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Validated removal of nuclear pseudogenes and sequencing artefacts from mitochondrial metabarcode data.

Authors :
Andújar, Carmelo
Creedy, Thomas J.
Arribas, Paula
López, Heriberto
Salces‐Castellano, Antonia
Pérez‐Delgado, Antonio José
Vogler, Alfried P.
Emerson, Brent C.
Source :
Molecular Ecology Resources. Aug2021, Vol. 21 Issue 6, p1772-1787. 16p.
Publication Year :
2021

Abstract

Metabarcoding of Metazoa using mitochondrial genes may be confounded by both the accumulation of PCR and sequencing artefacts and the co‐amplification of nuclear mitochondrial pseudogenes (NUMTs). The application of read abundance thresholds and denoising methods is efficient in reducing noise accompanying authentic mitochondrial amplicon sequence variants (ASVs). However, these procedures do not fully account for the complex nature of concomitant sequences and the highly variable DNA contribution of specimens in a metabarcoding sample. We propose, as a complement to denoising, the metabarcoding Multidimensional Abundance Threshold Evaluation (metaMATE) framework, a novel approach that allows comprehensive examination of multiple dimensions of abundance filtering and the evaluation of the prevalence of unwanted concomitant sequences in denoised metabarcoding datasets. metaMATE requires a denoised set of ASVs as input, and designates a subset of ASVs as being either authentic (mitochondrial DNA haplotypes) or nonauthentic ASVs (NUMTs and erroneous sequences) by comparison to external reference data and by analysing nucleotide substitution patterns. metaMATE (i) facilitates the application of read abundance filtering strategies, which are structured with regard to sequence library and phylogeny and applied for a range of increasing abundance threshold values, and (ii) evaluates their performance by quantifying the prevalence of nonauthentic ASVs and the collateral effects on the removal of authentic ASVs. The output from metaMATE facilitates decision‐making about required filtering stringency and can be used to improve the reliability of intraspecific genetic information derived from metabarcode data. The framework is implemented in the metaMATE software (available at https://github.com/tjcreedy/metamate). see also the Perspective by Natalie R. Graham, Rosemary G. Gillespie and Henrik Krehenwinkel [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1755098X
Volume :
21
Issue :
6
Database :
Academic Search Index
Journal :
Molecular Ecology Resources
Publication Type :
Academic Journal
Accession number :
151470636
Full Text :
https://doi.org/10.1111/1755-0998.13337