Unbiased Fitness Estimation of Pooled Barcode or Amplicon Sequencing Studies

Fangfei Li; Marc L Salit; Sasha F Levy

doi:10.1016/j.cels.2018.09.004

Unbiased Fitness Estimation of Pooled Barcode or Amplicon Sequencing Studies

Cell Syst. 2018 Nov 28;7(5):521-525.e4. doi: 10.1016/j.cels.2018.09.004. Epub 2018 Nov 1.

Authors

Fangfei Li¹, Marc L Salit², Sasha F Levy³

Affiliations

¹ Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.
² Joint Initiative for Metrology in Biology, Stanford, CA 94305, USA; National Institute of Standards and Technology, Gaithersburg, MD 20899, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
³ Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Joint Initiative for Metrology in Biology, Stanford, CA 94305, USA; National Institute of Standards and Technology, Gaithersburg, MD 20899, USA; Department of Genetics, Stanford University, Stanford, CA 94305, USA. Electronic address: [email protected].

Abstract

Standard practice for phenotyping complex cell pools is to measure the fold enrichment of genotype-specific amplicons after a period of competitive growth. Here, we show that fold-enrichment measures cannot be compared across genotype pools with different fitness distributions. We develop a method to calculate an unbiased estimate of relative fitness by tracking abundances over several time points and show how to optimize experimental protocols to minimize fitness measurement error.

Keywords: CRISPR; Fit-Seq; amplicon sequencing; barcode sequencing; deep mutational scanning; experimental bias; fitness; fold enrichment; log-linear regression; pooled growth; reproducibility.

Unbiased Fitness Estimation of Pooled Barcode or Amplicon Sequencing Studies

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