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

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.

SUBMITTER: Gohl DM

PROVIDER: S-EPMC6489363 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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

Gohl Daryl M DM Magli Alessandro A Garbe John J Becker Aaron A Johnson Darrell M DM Anderson Shea S Auch Benjamin B Billstein Bradley B Froehling Elyse E McDevitt Shana L SL Beckman Kenneth B KB

Genome biology 20190429 1

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 ...[more]

PMID: 31036053

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Project description:microRNA (miRNA) expression profiles based on the highly powerful Illumina sequencing technology rely on the construction of cDNA libraries in which adaptor ligation is known to deeply favor some miRNAs over others. This introduces erroneous measurements of the miRNA abundances and relative miRNA quantities in biological samples. Here, by using the commercial miRXplore Universal Reference that contains an equimolar mixture of 963 animal miRNAs and TruSeq or bulged adaptors, we describe a method for correcting ligation biases in expression profiles obtained with standard protocols of cDNA library construction and provide data for quantifying the true miRNA abundances in biological samples. Ligation biases were evaluated at three ratios of miRNA to 3'-adaptor and four numbers of polymerase chain reaction amplification cycles by calculating efficiency captures/correcting factors for each miRNA. We show that ligation biases lead to over- or under-expression covering a 105 amplitude range. We also show that, at each miRNA:3'-adaptor ratio, coefficients of variation (CVs) of efficiency captures calculated over the four number of amplification cycles using sliding windows of 10 values ranged from 0.1 for the miRNAs of high expression to 0.6 for the miRNAs of low expression. Efficiency captures of miRNAs of high and low expression in profiles are therefore differently impacted by the number of amplification cycles. Importantly, we observed that at a given number of amplification cycles, CVs of efficiency captures calculated over the three miRNA:3'-adaptor ratios displayed a steady value of 0.3 +/- 0.05 STD for miRNAs of high and low expression. This allows, at a given number of amplification cycles, accurate comparison of miRNA expression between biological samples over a substantial expression range. Finally we provide tables of correcting factors that allow to measure the abundances of 963 miRNAs in biological samples from TruSeq-based expression profiles and, an example of their use by characterizing miRNAs of the let-7, miR-26, miR-29, and miR-30 families as the more abundant miRNAs of the rat adult cerebellum.

Dataset Information

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

Publications

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

Similar Datasets

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