Publishing DNA-derived data through biodiversity data platforms

Publishing DNA-derived data through biodiversity data platforms
Publishing DNA-derived data through biodiversity data platforms
Kessy Abarenkov
Anders F. Andersson
Andrew Bissett
Anders G. Finstad
Frode Fossøy
Marie Grosjean
Michael Hope
Thomas S. Jeppesen
Urmas Kõljalg
Daniel Lundin
R. Henrik Nilsson
Maria Prager
Pieter Provoost
Dmitry Schigel
Saara Suominen
Cecilie Svenningsen
Tobias Guldberg Frøslev
version 1.3.3,
27 Feb 2025
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Abstract
Preface
1. Introduction
1.1. Rationale
1.2. Target audiences
1.3. Introduction to DNA-derived occurrence data
1.3.1. Environmental DNA as a source for DNA-derived occurrence data
1.3.2. DNA-metabarcoding: sequence-derived data
1.3.3. Metagenomic: sequence-derived data
1.3.4. qPCR/ddPCR: occurrence data
1.4. Introduction to biodiversity publishing
1.5. Processing workflows: from sample to ingestible data
1.6. Taxonomy of sequences
1.7. Outputs
2. Data packaging and mapping
2.1. Categorization of your data
2.1.1. Category I: DNA-derived occurrences
2.1.2. Category II: Enriched occurrences
2.1.3. Category III: Targeted species detection (qPCR/ddPCR)
2.1.4. Category IV: Name references
2.1.5. Category V: Metadata-only datasets
2.2. Data mapping
2.2.1. Mapping metabarcoding (eDNA) and barcoding data
2.2.2. Mapping ddPCR / qPCR data
2.3. Marine datasets and the Ocean Biodiversity Information System (OBIS)
3. Future prospects
Glossary
References
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Abarenkov K, Andersson AF, Bissett A, Finstad AG, Fossøy F, Grosjean M, Hope M, Jeppesen TS, Kõljalg U, Lundin D, Nilsson RN, Prager M, Provoost P, Schigel D, Suominen S, Svenningsen C & Frøslev TG (2023) Publishing DNA-derived data through biodiversity data platforms, v1.3. Copenhagen: GBIF Secretariat.
Authors
Kessy Abarenkov
kessy.abarenkov@ut.ee
, Natural History Museum and Botanical Garden, University of Tartu, 46 Vanemuise Street, 51003 Tartu, Estonia
Anders F. Andersson
anders.andersson@scilifelab.se
, Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, 17121 Stockholm, Sweden
Andrew Bissett
Andrew.Bissett@csiro.au
, CSIRO O&A, GPO box 1533, Hobart, Tasmania, 7000, Australia
Anders G. Finstad
anders.finstad@ntnu.no
, Department of Natural History, Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim, Norway
Frode Fossøy
Frode.Fossoy@nina.no
, Centre for Biodiversity Genetics (NINAGEN), Norwegian institute for nature research (NINA), P.O. Box 5685 Torgarden, NO-7485 Trondheim, Norway
Marie Grosjean
mgrosjean@gbif.org
, Global Biodiversity Information Facility, Universitetsparken 15, 2100 København Ø, Denmark
Michael Hope
Michael.Hope@ga.gov.au
, Atlas of Living Australia, CSIRO National Collections & Marine Infrastructure, GPO Box 1700, Canberra ACT 2601, Australia.
Thomas S. Jeppesen
tsjeppesen@gbif.org
, Global Biodiversity Information Facility, Universitetsparken 15, 2100 København Ø, Denmark
Urmas Kõljalg
urmas.koljalg@ut.ee
, Natural History Museum and Botanical Garden, University of Tartu, 46 Vanemuise Street, 51003 Tartu, Estonia.
Daniel Lundin
daniel.lundin@lnu.se
, Centre for Ecology and Evolution in Microbial model Systems - EEMiS, Linnaeus University, SE-39182 Kalmar, Sweden
R. Henrik Nilsson
henrik.nilsson@bioenv.gu.se
, University of Gothenburg, Department of Biological and Environmental Sciences, Box 461, 405 30 Göteborg, Sweden
Maria Prager
maria.prager@scilifelab.se
, Science for Life Laboratory, Department of Ecology, Environment and Plant Sciences, Stockholm University; Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet
Pieter Provoost
p.provoost@unesco.org
, Ocean Biodiversity Information System, Jacobsenstraat 1, 8400 Oostende, Belgium
Dmitry Schigel
dschigel@gbif.org
, Global Biodiversity Information Facility, Universitetsparken 15, 2100 København Ø, Denmark
Saara Suominen
s.suominen@unesco.org
, Ocean Biodiversity Information System, Jacobsenstraat 1, 8400 Oostende, Belgium
Cecilie Svenningsen
csvenningsen@gbif.org
, Global Biodiversity Information Facility, Universitetsparken 15, 2100 København Ø, Denmark
Tobias Guldberg Frøslev
tfroeslev@gbif.org
, Global Biodiversity Information Facility, Universitetsparken 15, 2100 København Ø, Denmark
Contributors
Valuable discussions with members of ELIXIR, iBOL, GGBN, GLOMICON, and OBIS networks contributed to compilation of this draft. We are especially grateful for inputs and encouragement from Andrew Bentley, Matt Blissett, Pier Luigi Buttigieg, Kyle Copas, Camila A. Plata Corredor, Gabriele Dröge, Torbjørn Ekrem, Birgit Gemeinholzer, Quentin Groom, Tim Hirsch, Donald Hobern, Hamish Holewa, Corinne Martin, Raissa Meyer, Chris Mungall, Daniel Noesgaard, Corinna Paeper, Tim Robertson, Maxime Sweetlove, Andrew Young, John Waller, Ramona Walls, John Wieczorek, Lucie Zinger who have contributed to the GBIF community review process.
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Publishing DNA-derived data
through biodiversity data platforms
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Version 1.3.3 released on 27 Feb 2025.
This version adds paragraph on marine datasets and the Ocean Biodiversity Information System (OBIS) along with some minor text edits throughout.
A minor update (Feb 2025) adds information and links to the
Metabarcoding Data Toolkit
Abstract
When genetic information is used to describe or classify a taxon, most users will foresee its use in the context of molecular ecology or phylogenetic research. It is important to realize that a sequence with coordinates and a timestamp is a valuable biodiversity occurrence which is useful in a much broader context than its original purpose. To realize this potential, DNA-derived data needs to be discoverable through biodiversity data platforms. This guide will teach you the principles and approaches of exposing “sequences with dates and coordinates” in the context of broader biodiversity data. The guide covers choices of particular schemas and terms, common pitfalls and good practice, without going into platform-specific details. It will benefit anyone interested in better exposure of DNA-derived data through general biodiversity data platforms, including national biodiversity portals.
Preface
The work on this guide started from discussions at the
biodiversity_next conference
in 2019 consolidating inputs from various resources, such as:
Final Report on Environomics Future Science Platform Project
ALA blog post eDNA records now available on ALA
Environmental DNA (eDNA) in the ALA
ALA eDNA data template
Norwegian Criteria for depositing eDNA samples and data, including vouchered specimens
Molecular biodiversity data in SBDI, Sweden
GBIF resources (How) can I publish molecular/sequence/DNA based data to GBIF?
Molecular data in GBIF
GBIF quick guide to publishing data and detailed guides to publishing data
How to publish data to GBIF
, as well as DwC/extension field overview.
Genomic Biodiversity Interest Group
1. Introduction
1.1. Rationale
The last 20 years have brought an increased understanding of the immense power of molecular methods for documenting the diversity of life on earth. Seemingly lifeless and mundane substrates such as soil and sea water turn out to abound with life–although perhaps not in a way that the casual observer may immediately appreciate. DNA-based studies have shown that organism groups such as fungi, insects, oomycetes, bacteria and archaea are everywhere, although we often cannot observe them physically (
Debroas et al. 2017
). The benefits of molecular methods are not restricted to the microscopic world: there are many organisms, such as some fish species, which can at least theoretically be observed physically but for which it is very costly, labour-intensive, and perhaps invasive to do so (
Boussarie et al. 2018
). In such situations, DNA data enable us to record the presence (and past presence) of these organisms non-invasively and with minimal effort. These developments mean that we do not always need tangible, physical manifestations of all organisms present at some site in order to record them. All organisms, whether or not they are physically observable, may be important when it comes to understanding biodiversity, ecology and biological conservation.
DNA-derived data enable us to record inconspicuous or otherwise unobservable taxa that fall below the radar of vetted protocols for field work, checklists, depositions into natural science collections, etc. The current maturity of DNA methodologies enables us to record the presence of these organisms to a level of detail that exceeds that of macroscopic observations of organisms in general. However, bearing in mind that DNA methodologies comes with their own problems and biases, it is important to use this moment to define and agree how we should record and report on an organism as present in some substrate or locality through molecular data. Doing so will help avoid significant inefficiencies that have been reported in other domains, in which the lack of standards and guidance has led to very heterogeneous and largely incomparable bodies of data (
Berry et al. 2021
Leebens-Mack et al. 2006
Yilmaz et al. 2011
Nilsson et al. 2012
Shea et al. 2023
). Moreover, clear documentation of the computational processing from raw sequence reads to deduced species observation, will enable reanalysis when improved methods appear.
DNA-derived occurrence data of species should be as standardized and reproducible as possible, regardless of whether or not the detected species have formal scientific names. In some cases, such occurrence records will hint at previously unknown geographical and ecological properties of described species, thus enriching our body of knowledge on these taxa. In other cases, the data may allow us to amalgamate and visualize information on currently undescribed species, potentially speeding up their eventual formal description. The ability to collect usable data even for unnamed species adds significantly to the many ways in which GBIF and other biodiversity data platforms index the living world, and make this knowledge available to all and for a variety of purposes, including biodiversity conservation. Recent estimates suggest that at least 85 per cent of all extant species are undescribed (
Mora et al. 2011
Tedesco et al. 2014
). Existing data standards have been designed for the minority of taxa that have been described. Good practices for dealing with DNA-derived data will help to characterize occurrences of all organisms, whether described or not.
This guide sets out the ways in which DNA-derived occurrence data should be reported for standardized inclusion in GBIF and other biodiversity data platforms. It does not express any view on the issue of access and benefit sharing for digital sequence information, the subject of extensive discussion through the
Convention on Biological Diversity
(CBD). However, it is worth noting that genetic barcodes and metabarcodes are typically genes or non-coding DNA fragments, which are not suitable for commercial exploitation. As the archiving of sequences through
International Nucleotide Sequence Database Collaboration (INDSC)
is a widespread norm in sequence-based research, publication of occurrence data originating from sequences does not involve publishing new sequences. In most cases these have already been placed in a public genetic repository. This guide therefore addresses the added value possible from deriving spatio-temporal occurrence data and dna-based names from dna data, rather than the value of the genetic information itself. In addition to dealing with sequence-derived data, this guide also includes suggestions for publishing occurrence data of species derived from qPCR or ddPCR analyses.
Reporting DNA-derived occurrences in an open and reproducible way brings many benefits: notably, it increases citability, highlights the taxa concerned in the context of biological conservation and contributes to taxonomic and ecological knowledge. Additionally, it also provides a mechanism to store occurrence records of undescribed species. When this yet to be described taxon is finally linked to a new Linnaean name, all these linked occurrence records will be immediately available. Each of these benefits provides a strong rationale for professionals to adopt the practices outlined in this guide, helping them to highlight a significant proportion of extant biodiversity, hasten its discovery and integrate it into biological conservation and policy-making.
1.2. Target audiences
This guide has been developed for multiple target audiences: students planning a first DNA-based study, researchers with old sequences and abundance tables they want to revive or preserve, biodiversity data specialists who are new to DNA-derived occurrences, and bioinformaticians familiar with sequence data but new to biodiversity data platforms. The guide does not directly target users of molecular data in biodiversity data platforms, but such users may find
section 1.7 on Outputs
particularly interesting. The authors' intention is to provide guidance on publishing data and associated attributes from genetic sequence through general biodiversity data platforms.
The
flowchart
outlines the processing steps involved in publishing amplicon-derived molecular biodiversity data in repositories such as GBIF and national biodiversity data platforms, including those built on the ALA platform. This guide’s focus is primarily on the steps following the arrival of raw
FASTQ
sequences from the sequencing step. By familiarizing themselves with the flowchart—and noting any steps that appear familiar or unclear—users will be able to navigate the content included in the guide.
Figure 1. Overall workflow for DNA sequence-derived biodiversity data as described in this guide.
We have done our best to present the information in this guide so that it is useful for each of the audiences described above, but background reading (e.g.
GBIF quick guide to data publishing
) may be required in certain cases.
1.3. Introduction to DNA-derived occurrence data
DNA-derived biological occurrence data include information derived from DNA from individual organisms, but also from environmental DNA (eDNA, i.e. DNA extracted from environmental samples,
Thomsen & Willerslev 2015
) and from bulk samples comprising many individuals (e.g. plankton samples or Malaise trap samples consisting of multiple individuals from many species). Currently, the greatest volume of DNA-derived occurrence data derives from eDNA. Since analytical methods and end products are largely similar for all sample sources, the discussion below will focus on eDNA (
§2.1.1
and
§2.1.2
), noting that the outline is applicable to the other sources. Surveys often utilize targeted sequencing of taxonomically and phylogenetically informative genetic markers, but can also use, for example, qPCR-based approaches that do not directly result in DNA sequence data (
§2.1.3
and
§2.2.2
). This guide may appear heavy in DNA related terms; if this is the case, please consult the
Glossary
1.3.1. Environmental DNA as a source for DNA-derived occurrence data
Environmental DNA has been in use as a term since 1987, when it was first used to describe DNA from microbes in sediment samples (
Ogram et al. 1987
). eDNA is now more broadly used to describe a complex mix of DNA from different organisms (
Taberlet et al. 2018
and
2012
). Thus, eDNA includes all DNA extracted from a specific environmental sample, regardless of substrate and which species it contains. It may be extracted from a wide range of sources, including skin and hair cells, saliva, soil, faeces, and from living or recently dead organisms (
Pietramellara et al. 2009
). Environmental DNA often sufficiently represents all organisms in a given sample. In practice, however, the presence of DNA in the environmental sample depends on an organisms habitat selection, body size, morphology and activity level. Also, the sampling methods used to capture the DNA (
Taberlet et al. 2018
) and the stage of DNA degradation can affect the presence of DNA.
Figure 2. Caricature of sampling processes comparing data collection by A) traditional ecological/biodiversity sampling methods, and B) eDNA-based studies, here exemplified by metabarcoding. This is a simplified representation. For eDNA, most of the steps up to sequencing will involve technical or biological replications to identify contamination and false positives as well as false negative results, making the structure of data and metadata hierarchical. However, studies will often include both types of sampling. For example, if the 'Reference Library' used in B) does not contain all relevant species from a given group of organisms, it will be necessary to go back to A). It may also be that 'Resolving against Reference Library' produced unexpected or unlikely results, in which case further studies using traditional methodology will be required to determine whether the species identified by bioinformatic analysis can be verified.
eDNA is thus a sample type, not a method, including DNA derived from any environmental sample rather than from the capture and sequencing of a targeted individual. Such sample types includes water, soil, sediment and air, but also gut content samples and tissue (plant/animal) where the host DNA is not targeted (
Taberlet et al. 2018
). A number of analytical methods exist for studying environmental DNA. These can be divided into two main classes: 1) those which aim to detect a specific organism and 2) those which describe an assemblage or a community of a range of organisms. Different methods of analysis will generate different types and volumes of data. Most often DNA concentrations are low, and technical and biological replicates should be used to validate species detection.
Several studies show that, for water samples, analyses based on eDNA may have a higher probability of finding rare and hard to survey species than conventional methods (
Thomsen et al. 2012
Biggs et al. 2015
Valentini et al. 2016
Bessey et al. 2020
). The same may be true in other environments. Therefore, eDNA may be suitable for monitoring rare red list species and undesirable alien species that often have low densities and that are difficult to detect with conventional methods because sometimes DNA traces can still be detected, although the actual organism is no longer present there. Environmental DNA methods are able to detect cryptic organisms, especially those that are small and unable to be detected by the naked eye (e.g. bacteria and fungi). In addition, eDNA can also be used for observation of many species simultaneously, and may describe entire biological communities or major components of them (
Ekrem & Majaneva 2019
).
Some studies show a relationship between the amount of DNA for a given species in an environmental sample and the biomass of the species in the environment. One can therefore potentially also think of environmental DNA allowing a so-called semi-quantitative estimate (indirect target) for organism biomass, both from environmental samples and bulk samples (
Takahara et al. 2012
Thomsen et al. 2012
Andersen et al. 2012
Ovaskainen et al. 2013
Lacoursière-Roussel et al. 2016
Thomsen et al. 2016
Valentini et al. 2016
Fossøy et al. 2019
Yates et al. 2019
Doi et al. 2017
). However, other studies show little correlation between environmental DNA quantity and estimated population density (
Knudsen et al. 2019
). PCR, quantification, mixing and other biases are frequently debated. For example, moult, reproduction and mass death can contribute to increased levels of crustacean environmental DNA in water, while turbidity and poor water quality reduce the amount of detectable environmental DNA (
Strand et al. 2019
). Therefore we encourage data publishers to supply both read counts for each OTU or ASV per sample as well as total read count per sample, as this is necessary information for users to make their own conclusions on presence/absence and (relative) abundance.
1.3.2. DNA-metabarcoding: sequence-derived data
The generation of sequence-derived data is currently increasing fast due to the development of
DNA-metabarcoding
. This method utilizes general primers to generate thousands to millions of short DNA-sequences for a given group of organisms with the help of high-throughput sequencing (HTS, alt. next-generation sequencing (NGS)). By comparing each DNA-sequence to a reference database such as GenBank (
Benson et al. 2006
), BOLD (
Ratnasingham et al. 2007
), or UNITE (
Nilsson et al. 2019
), each sequence can be assigned to a species or higher rank taxon identity.
DNA-metabarcoding
is used for samples originating from both terrestrial and aquatic environments, including water, soil, air, sediments, biofilms, plankton, bulk samples and faces, simultaneously identifying hundreds of species (
Ruppert et al. 2019
).
The identification and classification of organisms from sequence data and marker-based surveys depends on access to a reference library of sequences taken from morphologically identified specimens that are matched against the newly generated sequences. The efficacy of classification depends on the completeness (coverage) and the reliability of reference libraries, as well as the tools used to carry out the classification. These are all moving targets, making it essential to apply taxonomic expertise and caution in the interpreting results (
§1.6
). Availability of of all verified
amplicon sequence variants
Сallahan et al. 2017
) allow for precise reinterpretation of data, intra-specific population genetic analyses (
Sigsgaard et al. 2019
) and is likely to increase identification accuracy, and for this reason we recommend to share (unclustered) ASV data. In 2024, GBIF started the
Metabarcoding Data Programme
to facilitate the publication of eDNA metabarcoding data through GBIF.
1.3.3. Metagenomic: sequence-derived data
Sequence derived diversity data may also be generated using amplification free metagenomic methods whereby all DNA in a sample is targeted for sequencing (
Tyson & Hugenholtz 2005
), rather than specific amplicons or barcodes, as described above. Sequence derived diversity data obtained from metagenomic sequencing can be in the form of sequence matches to annotated gene databases (as above) or as (near) complete metagenome assembled genomes (MAGs). While metabarcoding methods still dominate in terms of sequence derived diversity information, metagenomic data is becoming more important, as evidenced by the growing number of MAGS and their utility in informing phylogeny and taxonomy (
Parks et al. 2020
); discussion of the rapidly evolving methods associated with metagenome analysis is beyond the scope of this document. This document uses metabarcoding as the model for discussion around concepts and methods for publishing sequence derived diversity data, and while the bioinformatic pathways will differ for metagenomic data, the end result (a sequence, often in the form of a contig/assembly) is congruent with the concepts suggested for metabarcoding data (i.e., sample specific, sample collection, data generation and processing workflow metadata should be captured).
1.3.4. qPCR/ddPCR: occurrence data
For the detection of specific species in eDNA-samples, most analyses include species-specific primers and qPCR (Quantitative Polymerase Chain Reaction) or ddPCR (Droplet-Digital Polymerase Chain Reaction). These methods do not generate DNA-sequences, and the occurrence data are completely dependent on the specificity of the primers/assays. Hence, there are strict recommendations for how to validate such assays and the requirements for publishing data (
Bustin et al. 2009
Huggett et al. 2013
), as well as the readiness for assays in routine monitoring (
Thalinger et al. 2020
). Analyses of eDNA-samples using qPCR requires few resources and can be done in most DNA-laboratories. The first example of using eDNA water samples utilized qPCR for detecting the invasive American Bullfrog (
Rana catesbeiana
) (
Ficetola et al. 2008
), and qPCR analyses of eDNA water samples are regularly used for detecting specific species of fish, amphibians, molluscs, crustaceans and more, as well as their parasites (
Hernandez et al. 2020
Wacker et al. 2019
Fossøy et al. 2019
Wittwer et al. 2019
). eDNA-detections using qPCR thus generate important occurrence data for single species.
1.4. Introduction to biodiversity publishing
Publishing biodiversity data is largely a process of making species occurrence data findable, accessible, interoperable and reusable, in accordance with the FAIR principles (
Wilkinson et al. 2016
). Biodiversity data platforms help expose and discover genetic sequence data as biodiversity occurrence records alongside other types of biodiversity data, such as museum collection specimens, citizen science observations, and classical field surveys. The structure, management and storage for each original data source will vary according to the needs of each community. The biodiversity data platforms support data discovery, access and reuse by making these individual datasets compatible with each other, addressing taxonomic, spatial and other inconsistencies in the available biodiversity data. Making data available through single access points supports large-scale data-intensive research, management, and policy. The compatibility between datasets is reached through the process of standardization.
A number of data standards are in use for general biodiversity data (
), and a separate set of standards for genetic sequence data (see
MIxS
and
GGBN
). This guide reflects some ongoing efforts to increase the compatibility between standards for general biodiversity and genetic data. Standards often highlight the subsets of fields which are most important or most frequently applicable. These subsets may be referenced as “cores”. The preferred format for publishing data in the GBIF and ALA networks is currently the Darwin Core Archive (DwC-A) using the
Darwin Core
(DwC) data standard. In practice, this is a compressed folder (a zip file) containing data files, in standard comma- or tab-delimited text format, a metadata file (
eml.xml
) that describes the data resource, and a metafile (meta.xml) that specifies the structure of files and data fields included in the archive. Standardized packaging ensures that the data can travel between systems using specific data exchange protocols.
Section 2
of this guide provides recommendations for the mapping of the data files, while guidelines and tools for constructing the xml files can be found here:
TDWG
GBIF
, and
ALA
A central part of the standardization process is the mapping of fields, which is required to transform the original field (column) structure in a source-data export into a standard field structure. Standardization may also affect the content of the individual fields within each record, for example, by recalculating coordinates to a common system, rearranging date elements, or mapping the contents of fields a standard set of values, often called a vocabulary. The process of standardization also provides an opportunity to improve data quality, for example, by filling in omissions, correcting typos and extra spaces and handling inconsistent use of fields. Such improvements enhance the quality of data and increase its suitability for reuse, but at the same time, data published in any state are better than data that remain unpublished and inaccessible. Standartization is typically applied to a copy or to an export from the data source, leaving the original untouched.
Figure 3. Outline of a platform for reporting and publishing DNA sequences and associated metadata (green box) based on existing systems and data standards (grey boxes). An envisioned system for regular (based on machine-to-machine reading of data) update of results (white box) can either read and update the Darwin Core Archive or various administration systems. The data transfer between the various elements (black arrows) will require various degrees of data transformation and harmonization and may include either mechanical or human quality assessment.
Once a dataset has been through these standardization and data quality processes, it should be placed in an accessible online location and associated with relevant metadata. Metadata–data or information about the dataset includes key parameters that describe the dataset and further improve its discoverability and reuse. Metadata should include other important elements such as authorship, Digital Object Identifiers (DOIs), organizational affiliations and other provenance information, as well as procedural and methodological information about how the dataset was collected and curated. We encourage to provide a description of workflow details and versions including quality control in the
methods section
in the EML file.
Datasets and their associated metadata are indexed by each data portal: this process enables users to query, filter and process data through APIs and web portals. Unlike journal publications, datasets may be dynamic products that go through multiple versions, with an evolving number of records and mutable metadata fields under the same title and DOI.
Note that holders of genetic sequence data are expected to upload and archive genetic sequence data in raw sequence data repositories such as NCBI’s
SRA
, EMBL’s
ENA
or
DDBJ
. The sequence archival topic is not covered here, but e.g.
Penev et al. (2017)
provide a general overview of the importance of data submission and guidelines in connection with scientific publication. Biodiversity data platforms such as ALA, GBIF, and most national biodiversity portals are not archives or repositories for raw sequence reads and associated files. We do, however, stress the importance of maintaining links between such primary data and derived occurrences in
Section 2
1.5. Processing workflows: from sample to ingestible data
Metabarcoding data can be produced from a number of different sequencing platforms (Illumina, PacBio, Oxford Nanopore, Ion Torrent, etc.) that rely on different principles for readout and generation of data that differ with respect to read length, error profile, whether sequences are single or paired-end, etc. Currently the Illumina short-read platform is the most widely adopted and as such is the basis of the descriptions here. However, the bioinformatics processing of the data follows the same general principles (QC, denoising, classification) regardless of the sequencing technology used (
Hugerth et al. 2017
Figure 2
).
Figure 4. Outline of bioinformatic processing of metabarcoding data.
Typically, the DNA sequences are first pre-processed by removing primer sequences and, depending on the sequencing method used, low quality bases, usually toward the 5’ and 3’ sequence ends. Sequences not fulfilling requirements on length, overall quality, presence of primers, tags etc. are removed.
The pre-processed sequences can then be assigned a taxon by comparing them against reference databases. When reference databases are incomplete, sequences classification can be done without taxonomic identifications, either by clustering sequences into operational taxonomic units based on their similarity (OTUs;
Blaxter et al. 2005
) or by denoising the data, i.e. explicitly detecting and excluding PCR/sequencing errors sequences to produce amplicon sequence variants (ASV; also referred to as zero radius OTU (zOTU)). Denoising attempts to correct errors that have been introduced in the PCR and/or sequencing steps, such that the denoised sequences are the set of unique biologically real sequences present in the original sequence mixture. In case of paired-end sequences, the forward and reverse sequences may be denoised separately before merging or else merged prior to denoising. ASVs in the resulting set can differ by as little as one base which is indicative of inter- or intraspecific sequence variation. Operationally, ASVs may be thought of as OTUs without defined radius and while denoising algorithms are typically very good, they do not entirely remove the problems of over-splitting or lumping sequences.
The PCR used for generating the sequencing library can result in the generation of artefactual sequences in the form of chimeras; a single sequence that originates from multiple parent sequences. Such sequences can be detected bioinformatically and removed, and this is typically done after denoising.
Finally, the pre-processed sequences, OTUs or ASVs, are taxonomically classified by comparing them to a database of annotated sequences (often referred to as reference libraries, see
§1.6
). As with the previous steps, several alternative methods are available. Most of these are either based on aligning the metabarcoding sequences to the reference sequences or on counting shared k-mers (short exact sequences).
Several open source tools and algorithms exist for bioinformatic processing of metabarcoding data (QIIME2
(Bolyen et al. 2019)
, DADA2
(Callahan et al. 2016)
, SWARM
(Mahé et al. 2014)
, USEARCH
(Edgar 2010)
, Mothur
(Schloss et al. 2009)
, LULU
(Frøslev et al. 2017)
, PROTAX
(Somervuo et al. 2016)
, VSEARCH
(Rognes et al. 2016)
). Given the existence of many popular and well used workflows, we make some recommendations below on analysing data for submission to biodiversity data platforms. This is not to suggest that these are the best methods or most appropriate for all purposes but is an attempt to encourage submission of relatively standardized data that may readily be compared via the platforms. If possible, a well documented and maintained workflow should be used (e.g.
nf-core/ampliseq pipeline
). Metadata should include workflow details and versions either in the metadata method steps or as a reference in the SOP field in the DNA derived data extension (see mapping in
Table 4
). Sequence data should be deposited in an appropriate nucleotide archive (NCBI’s SRA:
Leinonen et al. 2011
) or EMBL’s ENA (
Amid et al. 2020
)) and data submitted to the biodiversity platform should include the biosample ID obtained from the archive (see data mapping in
§2.2
). Making use of these sample IDs will reduce the chances of duplication and ensure sequence data are readily obtainable should opportunities for re-analysis arise, as reference libraries and bioinformatic tools improve. The core end-product of these pipelines is typically a file of counts of individual OTUs or ASVs in each sample along with the taxonomy assigned to these. This is generated either in tabular format or in the BIOM format (
McDonald et.al 2012
). OTU or ASV sequences are also often provided in the FASTA format (
Pearson & Lipman 1988
).
1.6. Taxonomy of sequences
Taxonomic annotation of sequences is a critical step in the processing of molecular biodiversity datasets, as scientific names are key to accessing and communicating information about the observed organisms. The accuracy and precision of such sequence annotation will depend on the availability of reliable reference databases and libraries across all branches of the tree of life, which in turn will require joint efforts from taxonomists and molecular ecologists. Public sequence databases should always be used knowingly of the fact that they suffer from various shortcomings related to, e.g., taxonomic reliability and lack of standardized metadata vocabularies (
Hofstetter et al. 2019
Durkin et al. 2020
).
Species, as described by taxonomists, are central to biology and attempts at characterizing biodiversity may therefore make use of the end products of taxonomic research. However, unlike DNA sequence data, taxonomic outputs are not always readily amenable to direct algorithmic or computational interpretation: classical taxonomy is a human-driven process which includes manual steps of taxon delimitation, description and naming, culminating in a formal publication in accordance to the international Codes of Nomenclature. As discussed in previous chapters, DNA sequence-based surveys are very effective at detecting hard to observe species and will often identify the presence of organisms currently outside traditional Linnaean taxonomic knowledge. While these guidelines do not address the publication of alternative species checklists derived from sequence data, the disconnection between traditional taxonomy and eDNA efforts is undesirable. Therefore we offer the following recommendations to readers of this guide.
As taxonomy is central to the discovery of biodiversity data, it is highly recommended that any eDNA sequencing efforts should seek to include relevant taxonomic expertise in their study. It will similarly be beneficial if eDNA sequencing studies are able to allocate a portion of their budget to generation and release of reference sequences from previously unsequenced type specimens or other important reference material from the local herbarium, museum, or biological collection. Taxonomists, too, can contribute towards this goal by always including relevant DNA sequences with each new species description (
Miralles et al. 2020
) and by targeting the many novel biological entities unravelled by eDNA efforts (e.g.
Tedersoo et al. 2017
).
Most current biodiversity data platforms are organized around traditional name lists and taxonomic indexes. As DNA sequence-derived occurrences are rapidly becoming a significant source of biodiversity data, and as official taxonomy and nomenclature for such data lags, it is recommended that data providers and platforms should continue to explore and include more flexible representations of taxonomy into their taxonomic backbones. These new representations include molecular reference databases (e.g., GTDB, BOLD, UNITE) that recognize sequence data as reference material for previously unclassified organisms. Additionally, we suggest other commonly used molecular databases (e.g., PR2, RDP, SILVA) should develop stable identifiers for taxa and make reference sequences available for those taxa, to allow their use as taxonomic references.
In contrast to classical taxonomy, which is a heavily manual process, clustering DNA sequences into taxonomic concepts relies on algorithmic analysis of similarity and other signals (such as phylogeny and probability), as well as some human editing. The resulting OTUs vary in stability, presence of reference sequences and physical material, alignments and cut-off values, and OTU identifiers such as DOIs (
Nilsson et al. 2019
). Even more importantly, they vary in scale, from local study- or project-specific libraries to global databases that enable broader cross-study comparison. In contrast to the centralization and codification of Linnaean taxa that are formally described in research publications, OTUs are distributed across multiple evolving digital reference libraries that differ in taxonomic focus, barcode genes and other factors. By associating standard sequences with identified reference specimens, BOLD and UNITE are establishing an essential mapping layer for linking ASVs and OTUs with Linnaean taxonomy. The GBIF backbone taxonomy includes identifiers for UNITE Species Hypotheses (SHs) as well as Barcode Index Numbers (BINs) which allows indexing of species occurrence data taxonomically annotated at the OTU level for primarily Fungi and Animals (
GBIF secretariat 2018
Grosjean 2019
).
Algorithms for taxonomic annotation of eDNA will typically assign each unique sequence to the nearest taxonomic group in a reference set, based on some criteria for relatedness and confidence. For poorly known groups of organisms, such as prokaryotes, insects and fungi, the annotation may be a non-Linnaean placeholder name for a (cluster-based) taxon (i.e. the ID/number of the relevant SH or BIN), and this taxon may represent a species or even a taxonomic unit above the species level. No reference database contains all species in a given group due to the many unknown, unidentified, and undescribed species on earth. Frequent ignorance of this fact has been the source of numerous taxonomic misidentifications during the last 30 years.
During import into the biodiversity platform (e.g. GBIF or OBIS), the taxonomic resolution for these DNA-based occurrences may be reduced even further, as the names/IDs obtained from comparing with the reference database (e.g. UNITE, BOLD) may not all be included in the taxonomic index of that platform at the time of publication. However, the inclusion of the underlying OTU or ASV sequence for each record will allow future users to potentially identify the sequence to a greater level of granularity, particularly as reference libraries improve over time. Therefore we also recommend to publish all sequences in a study - also those that are presently fully unclassified – as they may well be possible to identify with improved reference databases. In cases where the underlying sequence cannot be included as part of the submitted data, we advocate deposition of a (scientific or placeholder) name of the taxon (e.g. the BOLD BIN or UNITE SH) plus an MD5 checksum of the sequence as a unique taxon ID (see
§2.2, “Data mapping”
). MD5 checksums are unidirectional hash algorithms commonly used for
verifying file integrity
. In this case, they would provide a unique and repeatable representation of the original sequence that would nevertheless not allow the sequence itself to be recovered. This may be required in cases where sensitivity exists around access. MD5 checksums enable efficient query to determine whether the same exact sequence has been recovered in other eDNA efforts, but it is not a complete replacement of the sequence as MD5s do not enable further analyses. Two sequences differing by even a single base will get two completely different MD5 checksums, such that BLAST-style sequence similarity searches will not work.
1.7. Outputs
The purpose of exposing DNA-derived data through biodiversity platforms is to enable reuse of these data in combination with other biodiversity data types. It is very important to keep this reuse in mind when preparing your data for publication. Ideally, the metadata and data should tell a complete story in such a way that new, uninformed users can use this evidence without any additional consultations or correspondence. Biodiversity data platforms provide search, filtering, browsing, visualizations, data access, and data citation functionality. For metabarcoding data we encourage users to configure filters for minimum absolute and relative read abundance to make a suitable filtering of data. Singletons or any occurrence with an absolute read count below some selected value can be filtered out by setting a minimum read abundance per OTU or ASV (using the field organismQuantity). Occurrences with a relative read abundance below a selected threshold can be filtered out by setting a minimum value of relative organism quantity, which is calculated from the detected reads (organismQuantity) and total reads in the corresponding sample (sampleSizeValue) (
§2.2.1
). Users can often choose data-output formats (e.g. DwC-A, CSV) and then process, clean and transform data into the shape and format needed for the analyses.
At GBIF.org or through the GBIF API, registered users can search, filter, and download biodiversity data in the following three formats:
Simple
: a simple, tab-delimited format which includes only the GBIF-interpreted version of the data, as a result of the indexing process. This is suitable for quick tests and direct import into spreadsheets.
Darwin Core Archive
: richer format that includes both the interpreted data and the original verbatim version provided by the publisher (prior to indexing and interpretation by GBIF). Because it includes all the metadata and issue flags, this format provides a richer view of the downloaded dataset.
Species list
: a simple table format that includes only an interpreted list of unique species names from a dataset or query result.
Regardless of the selected format, each GBIF user download receives a reusable link to the query and a data citation that includes a DOI. This DOI-based citation system provides the means of recognizing and crediting uses to datasets and data originators, improving both the credibility and transparency of the findings based on the data. It is essential to follow data citation recommendations and use DOIs, as good data citation culture is not only the academic norm, but also a powerful mechanism for crediting acknowledging and, therefore, incentivizing data publishers.
2. Data packaging and mapping
This chapter focuses on practical details on turning your data export into a dataset indexed by a biodiversity data platform.
§2.1
will help you understand what is the optimal mapping schema for your data at hand.
§2.2
describes these mappings in detail.
This guide combines the standards for general biodiversity data publishing with genetic DNA-derived biodiversity data (
Figure 5
). This “do-section” stops at providing mapping recommendations for different types of DNA-derived data.
Data packaging and publishing pathways vary from platform to platform and are described in general documentation. One of the widespread ways to package data files is currently DwC-A, where data tables are arranged in a star schema, with records (rows) in peripheral extension files pointing to a single record in the central core file (
Figure 5
). The different types of core files (e.g. occurrence and sampling-event) correspond to different classes of datasets. Although DNA-derived datasets often are event-based in nature, i.e. hundreds or even thousands of quantified sequence occurrences may derive from a single sampling event and thus share most metadata attributes, the current recommendation is to publish data as Occurrence core (Category I or II) with the DNA derived data extension. This approach compensates for limitations of the DwC star schema, which would not allow any occurrence-level data in extension files (such as processed barcode sequences) to point to records in an event core file. We do, however, recommend including an eventID for each core record, to indicate the association between occurrences derived from the same sampling event.
Figure 5. Zoom in of DwC-A / IPT from figure 3 in chapter 1.2. The choice of core entity is mainly a matter of fitting data to the data import mechanism (ingestion) of the biodiversity data platforms. Most data could be formulated as either Occurrence, Event or Taxon core, but as only the core can have extensions, this will affect the choice. It is for example not possible to extend occurrences with DNA sequences if data are packaged using Event core.
2.1. Categorization of your data
For the purpose of this guide, we categorize data into five categories, linked by a key ID field (
eventID
), equivalent to the standards for general biodiversity data, and include fields relevant for DNA-derived data (see
§2.2, “Data mapping”
). These five categories seek to reflect the most commonly used molecular approaches to biodiversity characterization and are I) DNA-derived occurrences, II) enriched occurrences, III) targeted species detection, IV) name references and V) metadata only. Examine the decision tree and proceed to the correct section below.
Table 1. A decision tree for DNA-derived data categorization.
Is your data (meta)barcoding or qPCR based?
(Meta)barcoding
qPCR
Does data consist of digitized genetic material, or sequences, associated with location and time?
Category III
Targeted species detection
Yes
No
Is the genetic material the
only
evidence of a given organism or community?
Is the dataset a list of DNA-based names?
Yes
No
Yes
No
Category I
DNA-based occurrences
Category II
Enriched occurrences
Category IV
Name references
Category V
Metadata-only
Figure 6. Visual representation of categories I-V.
2.1.1. Category I: DNA-derived occurrences
This category concerns data where a DNA sequence or detection through PCR is the only evidence for the presence of a given organism or community. In other words, the data cannot be traced back to an observable specimen. This is the case for many metagenomics, metabarcoding and eDNA studies.
Examples of DNA-derived occurrence datasets
Holman L E, Bohmann K (2025). Eukaryotic metabarcoding (18S rRNA V9 region) of environmental DNA from an archived marine sediment record, Skagerrak, North Sea, spanning 8000 years. Globe Institute. Occurrence dataset
accessed via GBIF.org on 2025-02-27.
Flanders Research Institute for Agriculture, Fisheries and Food (ILVO): BulkDNA macrobenthos from sandextraction sites in the Belgian part of the North Sea
accessed via GBIF.org on 2025-02-27.
Okrasińska A, Pawłowska J (2025). Metabarcoding of fungi in post-industrial soils. Institute of Evolutionary Biology, University of Warsaw. Occurrence dataset
accessed via GBIF.org on 2025-02-28.
To specifically format and share metabarcoding datasets, we suggest the user friendly
Metabarcoding Data Toolkit (MDT)
– and consult the dedicated guide material:
Metabarcoding Data Toolkit – user guide
. For more general guidance on how to format and share these dna-derived data, see
§2.2.1
. General guidelines for Darwin Core occurrence datasets are also available through the
DwC-A template for occurrence datasets
and
Data quality requirements for occurrences
2.1.2. Category II: Enriched occurrences
If some genetic material is, or can be, associated with an observation or a specimen, we will categorize this type of data as “enriched occurrences”. In this context, the sequences are not the only evidence of occurrences. One can always trace the information back to a vouchered specimen or observed organism. This category includes barcoding datasets and some DNA metabarcoding datasets with reference material for example. For more guidance on barcoding, follow
Centre for Biodiversity Genomics, University of Guelph (2021)
Examples of Enriched occurrence datasets
The International Barcode of Life Consortium (2016) International Barcode of Life project (iBOL). Occurrence dataset
accessed via GBIF.org on 2020-04-16.
Takamura K (2019) Chironomid Specimen records in the Chironomid DNA Barcode Database. Version 1.9. National Institute of Genetics, ROIS. Occurrence dataset
accessed via GBIF.org on 2020-04-16.
Bessey C, Jarman SN, Stat M, Rohner CA, Bunce M, Koziol A, Power M, Rambahiniarison JM, Ponzo A, Richardson AJ & Berry O (2019) DNA metabarcoding assays reveal a diverse prey assemblage for Mobula rays in the Bohol Sea, Philippines. Ecology and Evolution 9 (5) 2459-2474.
, (Atlas of Living Australia website at
. Accessed 24 June 2020)
For guidance on how to format and share these datasets, see
§2.2.1
. General guidelines for Darwin Core occurrence datasets are also available through the
DwC-A template for occurrence datasets
and
Data quality requirements for occurrences
2.1.3. Category III: Targeted species detection (qPCR/ddPCR)
This category concerns data where a specific (qPCR/ddPCR) assay is used to detect the presence (or absence) of a DNA sequence specific to the target organism in an environmental sample. In this case the occurrence record may not even contain sequence data, as it is the process itself that determines the occurrence. With qPCR/ddPCR analyses for targeted species detection, many studies also report absence of that specific species for a given sample. Absence data is highly dependent on the detection limit of the specific assay, as well as field and lab protocols. As for DNA-metabarcoding data there is an issue of both false negatives and false positives, and it is important that sufficient information is reported for evaluating the records.
Examples of targeted species occurrence datasets
Strzelecki, Joanna; Feng, Ming; Berry, Olly; Zhong, Liejun; Keesing, John; Fairclough, David; Pearce, Alan; Slawinski, Dirk; Mortimer, Nick. Location and transport of early life stages of Western Australian Dhufish Glaucosoma hebraicum. Floreat, WA: Fisheries Research and Development Corporation; 2013.
(Atlas of Living Australia website at
. Accessed 22 July 2020)
For guidance on how to format and share these datasets, see
§2.2.2
. General guidelines for Darwin Core occurrence datasets are also available through the
DwC-A template for occurrence datasets
and
Data quality requirements for occurrences
2.1.4. Category IV: Name references
This category corresponds to DNA-derived names, derived from clustering or denoising (error-correction based models), such as stable non-Linnaean Operational Taxonomic Units (OTU), Amplicon Sequence Variant (ASV) and Barcode Index Numbers (BIN)—in other words, any reference to taxa or provisional names that are defined outside of the Linnaean taxonomy. Numerous projects produce local project- or study-specific libraries of OTUs, and although it is technically possible to publish these as checklists, they have limited to no value for data linking or interpretation; as a result, we do not encourage their publication through biodiversity data platforms. However, the inclusion of the widely adopted, stable, global, digitally referenceable OTUs into Linnaean taxonomic backbones is critically important for indexing unnamed “dark” biodiversity. GBIF have accumulated experience in integrating such large and global reference libraries of OTUs into the GBIF taxonomic backbone, which allows the display of OTUs under the nearest parent taxon which has a Scientific name (
Figure 7
).
Figure 7. OTUs (SHs) from UNITE (mainly fungi, above) and from BOLD (BINs) (mainly arthropods, below) are displayed in the GBIF backbone taxonomy under their corresponding parent taxa which have Scientific names. Multiple individually observed occurrences of cryptic biodiversity become discoverable together with non-genetic evidence through a single access point.
Examples of Name references checklists
The International Barcode of Life Consortium (2016). International Barcode of Life project (iBOL) Barcode Index Numbers (BINs). Checklist dataset
accessed via GBIF.org on 2020-04-16.
PlutoF (2019). UNITE - Unified system for the DNA based fungal species linked to the classification. Version 1.2. Checklist dataset
accessed via GBIF.org on 2020-04-16.
This guide does not provide mapping recommendations for global OTU checklists / reference libraries (Category IV), and publishing referenceable (project- or study-specific) OTU libraries as checklists is discouraged. For guidance on how to format and share OTU checklists, see the following general Darwin Core guidelines in
DwC-A template for checklists
and
Data quality requirements for checklists
General guidelines for MIxS checklists
. For advice on how to map global reference libraries of OTUs for inclusion in the GBIF taxonomic backbone, contact the
GBIF help desk
2.1.5. Category V: Metadata-only datasets
Metadata are data about the data and is a description of the dataset in broad terms, such as authors, author affiliations, original research purpose of the dataset, DOI(s), taxonomic scope, temporal scope, and geographical scope. Information regarding laboratory methods and general sequencing methods is included in this category. This category includes datasets or collections that cannot be made available online at the moment, e.g. undigitized work.
Examples of Metadata-only datasets
Collins E, Sweetlove M (2019). Arctic Ocean microbial metagenomes sampled aboard CGC Healy during the 2015 GEOTRACES Arctic research cruise. SCAR - Microbial Antarctic Resource System. Metadata dataset
accessed via GBIF.org on 2020-04-16.
Cary S C (2015). New Zealand Terrestrial Biocomplexity Survey. SCAR - Microbial Antarctic Resource System. Metadata dataset
accessed via GBIF.org on 2020-04-16.
Mapping recommendations for metadata-only DNA-derived datasets (Category V) is the same as for any other metadata-only datasets, and this guide does not provide any specific mapping recommendations for metadata. Please follow general recommendations of biodiversity data portals, paying attention to
required and recommended metadata
. Descriptions of field, lab, and bioinformatics steps should be as detailed as possible. Describing your methods as method steps in the EML metadata makes them display on the dataset homepage in GBIF (
Frøslev T, Ejrnæs R (2018). BIOWIDE eDNA Fungi dataset. Danish Biodiversity Information Facility. Occurrence dataset
accessed via GBIF.org on 2021-07-06). However, if a structured and possibly more detailed method description is already published somewhere (e.g. at
protocols.io
or
NEON protocols collection
), it is straightforward to provide a link through the MIxS SOP field (see
§2.2.1
).
2.2. Data mapping
While core files store ubiquitous data on the 'what, where and when' of a record, extension files are used to describe the specifics of a certain type of observation. We propose using the
DNA derived data extension
to complement occurrence data derived from either barcoding, metabarcoding (eDNA) or qPCR/ddPCR. The DNA derived data extension builds on the
Minimum information standards
developed by the Genomic Standards Consortium (GSC) and applied by the
ENA
for
submission of eDNA sample metadata
, for example. We are following and have contributed to the guidelines proposed by the
Sustainable DwC-MIxS interoperability task group under TDWG
. To improve indexing and search we have opted to split some MIxS terms, for instance separating forward and reverse primer sequences and names. Furthermore, some fields from the GGBN standard and fields from the
MIQE
(minimum information for the publication of quantitative real-time PCR) guidelines for qPCR and ddPCR data have been included to make it applicable for a wide range of DNA-derived data.
As a first step in preparing your data for publishing, you should make sure your field names / column headers follow the
Darwin Core data standard
. In many cases this is straightforward, such as renaming your
lat
or
latitude
field to
decimalLatitude
. However, the Darwin Core Standard is quite flexible and some terms are used in different ways, depending on the type of data. An example of this are the fields
organismQuantity
and
organismQuantityType
, which could be used to describe the number of individuals, per cent biomass or a score on the Braun-Blanquet Scale, as well as the number of reads of an ASV within a sample. Therefore, we here provide tables of required and recommended fields with descriptions and examples (
Table 1
Table 2
Table 3
and
Table 4
). The recommendation to use the Occurrence core for DNA-derived data stems from the strong desire to share the sequence to help qualify the determination. Additional fields and extensions (such as
extended Measurement or Fact (eMoF)
) are applicable - both to occurrence cores and event core. When a sequence is derived from an organism (e.g. a parasite, gut contents, epibiont etc.) the observation may be linked to the observation of the host organism. This can be achieved using the (
Resource Relation extension
) of Darwin Core (e.g.
). Perhaps the most important recommendation is to use globally unique (when available) and other permanent identifiers for as many data fields and parameters as possible (in all ID fields in the tables below).
2.2.1. Mapping metabarcoding (eDNA) and barcoding data
NB: To format and share metabarcoding datasets, we suggest the user friendly
Metabarcoding Data Toolkit (MDT)
which has a specific guide (
Metabarcoding Data Toolkit – user guide
) that includes dedicated versions of the tables in this section.
This section provides mapping recommendations for Categories I and II.
Table 2. Recommended fields for
Occurrence core
for Metabarcoding data
Field name
Examples
Description
Required
basisOfRecord
MaterialSample
The specific nature of the data record - a subtype of the
dcterms:type
. For DNA-derived occurrences, (see
Category I
and
Category III
) use MaterialSample. For enriched occurrences use PreservedSpecimen or LivingSpecimen as appropriate.
Required
occurrenceID
urn:catalog:UWBM:Bird:89776
A unique identifier for the occurrence, allowing the same occurrence to be recognized across dataset versions as well as through data downloads and use. May be a global unique identifier or an identifier specific to the data set.
Required
eventID
urn:uuid:a964765b-22c4-439a-jkgt-2
An identifier for the set of information associated with an Event (something that occurs at a place and time). May be a global unique identifier or an identifier specific to the data set.
Highly recommended
eventDate
2020-01-05
Date when the event was recorded. Recommended best practice is to use a date that conforms to ISO 8601-1:2019. For more information, check
Required
recordedBy
"Oliver P. Pearson | Anita K. Pearson"
A list (concatenated and separated) of names of people, groups, or organizations responsible for recording the original Occurrence. The recommended best practice is to separate the values with a vertical bar (' | '). Including information about the observer improves the scientific reproducibility (
Groom et al. 2020
).
Highly recommended
organismQuantity
33
Number of reads of this OTU or ASV in the sample.
Highly recommended
organismQuantityType
DNA sequence reads
Should always be “DNA sequence reads”
Highly recommended
sampleSizeValue
1233890
Total number of reads in the sample. This is important since it allows calculating the relative abundance of each OTU or ASV within the sample. This number should preferably be calculated after universal processing (quality control, ASV denoising, chimera removal, etc.), but before manual/selective removal of e.g. non-target OTUs or ASVs from the dataset. Rarefaction (resampling to even sequencing depth across samples) is not necessary or advised.
Highly recommended
sampleSizeUnit
DNA sequence reads
Should always be “DNA sequence reads”
Highly recommended
materialSampleID
urn:uuid:a964805b-33c2-439a-beaa-6379ebbfcd03
An identifier for the MaterialSample (as opposed to a particular digital record of the material sample). Use the biosample ID if one was obtained from a nucleotide archive. In the absence of a persistent global unique identifier, construct one from a combination of identifiers in the record that will most closely make the materialSampleID globally unique.
Highly recommended
samplingProtocol
UV light trap
The name of, reference to, or description of the method or protocol used during a sampling Event.
Recommended
associatedSequences
A list (concatenated and separated) of identifiers (publication, global unique identifier, URI) of genetic sequence information associated with the Occurrence. Could be used for linking to archived raw barcode reads and/or associated genome sequences, e.g. in a public repository.
Recommended
identificationRemarks
RDP annotation confidence (at lowest specified taxon): 0.96, against reference database: GTDB
Specification of taxonomic identification process, ideally including data on applied algorithm and reference database, as well as on level of confidence in the resulting identification.
Recommended
identificationReferences
A list (concatenated and separated) of references (publication, global unique identifier, URI) used in the Identification. Recommended best practice is to separate the values in a list with space vertical bar space ( | ).
Recommended
decimalLatitude
60.545207
The geographic latitude (in decimal degrees, using the spatial reference system given in geodeticDatum) of the geographic centre of a Location. Positive values are north of the Equator, negative values are south of it. Legal values lie between -90 and 90, inclusive.
Highly recommended
decimalLongitude
24.174556
The geographic longitude (in decimal degrees, using the spatial reference system given in geodeticDatum) of the geographic centre of a Location. Positive values are east of the Greenwich Meridian, negative values are west of it. Legal values lie between -180 and 180, inclusive.
Highly recommended
taxonID
ASV:7bdb57487bee022ba30c03c3e7ca50e1
For eDNA data, it is recommended to use an MD5 hash of the sequence and prepend it with “ASV:”. See also
§1.6
Highly recommended, if DNA_sequence is not provided
scientificName
Gadus morhua
L. 1758, BOLD:ACF1143
Scientific name of the closest known taxon (species or higher) or an OTU identifier from BOLD (BIN) or UNITE (SH)
Required
kingdom
Animalia
Higher taxonomy
Highly recommended
phylum
Chordata
Higher taxonomy
Recommended
class
Actinopterygii
Higher taxonomy
Recommended
order
Gadiformes
Higher taxonomy
Recommended
family
Gadidae
Higher taxonomy
Recommended
genus
Gadus
Higher taxonomy
Recommended
Table 3. Recommended fields from the DNA derived data extension (a selection) for metabarcoding data
Field name
Examples
Description
Required
DNA_sequence
TCTATCCTCAATTATAGGTCATAATTCACCATCAGTAGATTTAGGAATTTTCTCTATTCATATTGCAGGTGTATCATCAATTATAGGATCAATTAATTTTATTGTAACAATTTTAAATATACATACAAAAACTCATTCATTAAACTTTTTACCATTATTTTCATGATCAGTTCTAGTTACAGCAATTCTCCTTTTATTATCATTA
The DNA sequence (ASV). Taxonomic interpretation of the sequence depends on the technology and reference library available at the time of publication. Hence, the most objective taxonomic handle is the sequence which can be reinterpreted in the future.
Highly recommended
sop
Standard operating procedures used in assembly and/or annotation of genomes, metagenomes or environmental sequences.
A reference to a well documented protocol, e.g. using
protocols.io
Recommended
target_gene
16S rRNA, 18S rRNA, ITS
Targeted gene or marker name for marker-based studies
Highly recommended
target_subfragment
V6, V9, ITS2
Name of subfragment of a gene or markerImportant to e.g. identify special regions on marker genes like the hypervariable V6 region of the 16S rRNA gene
Highly recommended
pcr_primer_forward
GGACTACHVGGGTWTCTAAT
Forward PCR primer that was used to amplify the sequence of the targeted gene, locus or subfragment.
Highly recommended
pcr_primer_reverse
GGACTACHVGGGTWTCTAAT
Reverse PCR primer that was used to amplify the sequence of the targeted gene, locus or subfragment.
Highly recommended
pcr_primer_name_forward
jgLCO1490
Name of the forward PCR primer
Highly recommended
pcr_primer_name_reverse
jgHCO2198
Name of the reverse PCR primer
Highly recommended
pcr_primer_reference
Reference for the primers
Highly recommended
env_broad_scale
forest biome [ENVO:01000174]
Equivalent to env_biome in MIxS v4
In this field, report which major environmental system your sample or specimen came from. The systems identified should have a coarse spatial grain, to provide the general environmental context of where the sampling was done (e.g. were you in the desert or a rainforest?). We recommend using subclasses of ENVO’s biome class:
Recommended
env_local_scale
litter layer [ENVO:01000338]
Equivalent to env_feature in MIxS v4
In this field, report the entity or entities which are in your sample or specimen´s local vicinity and which you believe have significant causal influences on your sample or specimen. Please use terms that are present in ENVO and which are of smaller spatial grain than your entry for env_broad_scale.
Recommended
env_medium
soil[ENVO:00001998]
Equivalent to env_material in MIxS v4
In this field, report which environmental material or materials (pipe separated) immediately surrounded your sample or specimen prior to sampling, using one or more subclasses of ENVO´s environmental material class:
Recommended
lib_layout
Paired
Equivalent to lib_const_meth in MIxS v4
Specify whether to expect single, paired, or other configuration of reads
Recommended
seq_meth
Illumina HiSeq 1500
Sequencing method/platform used
Highly recommended
otu_class_appr
"dada2; 1.14.0; ASV"
Approach/algorithm and clustering level (if relevant) when defining OTUs or ASVs
Highly recommended
otu_seq_comp_appr
"blastn;2.6.0+;e-value cutoff: 0.001"
Tool and thresholds used to assign "species-level" names to OTUs or ASVs
Highly recommended
otu_db
"Genbank nr;221", "UNITE;8.2"
Reference database (i.e. sequences not generated as part of the current study) used to assigning taxonomy to OTUs or ASVs
Highly recommended
2.2.2. Mapping ddPCR / qPCR data
This section provides mapping recommendations for
Category III
Table 4. Recommended fields for Occurrence core for ddPCR/qPCR data
Field name
Examples
Description
Required
basisOfRecord
MaterialSample
The specific nature of the data record - a subtype of the dcterms:type. For DNA-derived occurrences (see
Category I
and
Category III
), use MaterialSample.
Required
occurrenceStatus
Present, Absent
A statement about the presence or absence of a taxon at a location.
Required
eventID
urn:uuid:a964765b-22c4-439a-jkgt-2
An identifier for the set of information associated with an Event (something that occurs at a place and time). May be a global unique identifier or an identifier specific to the dataset.
Highly recommended
eventDate
2020-01-05
Date when the event was recorded. Recommended best practice is to use a date that conforms to ISO 8601-1:2019. For more information, check
Required
recordedBy
"Oliver P. Pearson | Anita K. Pearson"
A list (concatenated and separated) of names of people, groups, or organizations responsible for recording the original Occurrence. The recommended best practice is to separate the values with a vertical bar (' | '). Including information about the observer improves the scientific reproducibility (
Groom et al. 2020
).
Highly recommended
organismQuantity
50
Number of positive droplets/chambers in the sample
Highly recommended for ddPCR, dPCR
organismQuantityType
ddPCR droplets
dPCR chambers
The partition type
Highly recommended for ddPCR, dPCR
sampleSizeValue
20000
The number of accepted partitions (n), e.g. meaning accepted droplets in ddPCR or chambers in dPCR.
Highly recommended for ddPCR, dPCR
sampleSizeUnit
ddPCR droplets
dPCR chambers
The partition type, should be equal to the value in organismQuantityType
Highly recommended for ddPCR, dPCR
materialSampleID
urn:uuid:a964805b-33c2-439a-beaa-6379ebbfcd03
An identifier for the MaterialSample (as opposed to a particular digital record of the material sample). Use the biosample ID if one was obtained from a nucleotide archive. In the absence of a persistent global unique identifier, construct one from a combination of identifiers in the record that will most closely make the materialSampleID globally unique.
Highly recommended
samplingProtocol
UV light trap
The name of, reference to, or description of the method or protocol used during a sampling Event.
Recommended
decimalLatitude
60.545207
The geographic latitude (in decimal degrees, using the spatial reference system given in geodeticDatum) of the geographic centre of a Location. Positive values are north of the Equator, negative values are south of it. Legal values lie between -90 and 90, inclusive.
Highly recommended
decimalLongitude
24.174556
The geographic longitude (in decimal degrees, using the spatial reference system given in geodeticDatum) of the geographic centre of a Location. Positive values are east of the Greenwich Meridian, negative values are west of it. Legal values lie between -180 and 180, inclusive.
Highly recommended
scientificName
Gadus morhua
L. 1758, BOLD:ACF1143
Scientific name of the closest known taxon (species or higher) or an OTU identifier from BOLD or UNITE
Required
kingdom
Animalia
Higher taxonomy
Highly recommended
phylum
Chordata
Higher taxonomy
Recommended
class
Actinopterygii
Higher taxonomy
Recommended
order
Gadiformes
Higher taxonomy
Recommended
family
Gadidae
Higher taxonomy
Recommended
genus
Gadus
Higher taxonomy
Recommended
Table 5. Recommended fields from the
DNA derived data extension
(a selection) for ddPCR/qPCR data
Field name
Examples
Description
Required
sop
Standard operating procedures used in assembly and/or annotation of genomes, metagenomes or environmental sequences.
A reference to a well documented protocol, e.g. using
protocols.io
Highly recommended
annealingTemp
60
The reaction temperature during the annealing phase of PCR.
Required if annealingTemp was supplied
annealingTempUnit
Degrees Celsius
Highly recommended
pcr_cond
initial denaturation:94_3;annealing:50_1;elongation:72_1.5;final elongation:72_10;35
Description of reaction conditions and components of PCR in the form of "initial denaturation:94degC_1.5min; annealing=…​"
Highly recommended
probeReporter
FAM
Type of fluorophore (reporter) used. Probe anneals within amplified target DNA. Polymerase activity degrades the probe that has annealed to the template, and the probe releases the fluorophore from it and breaks the proximity to the quencher, thus allowing fluorescence of the fluorophore.
Highly recommended
probeQuencher
NFQ-MGB
Type of quencher used. The quencher molecule quenches the fluorescence emitted by the fluorophore when excited by the cycler’s light source as long as fluorophore and the quencher are in proximity, quenching inhibits any fluorescence signals.
Highly recommended
ampliconSize
83
The length of the amplicon in basepairs
Highly recommended
thresholdQuantificationCycle
0.3
Threshold for change in fluorescence signal between cycles
qPCR: Highly recommended
baselineValue
15
The number of cycles when fluorescence signal from the target amplification is below background fluorescence not originated from the real target amplification.
qPCR: Highly recommended
quantificationCycle
37.9450950622558
The number of cycles required for the fluorescent signal to cross a given value threshold above the baseline. Quantification cycle (Cq), threshold cycle (Ct), crossing point (Cp), and take-off point (TOP) refer to the same value from the real-time instrument. Use of quantification cycle (Cq), is preferable according to the
RDML (Real-Time PCR Data Markup Language) data standard
automaticThresholdQuantificationCycle
no
Whether the threshold was set by instrument or manually
automaticBaselineValue
no
Whether baseline value was set by instrument or manually
contaminationAssessment
no
Whether DNA or RNA contamination assessment was done or not
estimatedNumberOfCopies
10300
Number of target molecules per µl. Mean copies per partition (?) can be calculated using the number of partitions (n) and the estimated copy number in the total volume of all partitions (m) with a formula ?=m/n.
amplificationReactionVolume
22
PCR reaction volume
amplificationReactionVolumeUnit
µl
Unit used for PCR reaction volume. Many of the instruments require preparation of a much larger initial sample volume than is actually analysed.
pcr_analysis_software
BIO-RAD QuantaSoft
The program used to analyse the d(d)PCR runs.
experimentalVariance
Multiple biological replicates are encouraged to assess total experimental variation. When single dPCR experiments are performed, a minimal estimate of variance due to counting error alone must be calculated from the binomial (or suitable equivalent) distribution.
target_gene
16S rRNA, 18S rRNA, nif, amoA, rpo
Targeted gene or marker name for marker-based studies
Highly recommended
target_subfragment
V6, V9, ITS
Name of subfragment of a gene or markerImportant to identify, for example, special regions on marker genes like the hypervariable V6 region of the 16S rRNA gene
Highly recommended
pcr_primer_forward
GGACTACHVGGGTWTCTAAT
Forward PCR primer that was used to amplify the sequence of the targeted gene, locus or subfragment.
Highly recommended
pcr_primer_reverse
GGACTACHVGGGTWTCTAAT
Reverse PCR primer that was used to amplify the sequence of the targeted gene, locus or subfragment.
Highly recommended
pcr_primer_name_forward
jgLCO1490
Name of the forward PCR primer
Highly recommended
pcr_primer_name_reverse
jgHCO2198
Name of the reverse PCR primer
Highly recommended
pcr_primer_reference
Reference for the primers
Highly recommended
env_broad_scale
forest biome [ENVO:01000174]
Equivalent to env_biome in MIxS v4
In this field, report which major environmental system your sample or specimen came from. The systems identified should have a coarse spatial grain, to provide the general environmental context of where the sampling was done (e.g. were you in the desert or a rainforest?). We recommend using subclasses of ENVO´s biome class:
Recommended
env_local_scale
litter layer [ENVO:01000338]
Equivalent to env_feature in MIxS v4
In this field, report the entity or entities which are in your sample or specimen´s local vicinity and which you believe have significant causal influences on your sample or specimen. Please use terms that are present in ENVO and which are of smaller spatial grain than your entry for env_broad_scale.
Recommended
env_medium
soil [ENVO:00001998]
Equivalent to env_material in MIxS v4
In this field, report which environmental material or materials (pipe separated) immediately surrounded your sample or specimen prior to sampling, using one or more subclasses of ENVO´s environmental material class:
Recommended
concentration
67.5
Concentration of DNA (weight ng/volume µl) see also
Recommended
concentrationUnit
ng/µl
Unit used for concentration measurement see also
Recommended
methodDeterminationConcentrationAndRatios
Nanodrop, Qubit
Description of method used for concentration measurement see also
Recommended
ratioOfAbsorbance260_230
1.89
Ratio of absorbance at 260 nm and 230 nm assessing DNA purity (mostly secondary measure, indicates mainly EDTA, carbohydrates, phenol), (DNA samples only). see also
Recommended
ratioOfAbsorbance260_280
1.91
Ratio of absorbance at 280 nm and 230 nm assessing DNA purity (mostly secondary measure, indicates mainly EDTA, carbohydrates, phenol), (DNA samples only). see also
Recommended
samp_collect_device
biopsy, niskin bottle, push core
The method or device employed for collecting the sample
Recommended
samp_mat_process
filtering of seawater, storing samples in ethanol
Any processing applied to the sample during or after retrieving the sample from environment. This field accepts OBI, for a browser of OBI (v 2018-02-12) terms please see
Recommended
samp_size
5 litre
Amount or size of sample (volume, mass or area) that was collected
Recommended
size_frac
0-0.22 micrometer
Filtering pore size used in sample preparation
Recommended
pcr_primer_lod
51
The assay’s ability to detect the target at low levels
Highly recommended
pcr_primer_loq
184
The assay’s ability to quantify copy number at low levels
Highly recommended
2.3. Marine datasets and the Ocean Biodiversity Information System (OBIS)
When working with datasets originating in the marine environment, it is recommended that the information is published also in the
Ocean Biodiversity Information System
(OBIS) in addition to GBIF. OBIS is a global biodiversity database, which is specialized in providing reliable and accessible data related to marine life and is a part of the IOC-UNESCO. Like GBIF, and ALA, OBIS uses the DwC-A format for data indexing and publishing. By publishing marine datasets through OBIS in addition to other biodiversity databases, the data can reach a broader audience, and diverse groups working in the field of marine biodiversity, as datasets in OBIS are often used for UN processes. With the focus on marine datasets, stringent quality controls on the data increase the reliability of the data and lead to small differences in what information is required for publishing in OBIS as opposed to GBIF.
To ensure consistent taxonomic nomenclature OBIS uses the
World Register of Marine Species
(WoRMS) as the only taxonomic backbone. This is the case also for occurrences derived from genetic data; a scientific name linked to a scientific name ID from the WoRMS database is highly recommended information for publishing. If a scientific name ID is not provided, OBIS will try to match the scientific name with WoRMS during ingestion, but this should be avoided whenever possible. Scientific names not listed in WoRMS are acceptable, and will be submitted to WoRMS for review and possible inclusion in the register. Fully unclassified sequences are recommended to be classified as "incertae sedis", with the WoRMS
scientificNameID
urn:lsid:marinespecies.org:taxname:12. This will ensure correct interpretation by both GBIF and OBIS. Additionally, it is recommended that sequence identifiers from the used reference databases (e.g. Barcode index numbers: BINs from BOLD) be added in the
taxonConceptID
field of the occurrence core table. In this way OBIS will retain its taxonomic backbone based on WoRMS, while enabling linking to disparate reference sequence databases. Names from reference databases which are not strictly scientific names, can be added as
verbatimIdentification
. Automatic classification of species names can often be done through the WoRMS taxon match services and R packages like worrms and taxize. In the future, OBIS plans to periodically search and update the taxonomic assignments of submitted sequences as reference databases develop with time, so recording the sequence information linked to each occurrence is highly recommended.
Another required field in OBIS data submissions are geographic coordinates. OBIS performs additional quality checks related to marine data; e.g. that coordinates for strictly marine species are not on land, and that the depth value reported is in a reasonable range. Finally, it should be mentioned that in addition OBIS supports the use of the
extended Measurement or Fact (eMoF)
. This extension allows linking environmental data and sampling facts to sampling events or occurrences, as well as biological measurements to occurrences in a flexible and standardized manner. OBIS has an example eDNA metabarcoding dataset with scripts for data formatting available at
Table 6. OBIS requirements and recommendations for recording DNA-based occurrences. The table highlights important differences in field values and requirements compared to when publishing to GBIF. Here exemplified with a DNA detection of Blue whale (Balaenoptera musculus).
Field name
Value/example (OBIS)
Description
Required
scientificName
Balaenoptera musculus
Scientific name, preferably as listed in the WoRMS database. This differs from GBIF, where it is recommended to use the taxon name derived from the classification approach used.
Required
scientificNameID
urn:lsid:marinespecies.org:taxname:137090
The scientific name ID of "Balaenoptera musculus" as per the WoRMS database.
Highly recommended
taxonConceptID
NCBI:txid9771
The NCBI ID linked to Balaenoptera musculus in the NCBI taxonomic database. Can also be a BIN-ID if BOLD was used for identification, or another ID from a different database.
Recommended
verbatimIdentification
Balaenoptera musculus
The name corresponding to the NCBI ID (Balaenoptera musculus) (or other ID). This does not necessarily correspond to the value in scientific name.
Recommended
Table 7. OBIS requirements and recommendations for recording sequences that cannot be classified to a scientific name at any taxonomic level.
Field name
Value (OBIS)
Description
Required
scientificName
incertae sedis
The scientific name for unknown sequences recommended by OBIS. Use this name when the sequence/taxonomy is unknown. This differs from GBIF, where it is recommended to use the taxon name as retrieved from the classifier even when it is not strictly a scientific name.
Required
scientificNameID
urn:lsid:marinespecies.org:taxname:12
The scientific name ID of "incertae sedis" as per the WoRMS database for unknown sequences recommended by OBIS. Use this ID when the sequence/taxonomy is unknown.
Highly recommended
taxonConceptID
NCBI:txid1899546
The ID in an external taxonomic database, like a sequence reference database for example.
Recommended
verbatimIdentification
Phototrophic eukaryote
The name of the taxon in an external database, corresponding to the taxon concept ID.
Recommended
3. Future prospects
The present interest in exposing DNA-derived data through biodiversity data platforms is very high, and it is very likely that the demand will grow. Our aim is for the mapping recommendations provided here to remain valid and evolve slowly, even as packaging and indexing by biodiversity data platforms may develop more rapidly. The authors are aware of but did not yet consult the
BOLD Handbook
BIOM format
and
We suggest that data platforms such as ALA and GBIF work towards adopting data formats that support more complex relational and hierarchical data. Examples could be the
Frictionless Data Format
and the more domain-specific
Biological Observation Matrix
(BIOM) format. The latter is used by several bioinformatic tools (
QIIME2
Mothur
USEARCH
etc.), and hence could help publishers skip a step in converting data into DwC-A format. A more flexible data format than the current DwC star schema is crucial for allowing hierarchical sampling events and material samples as well as attaching sequence data to individual occurrences within a sampling event.
Biodiversity data platforms will also need to enable researchers to easily include or exclude DNA-derived occurrence data from their query results. The data formats suggested above could open opportunities for a richer classification of the types of evidence on which a specific occurrence record is based. However, for the time being there is a lack of an appropriate value in the BasisOfRecord vocabulary for these data types. We suggest, as a pragmatic immediate solution, that the BasisOfRecord is extended with a value such as “DNA”, “DNA-derived”, or similar. As described above, DNA-derived data may come from well-documented sampling or individual organisms, may be backed by preserved physical material or not, and may result from genetic sequencing or other DNA detection methods, such as qPCR. Biodiversity data platforms and TDWG should provide the means of differentiating between these data types and their origins.
We also recommend that the data platforms index the actual sequences, or at least a MD5 checksum of these, to facilitate searches for ASVs across datasets. If ASVs are provided, MD5s should be generated by the biodiversity discovery platforms; if ASVs are not provided, MD5s need to be mandatory.
As mentioned in
§1.6
and
§2.1.4
, we encourage the biodiversity data platforms to continue work on adopting relevant molecular taxonomic reference databases into their taxonomic backbones.
Broader application of other methods and technologies, such as Oxford Nanopore, PacBio and shotgun sequencing, will likely trigger the need for adjustments to this guide to accommodate specific new data and metadata fields.
Glossary
Atlas of Living Australia (ALA)
The ALA is a web-based platform that pulls together Australian biodiversity data from multiple sources, making it accessible and reusable to anyone (see
). The open infrastructure platform developed by the ALA is also used by several other countries for their own national biodiversity data platform (see
).
Amplicon Sequence Variant (ASV)
Unique DNA sequence derived from high-throughput sequencing and denoising, and assumed to represent a biologically real sequence variant. See also
Operational Taxonomic Unit (OTU)
and
(Callahan et al. 2017)
Application Programming Interface (API)
Set of protocols and tools for interaction and data transmission between different computer applications.
Barcode Index Numbers (BINs)
Species-level
Operational Taxonomic Units (OTUs)
derived from clustering of the cytochrome c oxidase I (COI) gene in animals. Each BIN is assigned a globally unique identifier, and is made available in searchable database within the
Barcode of Life Data System (BOLD)
Barcode of Life Data System (BOLD)
BOLD
is the reference database maintained by the Centre for Biodiversity Genomics in Guelph on behalf of the International Barcode of Life Consortium (
IBOL
). It hosts data on barcode reference specimens and sequences for eukaryote species, particularly COI for animals, and maintains the Barcode Index Number (
BIN
Ratnasingham & Hebert 2013
) system, identifiers for OTUs of approximately species rank, based on clusters of closely similar sequences.
Biodiversity data platform
General online resource to discover and access biodiversity data derived from various sources, such as natural history collections, citizen science, ecology and monitoring projects, and genetic sequences. Can be global (
GBIF
) or national (
ALA
).
Clustering
In taxonomic classification, the process of grouping organisms together according to some similarity criterion. See
Operational Taxonomic Unit
Community (bulk) DNA
DNA from bulk samples (e.g. plankton samples or Malaise trap samples consisting of several individuals from many species). For the purpose of this guide, bulk sample DNA is included in the eDNA concept.
Darwin Core Archive (DwC-A)
Compressed (ZIP) file format for exchange of biodiversity data compiled in accordance with the
Darwin Core (DwC) standard
. Essentially a self-contained set of interconnected CSV files and an XML document describing included files and data columns, and their mutual relationships.
Darwin Core (DwC) standard
Standard for sharing and publishing biodiversity data, originating from the Biodiversity Information Standards (TDWG) community. In principle, a set of terms used for describing different entities of biodiversity observations, such as sampling events, occurrences and taxa. Current Darwin Core terms are described in the
Quick Reference Guide
Data vocabulary
Set of preferred terms or concepts
with specific, well-defined meanings and interrelationships, facilitating data exchange and reuse.
ddPCR (droplet digital Polymerase Chain Reaction)
Droplet digital
PCR
. Method for measuring absolute amount of DNA (number of copies) of one marker in a sample. See also
qPCR
Denoising
In metabarcoding, method for separation of true biological sequences (see
ASVs
) from spurious sequence variants caused by PCR amplification and sequencing error.
Digital Object Identifier (DOI)
Long-lasting reference used to uniquely identify (and locate) digital information objects, such as a biodiversity data set or a scientific publication.
DNA barcoding and metabarcoding (amplicon sequencing)
Use of short, standardized DNA fragments to identify individual organisms via sequencing. Metabarcoding combines barcoding with high-throughput DNA sequencing, using universal primers to amplify and sequence large groups of organisms in eDNA samples.
DNA marker
A DNA fragment used as a marker of some property (e.g., taxonomic affiliation). May, but does not have to, be a gene or a part of a gene.
DNA metabarcoding database
Database containing DNA sequences (DNA barcodes) from previously recovered or studied organisms. The reference sequences were ideally generated from individuals of described, well-studied species-with the type specimen serving as the ideal-or higher taxonomic level (e.g., genus, family), but may also stem from eDNA sequencing efforts. It is wise not to trust “reference sequences” blindly.
DNA probe
A short, synthetic single-stranded DNA fragment with fluorescent labelling that binds to a selected region of target DNA (marker) during PCR. Increases specificity and can be used in addition to primers in
qPCR
and
ddPCR
to detect and quantify a genetic marker.
European Bioinformatics Institute (EMBL-EBI)
Intergovernmental organization for bioinformatics research and services, part of the European Molecular Biology Laboratory (EMBL), providing eg. (raw) sequence reads and assembly data via
the European Nucleotide Archive (ENA)
Environmental DNA (eDNA)
DNA from an environmental sample, e.g. soil, water, air or host organism. An often used definition is that environmental DNA is the genetic material (DNA) obtained from environmental samples without any obvious evidence of biological source material (
Thomsen and Willerslev 2015
).
European Nucleotide Archive (ENA)
European repository for nucleotide sequences, covering raw sequencing data, sequence assembly information and functional annotation. Includes the
Sequence Read Archive (SRA)
, and is maintained by the European Bioinformatics Institute (EMBL-EBI), as part of
the International Nucleotide Sequence Database Collaboration (INSDC)
FASTQ
Text-based standard for storing molecular sequences and associated quality measures deriving from
High-throughput sequencing (HTS)
. For each sequence position, single ASCII-characters are used to represent base call (identified nucleotide) and score, respectively.
Global Biodiversity Information Facility (GBIF)
International network and research infrastructure, mainly focused on mobilizing and providing open access to global biodiversity data.
Global Genome Biodiversity Network (GGBN)
International network of institutions concerned with efficient sharing and usage of genomic biodiversity samples and associated metadata, e.g. promoting the Darwin Core-compatible GGBN Data Standard.
Global Positioning System (GPS)
Satellite navigation system operated by the United States Space Force.
High-throughput sequencing (HTS)
Different technologies for massively parallel sequencing, producing millions of DNA sequence reads from library preparations of genetic material, rather than targeting single amplicons as in traditional Sanger sequencing. Also called Next Generation Sequencing (NGS).
Ingestion
Process of importing data from heterogeneous sources, such as local databases, text files or spreadsheets, to a common destination system, such as an online
biodiversity data platform
, for storage and further analysis. Typically includes steps of extraction, transformation (cleaning) and loading (ETL).
Indexing
Organization of information in accordance with a specific schema or structure, making data easier to access and present.
International Nucleotide Sequence Database Collaboration (INSDC)
Joint effort of the DNA Databank of Japan (DDBJ),
EMBL
and
NCBI
to provide global public access to nucleotide sequence data and associated information.
Metagenomics
PCR-free sequencing of random genomic fragments in a mixed sample.
Minimum Information about any (x) Sequence (MIxS) standard
Family of standards (checklists) for sequence metadata, developed by the Genomic Standards Consortium (GSC).
molecular Operational Taxonomic Unit (mOTU)
See
Operational Taxonomic Unit (OTU)
National Center for Biotechnology Information (NCBI)
Division of United States National Library of Medicine (NLM) housing important bioinformatics resources, such as the GenBank database of DNA sequences, and the
Sequence Read Archive (SRA)
of high throughput sequencing data.
Next Generation Sequencing (NGS)
See
High-throughput sequencing (HTS)
Occurrence
An existence of an Organism (sensu
) at a particular place at a particular time.
Operational Taxonomic Unit (OTU)
Cluster of organisms based on similarity in specific DNA marker sequence(s), used for taxonomic classification. Includes, for example,
Species Hypothesis
in UNITE, and
Barcode Index Numbers
in the Barcode of Life Data System (BOLD).
Amplicon Sequence Variants (ASVs)
may be considered analogous to
zero radius OTUs (zOTUs)
Polymerase Chain Reaction (PCR)
Technique for fast amplification and detection of specific fragments of target DNA (or RNA) sequences. Amplified regions are determined by the pair of
PCR primers
used in the reaction.
Pipeline
In bioinformatics, a set of algorithms or tools applied in a predefined workflow to process e.g.
High-throughput sequencing (HTS)
data.
Primers (PCR primers)
Short, synthetic, single-stranded DNA fragments that bind to a selected region of target DNA (marker) to initiate replication during
PCR
. A pair of primers is necessary for the polymerase enzyme to amplify the selected marker.
qPCR (quantitative Polymerase Chain Reaction)
Quantitative
PCR
. Method that measures relative DNA quantity of a marker in a sample. See also
ddPCR
Sample
Material (water, soil, gut content, etc) obtained for analysis.
Sequence alignment
Bioinformatic process of comparing and arranging two or more molecular (DNA, RNA or protein) sequences to detect similarities caused by e.g. evolutionary relatedness.
Species Hypothesis (SH)
Species-level
Operational Taxonomic Unit (OTU)
as defined in the UNITE database and sequence management environment, for Fungi.
Specimen
An individual animal, plant, fungus, etc. used as an example of its species or type for scientific study or display.
Sequence Read Archive (SRA)
Public repository of high throughput (
NGS
) sequencing data, with instances operated by
the National Center for Biotechnology Information (NCBI)
the European Bioinformatics Institute (EMBL-EBI)
, and the DNA Data Bank of Japan (DDBJ). Includes both raw (non-denoised) sequencing output and
sequence alignments
. One of three components of
the European Nucleotide Archive (ENA)
, and previously known as the Short Read Archive.
Target-capture sequencing
Sequencing of DNA fragments isolated with hybridization probes.
UNITE
UNITE is a web-based sequence management environment centred on the eukaryotic nuclear ribosomal ITS region. All public sequences are clustered into species hypotheses (SHs), which are assigned unique DOIs. An SH-matching service outputs various elements of information, including what species are present in eDNA samples, whether these species are potentially undescribed new species, other studies in which they were recovered, whether the species are alien to a region, and whether they are threatened. The DOIs are connected to the taxonomic backbone of the
PlutoF platform
and
GBIF
, such that they are accompanied by a taxon name where available. The data used in UNITE are hosted and managed in PlutoF. Data are represented through a range of standards, primarily
Darwin Core
MIxS
, and
DMP Common Standard
; partial support is available for
EML
MCL
, and
GGBN
. PlutoF exports data primarily through the CSV and FASTA formats. PlutoF can also be used to publish data in GBIF (using the DwC format) and to prepare GenBank submission files. It is furthermore possible to download species lists from your data and download your project as a
JSON
document with project data in hierarchically structured.
Zero radius otu (zOTU)
See
ASV
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