes, much like a Bloom filter, but instead of 2, it gives 3 output types: 1) not in set 2) possibly in set 3) in set Depending on the input (sample population), it is possible to get results with all 3 states. Origami Assay's decoder differentiates the "(3) in set" from "(2) possibly in set" for efficient post-testing.
- TuringNYC 7y agoFirst, thank you! This is so cool! Question for you -- are these types of methods used in labs already? It seems the false positive issue could be overcome if you just re-test anything that is positive separately, wouldn't that work?
- pwoolf 7y agoWelcome, I enjoy it too. And yes, multiplex designs are used in quite a few lab settings. Quality control in factory settings and all over the place in bioinformatics. And yes, you can overcome the false positives by retesting. The nice thing about nonadaptive designs is that in most cases you don't need to re-test because the right answer just falls out. If you do need to retest, the design flags that too. Yay for math!