Tapestry: A Single-Round Smart Pooling Technique for COVID-19 Testing
Ghosh, S.; Rajwade, A.; Krishna, S.; Gopalkrishnan, N.; Schaus, T. E.; Chakravarthy, A.; Varahan, S.; Appu, V.; Ramakrishnan, R.; Ch, S.; Jindal, M.; Bhupathi, V.; Gupta, A.; Jain, A.; Agarwal, R.; Pathak, S.; Rehan, M. A.; Consul, S.; Gupta, Y.; Gupta, N.; Agarwal, P.; Goyal, R.; Sagar, V.; Ramakrishnan, U.; Krishna, S.; Yin, P.; Palakodeti, D.; Gopalkrishnan, M.
Show abstract
The COVID-19 pandemic has strained testing capabilities worldwide. There is an urgent need to find economical and scalable ways to test more people. We present Tapestry, a novel quantitative nonadaptive pooling scheme to test many samples using only a few tests. The underlying molecular diagnostic test is any real-time RT-PCR diagnostic panel approved for the detection of the SARS-CoV-2 virus. In cases where most samples are negative for the virus, Tapestry accurately identifies the status of each individual sample with a single round of testing in fewer tests than simple two-round pooling. We also present a companion Android application BYOM Smart Testing which guides users through the pipetting steps required to perform the combinatorial pooling. The results of the pooled tests can be fed into the application to recover the status and estimated viral load for each individual sample. NOTE: This protocol has been validated with in vitro experiments that used synthetic RNA and DNA fragments and additionally, its expected behavior has been confirmed using computer simulations. Validation with clinical samples is ongoing. We are looking for clinical collaborators with access to patient samples. Please contact the corresponding author if you wish to validate this protocol on clinical samples.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Nationwide rollout reveals efficacy of epidemic control through digital contact tracing 91%
- Statistical modeling, estimation, and remediation of sample index hopping in multiplexed droplet-based single-cell RNA-seq data 91%
- Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare 91%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.