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The Dark mAtteR iNvestigator (DARN) tool: getting to know the known unknowns in COI amplicon data

Authors :
Sanni Hintikka
Laura M. Gargan
Haris Zafeiropoulos
Christina Pavloudi
Jens Carlsson
Source :
Metabarcoding and Metagenomics, Vol 5, Iss, Pp 163-174 (2021)
Publication Year :
2021
Publisher :
Pensoft Publishers, 2021.

Abstract

The mitochondrial cytochrome C oxidase subunit I gene (COI) is commonly used in environmental DNA (eDNA) metabarcoding studies, especially for assessing metazoan diversity. Yet, a great number of COI operational taxonomic units (OTUs) or/and amplicon sequence variants (ASVs) retrieved from such studies do not get a taxonomic assignment with a reference sequence. To assess and investigate such sequences, we have developed the Dark mAtteR iNvestigator (DARN) software tool. For this purpose, a reference COI-oriented phylogenetic tree was built from 1,593 consensus sequences covering all the three domains of life. With respect to eukaryotes, consensus sequences at the family level were constructed from 183,330 sequences retrieved from the Midori reference 2 database, which represented 70% of the initial number of reference sequences. Similarly, sequences from 431 bacterial and 15 archaeal taxa at the family level (29% and 1% of the initial number of reference sequences respectively) were retrieved from the BOLD and the PFam databases. DARN makes use of this phylogenetic tree to investigate COI pre-processed sequences of amplicon samples to provide both a tabular and a graphical overview of their phylogenetic assignments. To evaluate DARN, both environmental and bulk metabarcoding samples from different aquatic environments using various primer sets were analysed. We demonstrate that a large proportion of non-target prokaryotic organisms, such as bacteria and archaea, are also amplified in eDNA samples and we suggest prokaryotic COI sequences to be included in the reference databases used for the taxonomy assignment to allow for further analyses of dark matter. DARN source code is available on GitHub at https://github.com/hariszaf/darn and as a Docker image at https://hub.docker.com/r/hariszaf/darn.

Details

Language :
English
ISSN :
25349708
Volume :
5
Database :
OpenAIRE
Journal :
Metabarcoding and Metagenomics
Accession number :
edsair.doi.dedup.....713de954ccc06d8bec71fca6e174ab84