Measuring sequencer size bias using REcount: A novel method for highly accurate Illumina sequencing-based quantification

Daryl M. Gohl; Alessandro Magli; John Garbe; Aaron Becker; Darrell M. Johnson; Shea Anderson; Benjamin Auch; Bradley Billstein; Elyse Froehling; Shana L. McDevitt; Kenneth B. Beckman

doi:10.1186/s13059-019-1691-6

Measuring sequencer size bias using REcount: A novel method for highly accurate Illumina sequencing-based quantification

Daryl M. Gohl, Alessandro Magli, John Garbe, Aaron Becker, Darrell M. Johnson, Shea Anderson, Benjamin Auch, Bradley Billstein, Elyse Froehling, Shana L. McDevitt, Kenneth B. Beckman

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

Quantification of DNA sequence tags from engineered constructs such as plasmids, transposons, or other transgenes underlies many functional genomics measurements. Typically, such measurements rely on PCR followed by next-generation sequencing. However, PCR amplification can introduce significant quantitative error. We describe REcount, a novel PCR-free direct counting method. Comparing measurements of defined plasmid pools to droplet digital PCR data demonstrates that REcount is highly accurate and reproducible. We use REcount to provide new insights into clustering biases due to molecule length across different Illumina sequencers and illustrate the impacts on interpretation of next-generation sequencing data and the economics of data generation.

Original language	English (US)
Article number	85
Journal	Genome biology
Volume	20
Issue number	1
DOIs	https://doi.org/10.1186/s13059-019-1691-6
State	Published - Apr 29 2019

Bibliographical note

Funding Information:
This work was supported by a grant from the University of Minnesota-Mayo Translational Product Development Fund to D.M.G. and K.B.B. (National Center for Advancing Translational Sciences of the National Institutes of Health Award Number UL1TR000114). The Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley was supported by an NIH S10 OD018174 Instrumentation Grant.

Publisher Copyright:
© 2019 The Author(s).

Keywords

ATAC-Seq
DNA library preparation
Genotyping by sequencing
Illumina
Next-generation sequencing
PCR-free
RAD-Seq
RNA-Seq
Size bias

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10.1186/s13059-019-1691-6

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Measuring sequencer size bias using REcount : A novel method for highly accurate Illumina sequencing-based quantification. / Gohl, Daryl M.; Magli, Alessandro; Garbe, John; Becker, Aaron; Johnson, Darrell M.; Anderson, Shea; Auch, Benjamin; Billstein, Bradley; Froehling, Elyse; McDevitt, Shana L.; Beckman, Kenneth B.

In: Genome biology, Vol. 20, No. 1, 85, 29.04.2019.

Research output: Contribution to journal › Article › peer-review

Gohl, Daryl M. ; Magli, Alessandro ; Garbe, John ; Becker, Aaron ; Johnson, Darrell M. ; Anderson, Shea ; Auch, Benjamin ; Billstein, Bradley ; Froehling, Elyse ; McDevitt, Shana L. ; Beckman, Kenneth B. / Measuring sequencer size bias using REcount : A novel method for highly accurate Illumina sequencing-based quantification. In: Genome biology. 2019 ; Vol. 20, No. 1.

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abstract = "Quantification of DNA sequence tags from engineered constructs such as plasmids, transposons, or other transgenes underlies many functional genomics measurements. Typically, such measurements rely on PCR followed by next-generation sequencing. However, PCR amplification can introduce significant quantitative error. We describe REcount, a novel PCR-free direct counting method. Comparing measurements of defined plasmid pools to droplet digital PCR data demonstrates that REcount is highly accurate and reproducible. We use REcount to provide new insights into clustering biases due to molecule length across different Illumina sequencers and illustrate the impacts on interpretation of next-generation sequencing data and the economics of data generation.",

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AB - Quantification of DNA sequence tags from engineered constructs such as plasmids, transposons, or other transgenes underlies many functional genomics measurements. Typically, such measurements rely on PCR followed by next-generation sequencing. However, PCR amplification can introduce significant quantitative error. We describe REcount, a novel PCR-free direct counting method. Comparing measurements of defined plasmid pools to droplet digital PCR data demonstrates that REcount is highly accurate and reproducible. We use REcount to provide new insights into clustering biases due to molecule length across different Illumina sequencers and illustrate the impacts on interpretation of next-generation sequencing data and the economics of data generation.

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Measuring sequencer size bias using REcount: A novel method for highly accurate Illumina sequencing-based quantification

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