4.6 DNA derived data

Contents:

4.6.1 Introduction to DNA data

DNA derived data are increasingly being used to document taxon occurrences. This genetic data may come from a sampling event, an individual organism, may be linked to physical material (or not), or may result from DNA detection methods e.g., metabarcoding or qPCR. Thus genetic data may reflect a single organism, or may include information from bulk samples with many individuals. Still, DNA-derived occurrence data of species should be documented as standardized and as reproducible as possible.

To ensure DNA data are useful to the broadest possible community, a community guide entitled Publishing DNA-derived data through biodiversity data platforms was published by GBIF, OBIS, and others. This guide is supported by the DNA derived data extension for Darwin Core, which incorporates MIxS terms into the Darwin Core standard. There are 5 categories for which genetic data could fall into:

DNA-derived occurrences
Enriched occurrences
Targeted species detection
Name references
Metadata only

For a guide and decision tree on determining which category your data falls into, see the Data packaging and mapping section of the GBIF guide. Refer to the examples below for use case examples of eDNA and DNA derived data (Category 1).

Currently, genetic data must be published with Occurrence core, not Event core. eDNA and DNA derived data are then linked to the Occurrence core data table with the use of occurrenceID and/or eventID. See below for further guidance on compiling genetic data. A new data model is being developed by GBIF and the OBIS community that may change this, however as it is not implemented yet, we focus on the current Darwin Core recommendations here.

To format datasets, you will need to have information on the sequence and possible taxonomy for each occurrence record associated with a DNA sample. Genetic data is often recorded in multiple different files, and this might be the type of format received from data providers. Important data tables can include: an OTU-table, a taxonomy table, a sample information table, and a .fasta file with sequences. The OTU-table is a sequence by sample table, which records the quantity of each unique sequence found in each sample. Sequences are usually referred to by an ID, which is unique only in the dataset (e.g. asv1, asv2, asv3 …). The taxonomy table is a sequence by taxonomy table, which records the taxonomy linked to each unique sequence, as defined by the annotation method. The sample information table records the metadata of each sample (e.g. location, time, and collection method). Finally the .fasta file records the actual DNA sequence that is linked to each sequence id.

Although this data is in multiple files, each unique sequence by sample combination is considered one occurrence. Therefore the data from these tables will need to be formatted to the “long format”, including a row for each sequence in each sample. See the figure below for a demonstration of how this can be done.

Combining multiple DNA data tables into one “long format” table

Doing this will help you when following the guidelines below.

4.6.2 How to find genetic data in OBIS

To find genetic data in OBIS we recommend using the R package robis, using the occurrence function. You must set extensions and/or hasextensions to “DNADerivedData” to ensure extension records are included in the results. hasextensions will exclude any occurrence that does not have the specified extension, in our case, DNADerivedData. The extensions parameter specifies which extensions to include. To obtain the DNA data, you have to extract the information from the extension using the unnest_extension() function. You can specify as many fields from the Occurrence table to be included, and pass them to the fields parameter. See the code below for an example. See also this vignette for a more detailed example, including how you can work further with these sequences in R.

dna_occ<-occurrence("Dinophyceae",hasextensions="DNADerivedData", extensions="DNADerivedData")
dnaseqs <-unnest_extension(dna_datasets, "DNADerivedData", fields = c("id", "phylum", "class", "family", "genus", "species"))
dnaseqs["DNA_sequence"]
# A tibble: 706 × 1
   DNA_sequence                                                                                              
   <chr>                                                                                                     
 1 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC…
 2 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC…
 3 GCTCCTACCGATTGAATGATCCGGTGAGGCCCCCGGACTGCGGCGCCGCAGCTGGTTCTCCAGCCGCGACGCCGCGGGAAGCTGTCCGAACCTTATCATTTAGAG…
 4 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC…
 5 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC…
 6 GCTCCTACCGATTGAATGATCCGGTGAGGCCCCCGGACTGCGGCGCCGCAGCTGGTTCTCCAGCCGCGACGCCGCGGGAAGCTGTCCGAACCTTATCATTTAGAG…
 7 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC…
 8 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC…
 9 GCTCCTACCGATTGAATGATCCGGTGAGGCCCCCGGACTGCGGCGCCGCAGCTGGTTCTCCAGCCGCGACGCCGCGGGAAGCTGTCCGAACCTTATCATTTAGAG…
10 AGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAACGCTCGTAGTCGGATTTCGGGGCGGGCCGACCGGTCTGCCGATGGGTATGCACTGGCCGGCGCGTCC…
# … with 696 more rows

Additionally, a prototype Sequence Search tool is in development that allows you to search for sequences or related sequences in OBIS. Note that the tool is not always up to date so use caution until the prototype is fully developed. To use the tool:

Copy your sequence in the provided box (an example sequence is provided for testing)
Press the Search button
View results below
You can also change the Minimum Alignment Score slider in the map view to see location of sequences

The search result will show you taxonomic information for species sequences that align to your sequence, the alignment score, and a link to the respective datasets.

OBIS Mapper

You can use the OBIS Mapper to obtain records that include the DNADerivedData extension by addding a filter for the extension when using the tool. To do this:

From the OBIS Mapper, navigate to the Criteria tab (the plus (+) sign)
Open the Extensions dropdown section
Check the box for DNADerivedData
Add any other filters you want, e.g. taxonomic, then click save to create the layer
Switch to the Layers tab
Download the data from the layer by clicking the green button (see Data Access for more on using the OBIS Mapper)

4.6.3 Guidelines for compiling genetic data: eDNA and metabarcoding datasets

As mentioned above, you will need to have information on the taxonomy and sequences for each occurrence record associated with a DNA sample. You should first fill in the Occurrence core table, and then complete the DNA Derived Data extension (as well as the eMoF extension, if applicable, for any measurements taken).

Occurrence core table In addition to the usual required terms for Occurrence datasets, you should consider the following additional terms:

Class Occurrence | DwC: organismQuantity
Class Occurrence | DwC: OrganismQuantityType
Class Occurrence| DwC: associatedSequences
Class Event | DwC: sampleSizeValue
Class Event | DwC: sampleSizeUnit
Class Event | DwC: samplingProtocol
Class Identification | DwC: identificationRemarks
Class Identification | DwC: identificationReferences
Class Identification | DwC: verbatimIdentification
Class Taxon | DwC: taxonConceptID
Class Material Sample | DwC:materialSampleID

For organismQuantity and sampleSizeValue in eDNA datasets, the quantities recorded with sequencing studies always represent relative abundance to the total reads in the sample, and cannot be directly compared across samples. This is due to the nature of the sample processing protocol and the amplification of DNA with PCR, which biases the original quantities. In organismQuantity, record the amount of a unique sequence in a specific sample (i.e. 33 reads). In sampleSizeValue, record the total number of all reads in that specific sample (i.e. 15310 reads). This information will allow people accessing the data to calculate the relative abundance of that sequence in the sample. The fields organismQuantityType, and sampleSizeUnit, should be populated with “DNA sequence reads”, as it is of high importance that sequence abundances are not confused with organism abundances recorded by traditional methods. The abundance information can usually be found in the “OTU-table”.

associatedSequences should contain a link to the “raw” sequences deposited in a public database or list of identifiers for the genetic sequence associated with the occurrence record (e.g. GenBank). The actual sequence of the occurrence will be documented in the DNA Derived Data extension.

identificationRemarks should be used to record information on how the taxonomic information of the occurrence was reached against which reference database, and, if possible, with which confidence. For example “RDP annotation confidence: 0.96, against reference database: GTDB”. This information should be recorded in the bioinformatic protocol of the study. Note: this information will also be recorded in the DNA derived extension in the fields otu_seq_comp_appr and otu_db.

identificationReferences should include a link to the bioinformatic pipeline or publication where the identification process is explained in detail.

taxonConceptID should include the taxonomic ID of the sequence (non-Linnean). Often genetic sequences can be assigned an ID linked to a reference database that is not a linnean name. These taxonomic names or IDs from other taxonomic databases (like the NCBI taxonomic database) can be recorded in this field. For example: NCBI:txid9771. The name linked to this ID can then be recorded in the field verbatimIdentification.

samplingProtocol can contain free-text that briefly describes the methods used to obtain the sample, or a link to a protocol that is recorded elsewhere.

DNA Derived Data extension The DNADerivedData extension is meant to capture information related to the sampled DNA, including sampling, processing, and other bioinformatic methods. The following (free-text) terms are required or highly recommended for eDNA and metabarcoding datasets. Note that some terms will be different for qPCR data (see below)

DNA Derived | DwC: DNA_sequence
DNA Derived | DwC: sop
DNA Derived | DwC: target_gene
DNA Derived | DwC: target_subfragment
DNA Derived | DwC: pcr_primer_forward
DNA Derived | DwC: pcr_primer_reverse
DNA Derived | DwC: pcr_primer_name_forward
DNA Derived | DwC: pcr_primer_name_reverse
DNA Derived | DwC: pcr_primer_reference
DNA Derived | DwC: Pcr_cond
DNA Derived | DwC: annealingTemp
DNA Derived | DwC: annealinTempUnit
DNA Derived | DwC: ampliconSize
DNA Derived | DwC: env_broad_scale
DNA Derived | DwC: env_local_scale
DNA Derived | DwC: env_medium
DNA Derived | DwC: lib_layout
DNA Derived | DwC: seq_meth
DNA Derived | DwC: otu_class_appr
DNA Derived | DwC: otu_seq_comp_appr
DNA Derived | DwC: otu_db

For a complete list of terms you can map to, see the DwC DNA Derived Data extension page. See the examples below for use case examples. The Marine Biological Data Mobilization Workshop also has a tutorial for this type of data.

DNA_sequence is the most important field, where the ASV/OTU sequence will be recorded. This field can then be searched with sequence alignment methods to, for example, find closely related sequences recorded in other studies, and will allow very powerful data comparison and analysis in the future. It will also make your sequence available in the OBIS sequence search tool.

The remaining metadata fields will help the person accessing the data to filter data of interest (e.g. specific genetic region with target_gene, target_subfragment, or pcr_primer fields), link to the public sequence databases with the MixS specific fields (e.g. env_ fields), and evaluate the reliability of the sequence annotation method (e.g. otu_ fields).

Environmental systems are described in the two fields env_broad_scale and env_local_scale and it is recommended to use Environment Ontology (ENVO)’s biome classes to describe the environmental system from which the sample was extracted. Like other identifiers, provide the exact ENVO reference identifier. env_broad_scale provides a coarse resolution for which environment your sample came from. Likely this will be marine biome (ENVO:00000447) for OBIS data. For local scale, identify the specific environment your sample was obtained from (e.g., coastal water, benthic zone, etc.).

When data tables are formatted and you are ready to publish it on the IPT, it will follow the same process for publishing on an IPT. You will upload your source files, and add the Occurrence core Darwin Core mappings, and then the DNA Derived Data Darwin Core mappings. However the extension must first be installed by the IPT administrator (often the node manager). Once the extension is installed, you can add the Darwin Core DNA Derived Data mapping for that file.

4.6.3.1 eDNA and DNA derived data example

The following example use cases draw on both the DNA-derived data guide and the DNA derived data extension to illustrate how to incorporate a DNA derived data extension file into a Darwin Core archive. Note: for the purposes of this section, only required Occurrence core terms are shown, in addition to all eDNA & DNA specific terms. For additional Occurrence core terms, refer to Occurrence.

4.6.3.1.1 eDNA data from Monterey Bay, California

The data for this example is from the use case “18S Monterey Bay Time Series: an eDNA data set from Monterey Bay, California, including years 2006, 2013 - 2016’. The data from this study originate from marine filtered seawater samples that have undergone metabarcoding of the 18S V9 region.

Occurrence core:

We can populate the Occurrence core with all the required and highly recommended fields, as well as considering the eDNA and DNA specific fields. The Occurrence core contain the taxonomic identification of each ASV observed; its number of reads, as well as relevant metadata including the sample collection location, references for the identification procedure, and links to archived sequences.

OccurrenceID and basisOfRecord are some of the required Occurrence core terms, in addition to the highly recommended fields of organismQuantity and organismQuantityType. A selection of samples from this plate were included in another publication (Djurhuus et al., 2020),which is recorded in identificationReferences along with the GitHub repository where the data can be found.

occurrenceID	basisOfRecord	organismQuantity	OrganismQuantityType	associatedSequences
11216c01_12_edna_1_S_occ1	MaterialSample	19312	DNA sequence reads	NCBI BioProject acc. nr. PRJNA433203
11216c01_12_edna_2_S_occ1	MaterialSample	16491	DNA sequence reads	NCBI BioProject acc. nr. PRJNA433203
11216c01_12_edna_3_S_occ1	MaterialSample	21670	DNA sequence reads	NCBI BioProject acc. nr. PRJNA433203

sampleSizeValue	sampleSizeUnit	identificationReferences	identificationRemarks
147220	DNA sequence reads	GitHub repository Djurhuus et al. 2020	unassigned, Genbank nr Release 221 September 20 2017
121419	DNA sequence reads	GitHub repository Djurhuus et al. 2020	unassigned, Genbank nr Release 221 September 20 2017
161525	DNA sequence reads	GitHub repository Djurhuus et al. 2020	unassigned, Genbank nr Release 221 September 20 2017

DNA Derived Data extension:

Next, we can create the DNA Derived Data extension which will be connected to the Occurrence core with the use of occurrenceID. This extension contains the DNA sequences and relevant DNA metadata, including sequencing procedures, primers used and SOP’s. The recommended use of ENVO’s biome classes were applied to describe the environmental system from which the sample was extracted. The samples were collected by CTD rosette and filtered by a peristaltic pump system. Illumina MiSeq metabarcoding was applied for the target_gene 18S and the target_subfragment, V9 region. URL’s are provided for the protocols followed for nucleic acids extraction and amplification.

For a detailed description of the steps taken to process the data, including algorithms used, see the original publication. Adding Operational Taxonomic Unit (OTU) related data are highly recommended and should be as complete as possible, for example:

occurrenceID	env-broad_scale	env_local_scale	env_medium
11216c01_12_edna_1_S_occ1	marine biome (ENVO:00000447)	coastal water (ENVO:00001250)	waterborne particulate matter (ENVO:01000436)
11216c01_12_edna_2_S_occ1	marine biome (ENVO:00000447)	coastal water (ENVO:00001250)	waterborne particulate matter (ENVO:01000436)
11216c01_12_edna_3_S_occ1	marine biome (ENVO:00000447)	coastal water (ENVO:00001250)	waterborne particulate matter (ENVO:01000436)

samp_vol_we_dna_ext	nucl_acid_ext	nucl_acid_amp	lib_layout	target_gene
1000ml	dx.doi.org/10.17504/protocols.io.xjufknw	dx.doi.org/10.17504/protocols.io.n2vdge6	paired	18S
1000ml	dx.doi.org/10.17504/protocols.io.xjufknw	dx.doi.org/10.17504/protocols.io.n2vdge6	paired	18S
1000ml	dx.doi.org/10.17504/protocols.io.xjufknw	dx.doi.org/10.17504/protocols.io.n2vdge6	paired	18S

target_subfragment	seq_meth	otu_class_appr	otu_seq_comp_appr
V9	Illumina MiSeq 2x250	dada2;1.14.0;ASV	blast;2.9.0+;80% identity;e-value cutoff: x MEGAN6;6.18.5;bitscore: 100 :2%
V9	Illumina MiSeq 2x250	dada2;1.14.0;ASV	blast;2.9.0+;80% identity;e-value cutoff: x MEGAN6;6.18.5;bitscore: 100 :2%
V9	Illumina MiSeq 2x250	dada2;1.14.0;ASV	blast;2.9.0+;80% identity;e-value cutoff: x MEGAN6;6.18.5;bitscore: 100 :2%

otu_db	sop	DNA_sequence
Genbank nr;221	dx.doi.org/10.17504/protocols.io.xjufknw or GitHub repository	GCTACTACCGATT…
Genbank nr;221	dx.doi.org/10.17504/protocols.io.xjufknw or GitHub repository	GCTACTACCGATT…
Genbank nr;221	dx.doi.org/10.17504/protocols.io.xjufknw or GitHub repository	GCTACTACCGATT…

pcr_primer_forward	pcr_primer_reverse	pcr_primer_name_forward	pcr_primer_name_reverse	pcr_primer_reference
GTACACACCGCCCGTC	TGATCCTTCTGCAGGTTCACCTAC	1391f	EukBr	Amaral-Zettler et al. 2009
GTACACACCGCCCGTC	TGATCCTTCTGCAGGTTCACCTAC	1391f	EukBr	Amaral-Zettler et al. 2009
GTACACACCGCCCGTC	TGATCCTTCTGCAGGTTCACCTAC	1391f	EukBr	Amaral-Zettler et al. 2009

4.6.3.1.2 16S rRNA gene metabarcoding data of Pico- to Mesoplankton

DNA derived datasets can also include an extendedMeasurementsOrFact (eMoF) extension file, in addition to the Occurrence and DNA derived extensions. In this example, environmental measurements were provided in an eMoF file, in addition to the DNA derived data and occurrence data. Here we show how to incorporate such measurements in the extensions.

In the publication “Diversity of Pico- to Mesoplankton along the 2000 km Salinity Gradient of the Baltic Sea”, a dataset with 16S rRNA gene metabarcoding data of surface water microbial communities was created from 21 off-shore stations, following a transect from Kattegat to the Gulf of Bothnia in the Baltic Sea. The full dataset entitled “Diversity of Pico- to Mesoplankton along the 2000 km Salinity Gradient of the Baltic Sea (Hu et al. 2016) is available from GBIF.

Occurrence core:

The Occurrence core contain information about the organisms in the sample including the taxonomy and quantity of organisms detected, the collection location, references for the identification procedure, and links to the sequences generated.

Important note: even though this dataset uses OTU identifiers for taxonomy (therefore not including scientificNameID) OBIS still recommends using scientificNameID.

basisOfRecord	occurrenceID	eventID	eventDate
MaterialSample	SBDI-ASV-3:16S_1:919a2aa9d306e4cf3fa9ca02a2aa5730	SBDI-ASV-3:16S_1	2013-07-13 07:08:00
MaterialSample	SBDI-ASV-3:16S_1:43e088977eba5732bfa45e20b1d8cdd2	SBDI-ASV-3:16S_1	2013-07-13 07:08:00
MaterialSample	SBDI-ASV-3:16S_1:887bc7033b46d960e893caceb711700b	SBDI-ASV-3:16S_1	2013-07-13 07:08:00

organismQuantity	organismQuantityType	sampleSizeValue	sampleSizeUnit
2235	DNA sequence reads	12393	DNA sequence reads
795	DNA sequence reads	12393	DNA sequence reads
40	DNA sequence reads	12393	DNA sequence reads

samplingProtocol	associatedSequences	identificationReferences	identificationRemarks
200–500 mL seawater were filtered onto 0.22 μm pore-size mixed cellulose ester membrane filters; [https://doi.org/10.3389/fmicb.2016.00679]	[https://www.ebi.ac.uk/ena/browser/view/ERR1202034]	[https://docs.biodiversitydata.se/analyse-data/molecular-tools/#taxonomy-annotation]	DADA2:assignTaxonomy:addSpecies annotation against sbdi-gtdb=R06-RS202-1; confidence at lowest specified (ASV portal) taxon: 0.5
200–500 mL seawater were filtered onto 0.22 μm pore-size mixed cellulose ester membrane filters; [https://doi.org/10.3389/fmicb.2016.00679]	[https://www.ebi.ac.uk/ena/browser/view/ERR1202034]	[https://docs.biodiversitydata.se/analyse-data/molecular-tools/#taxonomy-annotation]	DADA2:assignTaxonomy:addSpecies annotation against sbdi-gtdb=R06-RS202-1; confidence at lowest specified (ASV portal) taxon: 0.56
200–500 mL seawater were filtered onto 0.22 μm pore-size mixed cellulose ester membrane filters; [https://doi.org/10.3389/fmicb.2016.00679]	[https://www.ebi.ac.uk/ena/browser/view/ERR1202034]	[https://docs.biodiversitydata.se/analyse-data/molecular-tools/#taxonomy-annotation]	DADA2:assignTaxonomy:addSpecies annotation against sbdi-gtdb=R06-RS202-1; confidence at lowest specified (ASV portal) taxon: 0.99

decimalLatitude	decimalLongitude	taxonID	scientificName
55.185	13.791	ASV:919a2aa9d306e4cf3fa9ca02a2aa5730	UBA6821
55.185	13.791	ASV:43e088977eba5732bfa45e20b1d8cdd2	Chthoniobacterales
55.185	13.791	ASV:887bc7033b46d960e893caceb711700b	BACL27 sp014190055

kingdom	phylum	class	order	family	genus
Bacteria	Verrucomicrobiota	Verrucomicrobiae	Chthoniobacterales	UBA6821	UBA6821
Bacteria	Verrucomicrobiota	Verrucomicrobiae	Chthoniobacterales	NA	NA
Bacteria	Actinobacteriota	Acidimicrobiia	Acidimicrobiales	Ilumatobacteraceae	BACL27

DNA Derived Data extension:

The DNA Derived Data extension for metabarcoding data contains the DNA sequences and relevant DNA metadata, primers and procedures. This example table contains the highly recommended and recommended fields as populated with the example dataset data. For this dataset, authors additionally provided measurements of of water sample temperature and salinity, which are provided in an extendedMeasurementOrFact extension file:

id	env_broad_scale	env_local_scale	env_medium
SBDI-ASV-3:16S_1:919a2aa9d306e4cf3fa9ca02a2aa5730	aquatic biome [ENVO_00002030]	marine biome [ENVO_00000447]	brackish water [ENVO_00002019]
SBDI-ASV-3:16S_1:43e088977eba5732bfa45e20b1d8cdd2	aquatic biome [ENVO_00002030]	marine biome [ENVO_00000447]	brackish water [ENVO_00002019]
SBDI-ASV-3:16S_1:887bc7033b46d960e893caceb711700b	aquatic biome [ENVO_00002030]	marine biome [ENVO_00000447]	brackish water [ENVO_00002019]

lib_layout	target_gene	target_subfragment	seq_meth	sop
paired	16S rRNA	V3-V4	Illumina MiSeq	https://nf-co.re/ampliseq
paired	16S rRNA	V3-V4	Illumina MiSeq	https://nf-co.re/ampliseq
paired	16S rRNA	V3-V4	Illumina MiSeq	https://nf-co.re/ampliseq

pcr_primer_forward	pcr_primer_reverse	pcr_primer_name_forward	pcr_primer_name_reverse	DNA_sequence
CCTACGGGNGGCWGCAG	GACTACHVGGGTATCTAATCC	341	805R	TCGAGAATTTTTCACAATG…
CCTACGGGNGGCWGCAG	GACTACHVGGGTATCTAATCC	341	805R	TCGAGAATTTTTCACAATG…
CCTACGGGNGGCWGCAG	GACTACHVGGGTATCTAATCC	341	805R	TGGGGAATCTTGCGCAATG…

extendedMeasurementOrFact (eMoF) extension:

measurementID	occurrenceID	measurementType	measurementValue	measurementUnit
SBDI-ASV-3:16S_1:temperature	SBDI-ASV-3:16S_1:919a2aa9d306e4cf3fa9ca02a2aa5730	temperature	16.9	°C
SBDI-ASV-3:16S_1:salinity	SBDI-ASV-3:16S_1:919a2aa9d306e4cf3fa9ca02a2aa5730	salinity	7.25	psu
SBDI-ASV-3:16S_1:temperature	SBDI-ASV-3:16S_1:1ead98754d34073a4606f7ff1e94126e	temperature	16.9	°C

4.6.4 Unknown sequences

It is important to understand the significance of unknown and uncharacterized sequences in genetic studies. Sequences are given taxonomic names based on comparisons to a reference database. The reference databases contain sequences that have been submitted with a name. Ideally, the reference database is a collection of sequences that are derived from vouchered, morphologically identified specimens. Notably, this is frequently often not the case and sequences can also have erratic annotations. Furthermore, only a small portion of species have sequences in reference databases. Due to this reason, typically many sequences in any given study will remain uncharacterized. This is especially the case for tropical regions with high biodiversity. By also recording all sequences, including uncharacterized sequences, we make sure that the information is not lost, even if the annotation is currently incorrect or missing. These uncharacterized sequences can then still be compared to other studies, and can be given a taxonomic name as more specimens are sequenced and added to the reference databases.

For unknown sequences it is required to populate the scientificName field with “Incertae sedis”, or the lowest taxonomic information if available. For example, if it is only known which Class a sequence belongs to, populate scientificName with the associated Class name. Similarly, scientificNameID should be populated with the WoRMS LSID for the name given to scientificName. For records recorded as Incertae sedis, scientificNameID should be populated with urn:lsid:marinespecies.org:taxname:12. We recommend also populating verbatimIdentification with the name that was originally documented (e.g. phototrophic eukaryote).

4.6.5 Guidelines for compiling genetic data: qPCR

Compiling qPCR data is a little bit different than compiling eDNA or metabarcoding data. One of the main differences is that there are no sequences recorded in the DNA_sequence field of the DNA derived data extension. Instead, occurrences are based on detections made using species-specific primers and either qPCR (Quantitative Polymerase Chain Reaction) or ddPCR (Droplet-Digital Polymerase Chain Reaction), no sequencing is done.

It is very important to document the methods used for this type of data because the results can be sensitive to the specificity of the primers/assays used. Therefore documenting as much detail on the methodologies is important to ensure data interpretability.

Occurrence core table In addition to the Occurrence core terms, it is strongly recommend to including the following terms for qPCR data:

Class Occurrence | DwC:recordedBy
Class Occurrence | DwC: organismQuantity
Class Occurrence | DwC: organismQuantityType
Class Event | DwC: sampleSizeValue
Class Event | DwC: sampleSizeUnit
Class Event | DwC: samplingProtocol
Class Material Sample | DwC:materialSampleID

For ddPCR, organismQuantity refers to the number of positive droplets/chambers in the sample, and organismQuantityType is the partition type (e.g., ddPCR droplets, dPCR chambers). sampleSizeValue will be populated with the number of accepted partitions, e.g. meaning accepted droplets in ddPCR or chambers in dPCR. sampleSizeUnit is the partition type, which should be the same as organismQuantityType. All four of these fields are particularly important to include for ddPCR data.

For qPCR, these fields can be used for recording e.g. the number of copies that were calculated for the target gene in the sample. In this case organismQuantityType needs to contain the exact type of the measurement reported in the results. The field accepts any string, but the best practice would be to add a URI pointing to a vocabulary, as is done in the extendedMeasurementOrFact extension. The terms sampleSizeValue and sampleSizeUnit would not be used in this case.

materialSampleID should contain an identifier for the MaterialSample (i.e. occurrence record), rather than a digital record of the material sample. If an ID was obtained from a nucleotide archive, use the associated biosample ID. Otherwise, construct a persistent unique identifier from a combination of elements in the data that will make the materialSampleID globally unique, similar to eventIDs and occurrenceIDs.

recordedBy can be populated with the names of the people, groups, or organizations responsible for recording the original Occurrence. You can use a concatenated list for multiple names, by separating values with a vertical bar (’ | ’).

DNA Derived Data extension For qPCR datasets, it is strongly recommended to document as much detail as possible in this extension, particularly details about the PCR primers used and the target gene. We recommend you include the following terms, where relevant. For term definitions see DNA derived data extension.

Terms related to the sampling event:

DNA Derived | DwC: env_broad_scale
DNA Derived | DwC: env_local_scale
DNA Derived | DwC: env_medium
DNA Derived | DwC: samp_collect_device
DNA Derived | DwC: samp_mat_process
DNA Derived | DwC: samp_size
DNA Derived | DwC: size_frac

Terms related to DNA and PCR methods:

DNA Derived | DwC: sop
DNA Derived | DwC: concentration
DNA Derived | DwC: concentrationUnit
DNA Derived | DwC: methodDeterminationConcentrationAndRatios
DNA Derived | DwC: contaminationAssessment
DNA Derived | DwC: target_gene
DNA Derived | DwC: target_subfragment
DNA Derived | DwC: ampliconSize
DNA Derived | DwC: amplificationReactionVolume
DNA Derived | DwC: amplificationReactionVolumeUnit
DNA Derived | DwC: baselineValue
DNA Derived | DwC: automaticBaselineValue
DNA Derived | DwC: automaticThresholdQuantificationCycle
DNA Derived | DwC: thresholdQuantificationCycle
DNA Derived | DwC: pcr_analysis_software
DNA Derived | DwC: pcr_primer_forward
DNA Derived | DwC: pcr_primer_reverse
DNA Derived | DwC: pcr_primer_name_forward
DNA Derived | DwC: pcr_primer_name_reverse
DNA Derived | DwC: pcr_primer_reference
DNA Derived | DwC: pcr_cond
DNA Derived | DwC: pcr_primer_lod
DNA Derived | DwC: pcr_primer_loq
DNA Derived | DwC: annealingTemp
DNA Derived | DwC: annealinTempUnit
DNA Derived | DwC: probeQuencher
DNA Derived | DwC: probeReporter
DNA Derived | DwC: quantificationCycle
DNA Derived | DwC: ratioOfAbsorbance260_230
DNA Derived | DwC: ratioOfAbsorbance260_280

There are many specialized qPCR terms that are possible to add to the dataset. The terms concentration, concentrationUnit, ratioOfAbsorbance260_230, ratioOfAbsorbance260_280, and methodDeterminationConcentrationAndRatios are related to the original DNA sample before qPCR analysis, and can be useful for evaluating the prevalence of the marker in the sample as well as the purity of the DNA for any indication of PCR inhibition.

As with the metabarcoding dataset, the details of the PCR conditions and primers can be recorded in the multiple pcr_ terms as well as target_ terms, and amplificationReactionVolume and amplificationReactionVolumeUnit.

The main terms that are important for the quantification information and are different from the metabarcoding dataset are baselineValue, thresholdQuantificationCycle and quantificationCycle. The terms pcr_primer_lod, pcr_primer_loq, probeQuencher, probeReporter are additional terms specific for qPCR assays. The baselineValue indicates the number of cycles below which the signal is considered only background noise. The quantificationCycle is the most important and indicates at which cycle the particular sample crossed the detection threshold, this will be different for each sample. It is recommended to record this information, but not all of this may be easily available.

4.6.6 OBIS Bioinformatics Pipline

OBIS recognizes the vast amount of data generated from marine DNA sampling, especially from eDNA sequencing. Thus we have been developing a bioinformatics pipeline to facilitate publication of this data into OBIS. The pipeline was initially developed for the PacMAN project (Pacific Islands Marine Bioinvasions Alert Network).

Broadly speaking, it creates a framework that receives raw sequence data from eDNA samples, cleans, aligns, classifies sequences, and finally outputs a DwC-compatible table. The pipeline is currently under development and for now only accepts CO1 data. It will be extended to include other genetic markers in the future. More details about the PacMAN pipeline can be found on its associated GitHub repository. Once fully online, we will provide guidelines on how to use the pipeline.

OBIS is developing guidelines and pipelines to accept other data types, such as: