Tapestry Infographics


The BYOM smart testing Android app can be installed from the link given below.

Note: If the app does not install, activate "Developer mode" in the Android device before installation

The code developed for the Tapestry project is available in Github. The link to access the github repository is given below.


Tapestry: A Single-Round Smart Pooling Technique for COVID-19 Testing

Author information: Sabyasachi Ghosh, Ajit Rajwade, Srikar Krishna, Nikhil Gopalkrishnan, Thomas E. Schaus, Anirudh Chakravarthy, Sriram Varahan, Vidhya Appu, Raunak Ramakrishnan, Shashank Ch, Mohit Jindal, Vadhir Bhupathi, Aditya Gupta, Abhinav Jain, Rishi Agarwal, Shreya Pathak, Mohammed Ali Rehan, Sarthak Consul, Yash Gupta, Nimay Gupta, Pratyush Agarwal, Ritika Goyal, Vinay Sagar, Uma Ramakrishnan, Sandeep Krishna, Peng Yin, Dasaradhi Palakodeti, Manoj Gopalkrishnan

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.

DOI: 10.1101/2020.04.23.20077727

Link to the medrxiv publication

A Compressed Sensing Approach to Group-testing for COVID-19 Detection

Author information: Sabyasachi Ghosh, Rishi Agarwal, Mohammad Ali Rehan, Shreya Pathak, Pratyush Agrawal, Yash Gupta, Sarthak Consul, Nimay Gupta, Ritika Goyal, Ajit Rajwade, Manoj Gopalkrishnan

We propose Tapestry, a novel approach to pooled testing with application to COVID-19 testing with quantitative Polymerase Chain Reaction (PCR) that can result in shorter testing time and conservation of reagents and testing kits. Tapestry combines ideas from compressed sensing and combinatorial group testing with a novel noise model for PCR. Unlike Boolean group testing algorithms, the input is a quantitative readout from each test, and the output is a list of viral loads for each sample. While other pooling techniques require a second confirmatory assay, Tapestry obtains individual sample-level results in a single round of testing. When testing n samples with t tests, as many as k=O(t*logn) infected samples can be identified at clinically-acceptable false positive and false negative rates. This makes Tapestry viable even at prevalence rates as high as 10%. Tapestry has been validated in simulations as well as in wet lab experiments with oligomers. Clinical trials with Covid-19 samples are underway. An accompanying Android application Byom Smart Testing which makes the Tapestry protocol straightforward to implement in testing centres is available for free download.

Link to the arxiv publication

Related works

Origami Assays (Peter Woolf)*:

Presents multiplexed assay designs with detailed instructions on how the assay works.

Efficient high throughput SARS-CoV-2 testing to detect asymptomatic carriers (Shental et al.)

Study a non-adaptive group-testing approach (P-BEST): Pool 384 patient samples (1-5 positive carriers) into 48 pools and correctly identify positive carriers.

Rapid, Large-Scale, and Effective Detection of COVID-19 via Non-Adaptive Testing (Täufer)

Discuss shifted transversal designs for combinatoric group testing and quantify error bounds on number of false positives for a given design.

Everybody in the Pool: Researchers Use Algorithms to Tackle the Coronavirus Test Shortage

An interview with Dror Baron at NCSU on efforts by engineering and science community for developing pooled testing algorithms.

Analysis and Applications of Adaptive Group Testing Methods for COVID-19 (Mentus et al.)

Propose non-adaptive and adaptive group testing strategies based on generalized binary splitting (GBS), with a focus on vulnerable confined populations such as nursing homes, prisons and ships.

Pooled samples staff monitoring in Covid-19 care areas as part of Triple lock - starve the virus (UK DHSC)

A proposal for pooled testing strategy for health care workers once in 3 days in affected areas.

Pooling of samples for testing for SARS-CoV-2 in asymptomatic people (Lohse et al.)

Testing strategy for pooling of samples before RT-PCR amplification. The results show that up to 30 samples are detectable in under 30 Ct.

PCR Pooling

Provides a software tool to enable COVID-19 pool testing by offering group testing designs and decoding with improvement in throughput by upto 8x RT-PCR tests.

Testing pooled samples for COVID-19 helps Stanford researchers track early viral spread in Bay Area

Press release on the tracking of virus during early days of pandemic by pooling samples in the San Fransisco Bay area by researchers at Stanford School of Medicine.

*The FAQ page by Peter Woolf is a good resource for more information on pooled testing

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Corresponding author:

Manoj Gopalkrishnan

IIT Bombay