Author: Jonathan L Schmid-Burgk; David Li; David Feldman; Mikolaj Slabicki; Jacob Borrajo; Jonathan Strecker; Brian Cleary; Aviv Regev; Feng Zhang
Title: LAMP-Seq: Population-Scale COVID-19 Diagnostics Using a Compressed Barcode Space Document date: 2020_4_8
ID: 68ps3uit_15
Snippet: Interpreting the compressive barcoding problem as a modified Bloom filter (Supplementary Note 1), we predict that when using k = 5 barcodes per sample, requesting k' = 3 barcodes to be detected per sample, and splitting samples into m2 = 10 pools per run, both the false-negative and false-positive rates of detection using a compressed barcode space will be less than 0.2% as long as the global frequency of positive samples is below 1.3% ( Fig. 2A).....
Document: Interpreting the compressive barcoding problem as a modified Bloom filter (Supplementary Note 1), we predict that when using k = 5 barcodes per sample, requesting k' = 3 barcodes to be detected per sample, and splitting samples into m2 = 10 pools per run, both the false-negative and false-positive rates of detection using a compressed barcode space will be less than 0.2% as long as the global frequency of positive samples is below 1.3% ( Fig. 2A) , at an estimated cost of < 7 USD per sample (Fig. 2D) . We must emphasize, however, that the suggested barcoding method
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