To denoise or to cluster, that is not the question: optimizing pipelines for COI metabarcoding and metaphylogeography

Abstract

Background The recent blooming of metabarcoding applications to biodiversity studies comes with some relevant methodological debates. One such issue concerns the treatment of reads by denoising or by clustering methods, which have been wrongly presented as alternatives. It has also been suggested that denoised sequence variants should replace clusters as the basic unit of metabarcoding analyses, missing the fact that sequence clusters are a proxy for species-level entities, the basic unit in biodiversity studies. We argue here that methods developed and tested for ribosomal markers have been uncritically applied to highly variable markers such as cytochrome oxidase I (COI) without conceptual or operational (e.g., parameter setting) adjustment. COI has a naturally high intraspecies variability that should be assessed and reported, as it is a source of highly valuable information. We contend that denoising and clustering are not alternatives. Rather, they are complementary and both should be used together in COI metabarcoding pipelines. Results Using a COI dataset from benthic marine communities, we compared two denoising procedures (based on the UNOISE3 and the DADA2 algorithms), set suitable parameters for denoising and clustering, and applied these steps in different orders. Our results indicated that the UNOISE3 algorithm preserved a higher intra-cluster variability. We introduce the program DnoisE to implement the UNOISE3 algorithm taking into account the natural variability (measured as entropy) of each codon position in protein-coding genes. This correction increased the number of sequences retained by 88%. The order of the steps (denoising and clustering) had little influence on the final outcome. Conclusions We highlight the need for combining denoising and clustering, with adequate choice of stringency parameters, in COI metabarcoding. We present a program that uses the coding properties of this marker to improve the denoising step. We recommend researchers to report their results in terms of both denoised sequences (a proxy for haplotypes) and clusters formed (a proxy for species), and to avoid collapsing the sequences of the latter into a single representative. This will allow studies at the cluster (ideally equating species-level diversity) and at the intra-cluster level, and will ease additivity and comparability between studies.

Managing human mediated range shifts: understanding spatial, temporal and genetic variation in marine non-native species

“…[65]. Recent work has highlighted that different bioinformatic methods have an effect on the resolution of intra-specific variation of eDNA metabarcoding data [42, 45, 72]. Therefore in addition to the sequenced tissue samples and DADA2 methods outlined above, we reanalysed the COI data using the unoise3 algorithm [73] as follows.…”

DnoisE: Distance denoising by Entropy. An open-source parallelizable alternative for denoising sequence datasets

“…We used different numbers of cores, from 1 to 59, for parallelization. We applied the entropy correction values from Antich et al (2021) (Scripts for installation and examples of running DnoisE are provided in Supplementary materials).…”

“…Recent studies have explored the application of both methods to filter metabarcoding data (Antich et al, 2021;Brandt et al, 2021;Elbrecht et al, 2018;Turon et al, 2020). Importantly, the combination of clustering and denoising opens the door to the analysis of intraspecies (intra-MOTU) variability (Antich et al, 2021).…”

“…Recent studies have explored the application of both methods to filter metabarcoding data (Antich et al, 2021;Brandt et al, 2021;Elbrecht et al, 2018;Turon et al, 2020). Importantly, the combination of clustering and denoising opens the door to the analysis of intraspecies (intra-MOTU) variability (Antich et al, 2021). Turon et al (2020) proposed the term metaphylogeography for the study of population genetics using metabarcoding data, and Zizka et al (2020) found different haplotype composition between perturbed and unperturbed rivers, both studies using a combination of clustering and denoising steps.…”

Bacteria are everywhere, even in your COI marker gene data!

“…More specifically, 57 marine, surface water, eDNA samples from Ireland were analysed through a. QIIME2 (Bolyen et al 2018) and DADA2 (Callahan et al 2016) and, b. PEMA (Zafeiropoulos et al 2020). Similarly, 18 mangrove and 18 reef marine eDNA samples from Honduras, were analyzed using a. JAMP v0.74 (Elbrecht 2021) and DnoisE (Antich et al 2021) and b. PEMA . Furthermore, a sediment sample and two samples from Autonomous Reef Monitoring Structures (ARMS) one conserved in DMSO and another in ethanol from the Obst et al (2020) dataset were analysed using PEMA.…”