MetaCOXI: an integrated collection of metazoan mitochondrial cytochrome oxidase subunit-I DNA sequences

Abstract

Nucleotide sequences reference collections or databases are fundamental components in DNA barcoding and metabarcoding data analyses pipelines. In such analyses, the accurate taxonomic assignment is a crucial aspect, relying directly on the availability of comprehensive and curated reference sequence collection and its taxonomy information. The currently wide use of the mitochondrial cytochrome oxidase subunit-I (COXI) as a standard DNA barcode marker in metazoan biodiversity studies highlights the need to shed light on the availability of the related relevant information from different data sources and their eventual integration. To adequately address data integration process, many aspects should be markedly considered starting from DNA sequence curation followed by taxonomy alignment with solid reference backbone and metadata harmonization according to universal standards. Here, we present MetaCOXI, an integrated collection of curated metazoan COXI DNA sequences with their associated harmonized taxonomy and metadata. This collection was built on the two most extensive available data resources, namely the European Nucleotide Archive (ENA) and the Barcode of Life Data System (BOLD). The current release contains more than 5.6 million entries (39.1% unique to BOLD, 3.6% unique to ENA, and 57.2% shared between both), their related taxonomic classification based on NCBI reference taxonomy, and their available main metadata relevant to environmental DNA studies, such as geographical coordinates, sampling country and host species. MetaCOXI is available in standard universal formats (‘fasta’ for sequences & ‘tsv’ for taxonomy and metadata), which can be easily incorporated in standard or specific DNA barcoding and/or metabarcoding data analysis pipelines. Database URL: https://github.com/bachob5/MetaCOXI

COInr and mkCOInr: Building and customizing a non-redundant barcoding reference database from BOLD and NCBI using a lightweight pipeline

“…However, a potential source of problem s for installing and using the scripts is the high need of third -party programs and packages . METACOXI database (Balech et al, 2022 ) is a COI database that satisfies many criteria. It includes all Metazoan COI sequences from BOLD and NCBI (ENA) and uses NCBI taxIDs wherever possible .…”

“…BOLD_NCBI_Merger (Macher, Macher, & Leese, 2017) uses a very simple method based on identical taxon names, without avoiding the pitfalls of homonyms. MetaCOXI (Balech, Sandionigi, Marzano, Pesole, & Santamaria, 2022) obtains NCBI taxIDs and taxonomic lineages based on ENA flat files, when available. However, when this information is not offered (the sequence is present only in BOLD), NCBI taxIDs are determined by simply matching taxon names to NCBI taxonomy, without checking for homonymy.…”

COInr and mkCOInr: Building and customizing a nonredundant barcoding reference database from BOLD and NCBI using a semi‐automated pipeline

“…Meta-Fish-Lib (Collins et al, 2021) and MitoFish (Sato et al, 2018) are reference libraries of fishes, and COins (Magoga et al, 2022) is a manually curated COI database of insects. The COInr database and the mkcoinr tool are most comparable to the MARES (Arranz et al, 2020) and metacoxi (Balech et al, 2022)…”

“…bold_ncbi_merger (Macher et al, 2017) uses a simple method based on identical taxon names. metacoxi (Balech et al, 2022) obtains NCBI taxIDs and taxonomic lineages based on ENA flat files, when available. However, when this information is not available (the sequence is present only in BOLD), NCBI taxIDs are determined by simply matching taxon names to the NCBI taxonomy, without checking for homonymy.…”

“…Therefore, creating a merged database with sequences from both sources is highly desirable. Most existing COI databases are sourced from NCBI‐nt (e.g., Bengtsson‐Palme et al, 2018; Curd et al, 2019; Keller et al, 2020; Richardson et al, 2020) and only a few of them combine sequences from BOLD and NCBI (Arranz et al, 2020; Balech et al, 2022; Macher et al, 2017; Porter & Hajibabaei, 2018a).…”