Publishing eDNA Data in OBIS

From sequences to a global open biodiversity record

Saara Suominen, Steve Formel, Elizabeth Lawrence, Emilie Boulanger

2026-07-09

Agenda

Time (min)
00:00	Welcome and housekeeping
00:10	Introduction to DNA-derived data
00:40	Dataset exercise introduction
00:45	Exercise: samples.xlsx (Event & eMoF table)
00:55	Exercise: seqtab.txt (Occurrence)
01:05	Exercise: taxonomy.txt (Occurrence)
01:10	Exercise: sequences (DNA-derived data)
01:15	Exercise: methods (DNA-derived data)
01:30	Q & A

eDNA data in OBIS

• OBIS currently holds 128 datasets with eDNA-derived occurrences, including 44,5 million records
• About 2 million distinct sequences
• That is 2% of all datasets, but 25% of all occurrence records in OBIS

OBIS is endorsed by UNESCO’s IOC. Data published there can flow to GBIF as well, giving you double visibility with a single submission. Emphasize that OBIS is not just a repository but actively feeds into global biodiversity assessments, climate analyses, and conservation planning.

What is eDNA?

Environmental DNA (eDNA)

“Any DNA collected from an environmental sample without first isolating targeted organisms”
— Taberlet 2012

• Environments: water, soil, sediment, air
• Free DNA, particle-bound DNA, organelles, cells, tissue
• From viruses to fish, crustaceans, and plants

🌊🌱🍃

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Most common eDNA data types you may encounter:

Environmental DNA Metabarcoding

Quantitative PCR (PCR)

Environmental DNA is any DNA collected from an environmental sample without first isolating target organisms (Taberlet 2012). The most common we’ll likely encounter as OBIS nodes is metabarcoding and qPCR. For this workshop we’ll focus more on metabarcoding

Five data categories

Which one fits your data?

#	Category	Example
1	DNA-derived occurrences	Metabarcoding (ASVs/OTUs assigned to taxa)
2	Enriched occurrences	Voucher specimen + barcoded
3	Targeted species detection	qPCR / ddPCR assays
4	Name references	Sequence in GenBank only
5	Metadata only	Dataset without sequences

🔎 Use the GBIF decision tree to confirm your category

We won’t go into details on this slide - just reiterating that Categories 1 and 3 are by far the most common for eDNA researchers. Category 1 covers metabarcoding; Category 3 covers targeted detection methods like qPCR/ddPCR where no sequences are produced. Be aware that there are other possible types but they are outside the scope of this workshop.

What is Environmental DNA Metabarcoding?

Barcoding + Meta

Barcoding = species ID via a genetic marker sequence
Meta = all together (all organisms at once)

Choosing the right barcode

• Defines which organisms can be identified
• Trade-off: broad groups vs species-level precision
• Common markers: 12S, 16S, 18S, COI, ITS

Environmental DNA is any DNA collected from an environmental sample without first isolating target organisms (Taberlet 2012). Metabarcoding combines ‘meta’ — all together — with barcoding, which is species identification using a short genetic marker. The choice of marker determines which organisms are detected, and there is always a trade-off between taxonomic breadth and species-level resolution.

The eDNA Metabarcoding Workflow

Figure adapted from NatureMetrics and Gill et al. 2016 · Emilie Boulanger

The metabarcoding workflow has six main stages. 1 — Sample collection. 2 — DNA extraction and PCR amplification with specific primers. 3 — High-throughput sequencing generating millions of reads simultaneously. 4 — Bioinformatic treatment: quality filtering and OTU/ASV clustering. 5 — Taxonomic assignment against reference databases. 6 — Ecological analyses producing a presence/absence or abundance matrix across samples.

Key Metadata: eDNA Metabarcoding

From field collection to taxonomy — what to capture at each step

⛵ Field Sampling

• Date, Coordinates, Environmental measurements

🧪 Biomarker / PCR

• Primer sequence, primer reference, target gene, PCR conditions, length of product, concentration,

🧬 DNA Extraction

• Concentration, Extraction method, Extracted amount

💻 Sequences & Taxonomy

• Sequencing platform, Library layout, Public repositry ID (NCBI, ENA)
• Taxonomy, annotation confidence and reference, Pipeline, Reference library

At field sampling: map to eventID, decimalLatitude/Longitude, samplingProtocol, Env_broad_scale. At DNA extraction: Concentration and ratioOfAbsorbance fields. At PCR stage: target_gene, pcr_primer_forward/reverse, pcr_cond. At sequences/taxonomy: seq_meth, DNA_sequence, scientificName, taxonID, otu_db.

Key Metadata: Quantitative PCR (qPCR)

Targeted species detection — additional fields vs metabarcoding · Category 3

⛵ Field Sampling

• Date, Coordinates, Environmental measurements

🧪 PCR Protocol

• PCR conditions

🧬 DNA Extraction

• Concentration, Extraction method, Extracted amount

🎯 Target & Quantity

• Target gene region, primers
• Copy number, concentaration, LOD, Cq, baseline

Category 3 — Targeted species detection · basisOfRecord = MaterialSample

qPCR = Category 3 — targeted species detection. Adds qPCR-specific fields: probeReporter, probeQuencher, estimatedNumberOfCopies, thresholdQuantificationCycle, and baselineValue. Key difference from metabarcoding: you are detecting one specific taxon, so scientificName and scientificNameID are required from the start. Use basisOfRecord = MaterialSample.

Why publish eDNA data?

eDNA captures what other methods miss

• Cryptic and rare marine taxa
  
• Non-invasive, scalable sampling
  
• Community-level biodiversity signals

Shared eDNA data is powerful data

• Data contributes to global analyses
• Data has long-term value
• Your sequences become findable via OBIS sequence search tools

Benefit	Why it matters
Open DOI	Citable in publications
Long-term archive	FAIR data
Global reach	OBIS + GBIF
Sequence search	Reuse & reanalysis

Mention the OBIS sequence search prototype at sequence.obis.org — users can search for similar sequences across all OBIS datasets. This is only possible because sequences are published in standardized form.

What is special about eDNA data?

Enormous potential…

• Expands access to biodiversity data at scale
• Captures environmental, specimen, and bulk-sample DNA
• Raw sequences already widely shared in NCBI

…but NCBI alone is not enough

• Raw sequences cannot be searched by species in time and space
• No link to spatial coordinates or sampling date
• Cannot support distribution maps, trend analyses, or MPA assessments

Raw sequences deposited in NCBI lack coordinates and dates — they cannot answer where or when a species was detected. OBIS, GBIF, and ALA now accept DNA-derived occurrences linked to space and time. DNA data requires special care: it is derived, depends on reference databases, and often produces many unknown sequences. The Darwin Core DNA extension preserves methodological context for future re-analysis.

What is special about eDNA data?

Special considerations

⚗️ Derived information — not a direct observation; extensive lab and bioinformatic processing

📚 Relies on reference databases — results depend on database completeness

❓ Large fraction of unknown sequences — no match in any database

📋 The DwC DNA Extension records how work was done, enabling future re-analysis

Raw sequences deposited in NCBI lack coordinates and dates — they cannot answer where or when a species was detected. OBIS, GBIF, and ALA now accept DNA-derived occurrences linked to space and time. DNA data requires special care: it is derived, depends on reference databases, and often produces many unknown sequences. The Darwin Core DNA extension preserves methodological context for future re-analysis.

eDNA data standards

Two major eDNA data standards exist

1. MiXS
   (Minimum Information about any (x) Sequence) — for raw sequences in NCBI

2. Darwin Core (DwC)
   Community vocabulary for biodiversity data — species, location, date, basis of record

OBIS + GBIF + ALA accept genetic data linked to spatial coordinates and time

🔜Developing landscape

• Rapidly changing field
• For OBIS future directions include the DWC-DP and FAIReDNA terms

Raw sequences deposited in NCBI lack coordinates and dates — they cannot answer where or when a species was detected. OBIS, GBIF, and ALA now accept DNA-derived occurrences linked to space and time. DNA data requires special care: it is derived, depends on reference databases, and often produces many unknown sequences. The Darwin Core DNA extension preserves methodological context for future re-analysis.

Darwin Core + DNA Derived Data Extension

• Darwin Core: shared vocabulary for biodiversity data
  
• DNA Extension: adds MIxS terms for sequences, primers, methods
  
• Developed jointly by GBIF, OBIS, and the genomics community

meta.xml ← links all tables
eml.xml ← metadata (who, what, where, when, how)
occurrence.csv ← the occurrence records
dna_derived.csv ← the DNA extension

MIxS = Minimum Information about any (x) Sequence. The extension incorporates these community standards so the same fields used in INSDC/GenBank submissions can be reused here.

The DNA publishing guide

Publishing DNA-derived data through biodiversity data platforms

From raw outputs → long format

DNA data is more complex than a lat/lon + species name

Your typical metabarcoding outputs:

File	Content
OTU/ASV table	Sequences × Samples (read counts)
Taxonomy table	Sequence [ID] → taxon assignment
Sample metadata	Location, date, method
FASTA file	Actual DNA sequences

The transformation:

One row = one unique sequence in one sample = one occurrence record

The most common stumbling block is this “wide to long” transformation. The OBIS manual has a clear diagram. The Metabarcoding Data Toolkit (MDT) can automate this for common pipeline outputs.

Example transformation

Key fields to include: Occurrence core

Occurrence core table (mandatory fields)

• occurrenceID — unique, stable ID
• scientificName — WoRMS
• basisOfRecord = “MaterialSample”
  
• eventDate, decimalLatitude, decimalLongitude
  
• occurrenceStatus = “present”

Occurrence core table (strongly recommended fields)

• scientificNameID (WoRMS AphiaID)
  
• organismQuantity (read count of sequence)
  
• associatedSequences (link to e.g. GenBank or ENA accession)
• organismQuantityType = “DNA sequence reads”
• sampleSizeValue (total read count in sample)
  
• sampleSizeUnit = “DNA sequence reads”
• samplingProtocol - (link to your SOP)

WoRMS Taxon Matching

Options for taxon matching:

• WoRMS Taxon Match Tool
• Programmatically:
  • obistools::match_taxa
  • worrms::wm_records_taxamatch
  • pyworms

No WoRMS match?

Use highest-rank taxon with a match (e.g. family or order). Keep the sequence — it can be re-identified later as reference databases improve.

Still no match?

Unknown sequences recorded as: scientificName = “Biota incertae sedis” scientificNameID = urn:lsid:marinespecies.org:taxname:12

Key fields to include: DNA Extension

DNA Extension (example key fields)

• DNA_sequence — ASV/OTU sequence
• target_gene — e.g. “COI”, “18S rRNA”
• pcr_primer_name_forward — and reverse
• seq_meth — e.g. “Illumina MiSeq”
• otu_class_appr — e.g. “DADA2 v1.18”
• otu_db — reference database
• sop — link to your protocol

https://manual.obis.org/dna_data.html#quick-start-guide

WoRMS = World Register of Marine Species. Every marine occurrence should have a WoRMS AphiaID as the taxonID. For freshwater / terrestrial taxa use the appropriate backbone (CoL, etc.). Unidentified sequences should still be included — they can be assigned taxon later.

Table structure options

Option A — Occurrence Core

occurrence.csv 
  └── dna_derived.csv  (linked via occurrenceID)
  └── emof.csv         (linked via occurrenceID)

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Option B — Event Core (recommended for eDNA)

event.csv
  └── occurrence.csv        (linked via eventID)
       └── dna_derived.csv  (linked via occurrenceID)
  └── emof.csv              (linked via eventID or occurrenceID)

Why Event Core? Sample-level metadata (location, date, method) is recorded once per sampling event rather than repeated in every row.

For a survey with 500 samples × 2000 ASVs = 1M rows, repeating location and date for every row is wasteful and error-prone. The Event Core avoids this. A new GBIF/OBIS data model is under development that will be even more flexible but is not yet implemented (as of 2025).

Dataset Structure: Three Linked Tables

Event Core

• eventID
• eventDate
• decimalLatitude
• decimalLongitude
• env_medium
• samplingProtocol

→ the where, when & how of collection

Occurrence Extension

• occurrenceID
• eventID
• scientificName
• taxonID (WoRMS)
• basisOfRecord
• occurrenceStatus

→ what organism was detected

DNA Derived Data Ext.

• occurrenceID
• DNA_sequence
• target_gene
• pcr_primer_forward
• pcr_primer_reverse
• seq_meth

→ the molecular context

Tables linked by eventID and occurrenceID

Optional 4th table: eMoF for measurements and facts

Your published dataset consists of up to three linked tables. The Event core captures sampling events — where, when, and how. The Occurrence extension records each taxon detection. The DNA Derived Data extension carries all molecular metadata. The tables are joined by eventID and occurrenceID. This avoids repeating sampling metadata for every sequence.

Example usage of Event core structure

• Why Event Core? Sample-level metadata (location, date, method) is recorded once per sampling event rather than repeated in every row.

eMoF extension

• Table to record all types of measurements (abiotic, biotic, environmental, sampling…)
• Measurements recorded in long format
  • measurementType, measurementValue, measurementUnit
• Linking sampling info to eventIDs decreases duplication
• Use controlled vocabulary whenever possible!

eventID	occurrenceID	measurementType	measurementValue	measurementUnit
YEARsite1samp1		temperature	25	C

measurementTypeID	measurementUnitID
http://vocab.nerc.ac.uk/collection/P01/current/TEMPPR01/	http://vocab.nerc.ac.uk/collection/P06/current/UPAA/

As some of us may already be familiar, eMoF is the extension table that holds ALL kinds of measurement or fact data including measurements on organisms, information about the sampling gear used, or environmental variables measured. A reminder that this data is organized in LONG format. All the measurements are organized in rows within the columns measurementType, measurementValue, and measurementUnit

Controlled Vocabulary

Identifiers for each eMoF column

• measurementUnitID
• measurementValueID
  • non-numeric values
• measurementTypeID

→ Facilitates data understanding

→ Enables data aggregation

→ Decreases misuse potential

What should vocabulary terms capture?

OBIS recommends using NERC vocabulary terms

Emof lets you attach vocabulary identifiers to each of the emof columns. This makes our measurement data are more easily comparable and interoperable, more easily aggregated for analysis, and can decrease misuse potential!

When selecting a vocab it’s important to think about what information needs to be included in the definition for the users to understand your measurement so you should consider the property/attribute (e.g. length, concentration), what’s the object/entity of interest (a organism, a chemical), whether enviro context is important, and whether a user needs to know the methods used to properly understand the measurement OBIS recommends the NERC vocabular collection and the OBIS manual has much more detail on controlled vocabulary

Tools to Help You

GBIF Metabarcoding Data Toolkit

Automates DwC mapping from bioinformatic outputs

gbif.org/tools/mdt

robis (R package)

Query OBIS; retrieve DNA records with unnest_extension()

github.com/iobis/robis

obistools (R package)

Validation, QC, WoRMS matching, archive structure checks

github.com/iobis/obistools

OBIS Sequence Search

BLAST-search sequences already in OBIS (prototype)

sequence.obis.org

The GBIF Metabarcoding Data Toolkit takes your bioinformatic outputs and handles much of the Darwin Core mapping. The obistools R package checks coordinates, matches taxa to WoRMS, and verifies archive structure. The robis package lets you query OBIS and access DNA data with unnest_extension. The OBIS Sequence Search prototype lets you BLAST-search sequences already in OBIS.

The Metabarcoding Data Toolkit (MDT)

A newer tool to simplify the workflow

• Developed by GBIF specifically for metabarcoding data
  
• Accepts common pipeline outputs (QIIME2, DADA2, etc.)
  
• Handles the wide-to-long transformation
  
• Generates Darwin Core Archive + DNA Extension automatically
  
• Integrates with IPT publishing

🔗 Available at: mdt.gbif.org

Note

Still under active development — check the GBIF documentation for the latest capabilities. Currently not possible to use the event core structure

The MDT is a significant time-saver for complex datasets. It can be used alongside the IPT rather than replacing it — it handles the data formatting step and then hands off to the IPT for publishing.

OBIS DNA Data Management Platform

🔧 OBIS is developing a data management platform for DNA data including:

• Bioinformatics
• QC
• Validation
• Publication to OBIS pipeline

To be offered to OBIS nodes to support local eDNA projects!

🚧

eDNA is Already in OBIS — and Being Used

Real eDNA datasets already published:

• eDNA surveys from Monterey Bay, California
• 16S rRNA metabarcoding of pico-to-mesoplankton
• Fish eDNA surveys across multiple ocean basins

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How to find eDNA data in OBIS:

library(robis)

# Filter by DNA extension
occurrence(
  hasextensions = "DNADerivedData",
  taxonid = 127160  # e.g. Actinopteri
)

Species Distribution Modeling

Biodiversity Assessments

MPA Baseline Studies

Global Meta-analyses

There are already thousands of eDNA-derived occurrence records in OBIS. You can explore them via the OBIS Mapper by filtering for the DNADerivedData extension, or via robis with hasextensions=‘DNADerivedData’. These datasets are being used for species distribution modeling, biodiversity assessments, and baseline studies for marine protected areas.

Resources and community

Resource	Link
OBIS Manual (DNA chapter)	manual.obis.org/dna_data.html
OBIS DNA training slides (this presentation!)	github.com/iobis/obis_edna_slides_node_training
GBIF DNA publishing guide	doi.org/10.35035/DOC-VF1A-NR22
OBON 2024 DNA training	github.com/iobis/obon-2024-dna-training
IOOS Bio Mobilization Workshop	ioos.github.io/bio_mobilization_workshop
GBIF-NA DNA Publishing Workshop	sunray1.github.io/2025-05-09-GBIF-NA-DNAPublishing
OBIS helpdesk	helpdesk@obis.org

The community is genuinely welcoming to beginners. The OBIS helpdesk responds quickly. The workshops listed here offer hands-on help with your own data — participants bring actual datasets and work through formatting with instructors.

Summary

Three steps to publish your eDNA data:

1. Format — Darwin Core + DNA Derived Data Extension
   Long format: one row = one sequence per sample

2. Document — Complete EML metadata
   Primers, methods, bioinformatics pipeline, license

3. Publish — Through your OBIS node IPT
   Get a DOI. Be cited. Join the global record.

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Start here → manual.obis.org/dna_data.html

Why it matters

eDNA captures biodiversity no other method can.
Publishing it in OBIS makes it
reusable, citable, and permanent.

Close with energy. Remind the audience that the gap between eDNA data currently in OBIS and what could be there is enormous — their data can meaningfully fill it. If time allows, invite them to share what kind of eDNA data they have and talk about the right category/structure for their case.