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

Abstract

1. Metabarcoding of Metazoa using mitochondrial genes is confounded by the co-amplification of mitochondrial pseudogenes (NUMTs). Current denoising protocols have been designed to remove PCR and sequencing artefacts, but pseudogenes are not usually recognised by these procedures.Authentic mitochondrial amplicon sequence variants (ASVs), which represent the majority of reads, can be distinguished from PCR-derived errors, sequencing errors and NUMTs (non-authentic ASVs) due to their lower abundances. However, the use of simple read abundance thresholds is complicated by the highly variable DNA contribution of individuals in a metabarcoding sample.2. We show how ASVs that survive standard denoising, but are identified as non-authentic, are consistent with expectations for NUMTs with regard to patterns of phylogenetic relatedness, readabundance, and library co-occurrence. We then propose and demonstrate a new self-validating framework, named NUMT dumping, which allows NUMT filtering strategies to be evaluated by quantifying (i) the prevalence of non-authentic ASVs (NUMT and erroneous sequences) and (ii) the collateral effects on the removal of authentic ASVs (mtDNA haplotypes) in filtered data. We propose several filtering strategies within the NUMT dumping framework, based on the application of read-abundance thresholds, structured with regard to sequence library and phylogeny.3. The framework was validated using mock and natural communities, both of which showed opposing trends for the removal of authentic and non-authentic ASVs, when threshold values for minimum abundance to filter out sequences were increased. Filtering can be optimized to retain less than 5% of non-authentic ASVs while retaining more than 89% of authentic mitochondrial ASVs, or complete removal of non-authentic ASV with 77% of authentic mitochondrial ASVs retained. 4. We provide a program, NUMTdumper, that can be used to evaluate and decide upon the most adequate metabarcoding filtering strategy for specific research objectives, providing a measure of expected prevalence of non-authentic ASVs in metabarcoding datasets. In addition, this evaluation allows the user to quantify effects of taxonomic inflation when ASVs are clustered into OTUs. It improves the reliability of intraspecific genetic information derived from metabarcode data, opening the door for community-level genetic analyses requiring haplotype-level resolution.

The limited spatial scale of dispersal in soil arthropods revealed with whole‐community haplotype‐level metabarcoding

“…Haplotypes from each region were further filtered to remove likely nuclear mitochondrial (numts) pseudogenes, following a protocol based on the relative abundance of codistributed reads (Andújar et al., 2020). The set of putative haplotypes for Acari, Collembola and Coleoptera was used to generate a community table with read counts (haplotype abundance) by sample against the complete collection of reads (i.e.…”

“…The methodology involves the bulk sequencing of mixed communities and subsequent clustering of DNA reads into operational taxonomic units (OTUs) that broadly represent the species category. While an efficient method to approximate community profiles at the species level, precise removal of primary DNA reads affected by sequencing errors (Andújar, Arribas, Yu, Vogler, & Emerson, 2018; Elbrecht, Vamos, Steinke, & Leese, 2018; Turon, Antich, Palacín, Præbel, & Wangensteen, 2019) and co‐amplified nuclear mitochondrial copies (numts; Andújar et al., 2020) would avert the need for clustering. Read‐based data raise the prospect of reliable haplotype information from mitochondrial COI cMBC, which represents a step change for the study of diversity patterns through whole‐community genetic analyses at haplotype‐level resolution.…”

MetaWorks: A flexible, scalable bioinformatic pipeline for high-throughput multi-marker biodiversity assessments

“…Another python package called ‘Alfie’ calculates k-mer frequencies and classifies COI metabarcode sequences to the kingdom rank using a machine learning method [56]. A new program, called NUMTdumper, has been developed as a stand-alone program meant to be incorporated into bioinformatic pipelines [57]. NUMTdumper provides a method to screen for NuMTs based on read counts while acknowledging the trade-offs between removing all possible NuMTs while erroneously removing genuine reads.…”

Revealing community assembly through barcoding: Mediterranean butterflies and dispersal variation

“…Baselga et al., 2013, 2015; Craft et al., 2010; Gómez‐Rodríguez et al., 2019; Múrria et al., 2015; Papadopoulou et al., 2011; Salces‐Castellano et al., 2020; Scalercio et al., 2020). Together with advances in both (a) the generation of community‐level metabarcode data and (b) the recovery of reliable intraspecific sequence variation (Andújar et al, 2020; Elbrecht, Vamos, Steinke, & Leese, 2018; Turon, Antich, Palacín, Præbel, & Wangensteen, 2019), the logistical constraints for site‐based community barcoding are greatly reduced, even for species‐rich and hyperdiverse assemblages (Arribas, Andújar, Salces‐Castellano, Emerson, & Vogler, 2020). Site‐based community metabarcoding thus presents itself as an exciting opportunity, particularly given the potential for revealing otherwise hidden patterns, as exemplified by Scalercio et al.…”