DOI: 10.5061/dryad.tmpg4f4xr
title: Datasets from: Validated removal of nuclear pseudogenes and sequencing artefacts from mitochondrial metabarcode

description: 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 (mtDNA
 haplotypes) or non-authentic 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 non-authentic 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).
description: To ensure uniform treatment of datasets, raw sequence reads were
 re-processed following a uniform protocol including primer removal, paired
 end merging, quality filtering, length filtering for reads ranging between
 416-420 bp (the expected 418 bp amplicon ± 2 bp), followed by denoising
 library by library using UNOISE 3 in USEARCH v11 (Edgar, 2016). The last
 step in USEARCH included chimera filtering, dereplication, and removal of
 all singleton reads which were not considered further.
 Steps followed are in file "Standard processing from Raw
 reads to ASVs.sh" The sequences surviving the
 cleaning and denoising steps (hereafter ASVs) were classified to order or
 superfamily level, and only ASVs classified as the target taxon or taxa
 were retained: Apoidea (BEE dataset), Coleoptera (COL) and Coleoptera,
 Acari and Collembola (CAC). These correspond to
 datasets: "COL_Dataset. ASVs Coleoptera from
 Tenerife.fas" "CAC_Dataset. ASVs Coleoptera
 Collembola Acari from Grazalema.fasta"
 "BEE_Dataset. ASVs Apoidea from Mock
 communities.fasta" To perform classification of
 ASVs, we generated a reference database comprised of the NCBI
 <i>nt</i> database (downloaded 17 June, 2018) combined with
 either (i) 1,011 additional reference COI sequences from Coleoptera, Acari
 and Collembola specimens collected in the Canary Islands and Sierra de
 Grazalema (COL and CAC datasets) or (ii) the BEEEE reference database
 described in Creedy et al 2019. For each of the three datasets, searches
 against the reference database were performed using the BLASTn algorithm,
 with the following settings:  -evalue 0.001, -max_target_seqs 100. Blast
 results were then processed with MEGAN6, using the weighted lowest common
 ancestor algorithm with default settings to assign taxonomy to ASVs.COL
 and CAC ASVs datasets were obtained processing COI metabarcode raw data
 from soil samples according to procedures in Andújar et al 2021, and
 filtering the obtained total dataset of ASVs to retain only those
 classified as Coleoptera, Collembola, and Acari respectively, using Blast
 and MEGA tools. Details in Andújar et al 2021.
 Sequences used to identify va-ASVs are provided in the following
 files: "Sanger sequences used to Identify va-ASVs
 on the CAC and COL datasets.fasta" "Reference
 sequences used to identify va-ASVs on the BEE
 dataset.fasta"