Diagnostic Test

UCLA Swab-Seq COVID-19 Diagnostic Test

Swab-Seq is a novel technology developed by UCLA faculty in collaboration with colleagues throughout the country that uses genomic sequencing technology to identify the presence of coronavirus in a sample. The advantage of Swabseq is that a single lab can perform 10s or 100s of thousands of tests at a very low cost.

The coronavirus that causes COVID-19 is an RNA virus. Fortunately, over the past 25 years, there have been tremendous advances in genomic sequencing technology, ever since the start of the Human Genome Project and these advances provide us with tremendously efficient and cost-effective ways to measure RNA. Swabseq directly leverages these technological advances to perform diagnostic testing.

Swab-Seq works by adding a barcode that acts as a unique "sticker" to each sample that is collected. All of the samples are then mixed together and the results are put into a genomic sequencing machine. The resulting mixture contains 10s or 100s of thousands of sequences and the sequence generated contains information from all of these samples. By looking at the barcodes or “stickers” in the resulting sequence, the technology can identify which samples contain evidence of the coronavirus that causes COVID-19.

The development of Swab-Seq technology is a collaboration between the Department of Human Genetics and the Department of Computational Medicine, UCLA Health and Octant.

Full details of the Swab-Seq technology are available in our preprint:

"Swab-Seq: A high-throughput platform for massively scaled up SARS-CoV-2 testing"
doi: https://doi.org/10.1101/2020.08.04.20167874
Joshua S. Bloom, Eric M. Jones, Molly Gasperini, Nathan B. Lubock, Laila Sathe, Chetan Munugala, A. Sina Booeshaghi, Oliver F. Brandenberg,  Longhua Guo, Scott W. Simpkins, Isabella Lin, Nathan LaPierre, Duke Hong, Yi Zhang, Gabriel Oland, Bianca Judy Choe, Sukantha Chandrasekaran, Evann E. Hilt, Manish J. Butte, Robert Damoiseaux, Aaron R. Cooper, Yi Yin, Lior Pachter, Omai B. Garner, Jonathan Flint, Eleazar Eskin, Chongyuan Luo, Sriram Kosuri, Leonid Kruglyak, Valerie A. Arboleda.