MAUI-seq: Metabarcoding using amplicons with unique molecular identifiers to improve error correction.

Bryden Fields,Stig U Andersen,J Peter W Young,Ville‐Petri Friman,Sara Moeskjær

doi:10.1111/1755-0998.13294

Bryden Fields, Stig U Andersen + Show 3 more

Open Access

https://doi.org/10.1111/1755-0998.13294

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Abstract

Sequencing and PCR errors are a major challenge when characterizing genetic diversity using high-throughput amplicon sequencing (HTAS). We have developed a multiplexed HTAS method, MAUI-seq, which uses unique molecular identifiers (UMIs) to improve error correction by exploiting variation among sequences associated with a single UMI. Erroneous sequences are recognized because, across the data set, they are over-represented among the minor sequences associated with UMIs. We show that two main advantages of this approach are efficient elimination of chimeric and other erroneous reads, outperforming dada2 and unoise3, and the ability to confidently recognize genuine alleles that are present at low abundance or resemble chimeras. The method provides sensitive and flexible profiling of diversity and is readily adaptable to most HTAS applications, including microbial 16S rRNA profiling and metabarcoding of environmental DNA.

Highlights

The evaluation of DNA diversity in environmental samples has become a pivotal approach in microbial ecology [1] and is increasingly used to assess the distribution of larger organisms [2]
We have developed a multiplexed highthroughput amplicon sequencing (HTAS) method, MAUI-seq, which uses unique molecular identifiers (UMIs) to improve error correction by exploiting variation among sequences associated with a single UMI
We show that two main advantages of this approach are efficient elimination of chimeric and other erroneous reads, outperforming DADA2 and UNOISE3, and the ability to confidently recognise genuine alleles that are present at low abundance or resemble chimeras

Summary

Results

We have developed a multiplexed HTAS method, MAUI-seq, which uses unique molecular identifiers (UMIs) to improve error correction by exploiting variation among sequences associated with a single UMI. We show that two main advantages of this approach are efficient elimination of chimeric and other erroneous reads, outperforming DADA2 and UNOISE3, and the ability to confidently recognise genuine alleles that are present at low abundance or resemble chimeras

Introduction

Discussion

Materials And Methods

Availability of supporting data