kathryn-explorable / swabseq

SwabSeq

SwabSeq allows for detection of SARS-COV-2 genomic RNA, without purification, in a single-step RT-PCR followed by sequencing. This eliminates some of the current bottlenecks to most current COVID sequencing protocols (e.g. rare/expensive reagents, extra purification steps, qPCR), and also utilizes multiplexing to sequence larger batches of samples without extensive automation.

SwabSeq is:

• simple (1 person can run 1-10k samples in a day if in microtiter plates w/ no automation)
• cheap (~$1/sample in consumable costs)
• scalable (~10K samples/day, w/ automation 100k/day or more dependent on thermocycler capacity)
• sensitive (LoD ~1-6 molecules/test) and quantitative (> 3-4 logs)

For more details, including experimental protocols and preliminary analyses please see our Notion Page

• Octant COVID-19 Notion

Getting Started

If you are interested in running the computational portion of SwabSeq, see the wiki links below

• Installation
• Running the Analysis Pipeline
• Barcode Map Spec
• Plate Map Spec

Example Analysis

We've also included an example .Rmd file outlining a typical analysis

• Example Analysis

Contributing

Please feel free to open an issue or pull request.

Authors

This was done largely by two Octant team members, Eric Jones and Aaron Cooper working out of the Octant lab while things were shut down.

• Eric Jones - Project Lead
• Aaron Cooper
• Joshua Bloom
• Nathan Lubock
• Scott Simpkins
• Molly Gasperini
• Sri Kosuri

License

The code contained in this repository is licensed under the Apache 2.0 License - see the LICENSE file for details. Additional licensing information:

• starcode - GPL-3.0

The associated lab protocols etc. found on our Notion Page are provided under the Open Covid Pledge License.

Acknowledgments

The rest of the Octant team has been instrumental in developing this platform over the last several years. In addition, we received a lot of advice on controls to run and how to think about tests from collaborators at UCLA (Jonathan Flint, Leonid Krugylak, Eleazar Eskin, Yi Yin, and Valerie Arboleda), UW (Lea Starita, Jay Shendure, Jase Gehring, Sanjay Srivatsan, Beth Martin), the Broad (Feng Zhang), and UC Berkeley (Fyodor Urnov, Patrick Hsu). We'd also like to thank members of the Covid Scaleup Slack group for helpful discussions.