j Ww | M - Mi ¢ Metabarcoding and Metagenomics 5: 207-217

DOI 10.3897/mbmg.5.67862
Metabarcoding & Metagenomics :

Data Paper 8

Revision and annotation of DNA barcode records for marine
invertebrates: report of the 8 iBOL conference hackathon

Adriana E. Radulovici!’, Pedro E. Vieira®’, Sofia Duarte*, Marcos A. L. Teixeira®”,
Luisa M. S. Borges* , Bruce E. Deagle° , Sanna Majaneva®’, Niamh Redmondé,
Jessica A. Schultz!’, Filipe O. Costa*?

Centre for Biodiversity Genomics, University of Guelph, Guelph, Canada

Centre of Molecular and Environmental Biology (CBMA), University of Minho, Braga, Portugal

Institute of Science and Innovation for Bio-Sustainability (IB-S), University of Minho, Braga, Portugal

L? Scientific Solutions, Geesthacht, Germany

Australian National Fish Collection, Commonwealth Scientific and Industrial Research Organisation, Battery Point, Tasmania, Australia
Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway

Department of Arctic and marine biology, UiT The Arctic University of Norway, Tromso, Norway

Smithsonian Institution Barcode Network, Smithsonian National Museum of Natural History, Washington, DC, USA

O ON DOF WYN

Department of Integrative Biology, University of Guelph, Guelph, Canada

Corresponding authors: Adriana E. Radulovici (aradulov@uoguelph.ca), Filipe O. Costa (fcosta@bio.uminho.pt)

Academic editor: Fedor Ciampor Jr | Received 4 May 2021 | Accepted 8 December 2021 | Published 16 December 2021

Abstract

The accuracy of specimen identification through DNA barcoding and metabarcoding relies on reference libraries containing records
with reliable taxonomy and sequence quality. The considerable growth in barcode data requires stringent data curation, especially in
taxonomically difficult groups such as marine invertebrates. A major effort in curating marine barcode data in the Barcode of Life Data
Systems (BOLD) was undertaken during the 8" International Barcode of Life Conference (Trondheim, Norway, 2019). Major taxo-
nomic groups (crustaceans, echinoderms, molluscs, and polychaetes) were reviewed to identify those which had disagreement between
Linnaean names and Barcode Index Numbers (BINs). The records with disagreement were annotated with four tags: a) MIS-ID (mis-
identified, mislabeled, or contaminated records), b) AMBIG (ambiguous records unresolved with the existing data), c) COMPLEX
(species names occurring in multiple BINs), and d) SHARE (barcodes shared between species). A total of 83,712 specimen records
corresponding to 7,576 species were reviewed and 39% of the species were tagged (7% MIS-ID, 17% AMBIG, 14% COMPLEX, and
1% SHARE). High percentages (>50%) of AMBIG tags were recorded in gastropods, whereas COMPLEX tags dominated in crusta-
ceans and polychaetes. The high proportion of tagged species reflects either flaws in the barcoding workflow (e.g., misidentification,
cross-contamination) or taxonomic difficulties (e.g., synonyms, undescribed species). Although data curation is essential for barcode
applications, such manual attempts to examine large datasets are unsustainable and automated solutions are extremely desirable.

Key Words

annotation, data curation, DNA barcoding, marine invertebrates, metabarcoding, reference libraries

Introduction barcoding and metabarcoding, and therefore, a critical

component in molecular biomonitoring and molecular
Reference libraries, which are collections of compliant taxonomy (Weigand et al. 2019). The number of DNA
DNA sequences assigned to species, constitute the back- sequences and species included in reference libraries
bone of species identification systems based on DNA _ has increased dramatically over the last 15 years (Porter

* Current address: McGill University, Montreal, Canada (adriana.radulovici@mcgill.ca).

Copyright Adriana E. Radulovici et al.. This is an open access article distributed under the terms of the Creative Commons Attribution > PENSUFT.

License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and
source are credited.

208

and Hayjibabaei 2018). To date, the ever-growing librar-
ies have been deposited mostly in two large and public
molecular databases, namely (i) GenBank (Sayers et al.
2021), a repository with data usually released after publi-
cation, and (11) the Barcode of Life Data Systems (BOLD,
Ratnasingham and Hebert 2007), a workbench in which
data can be validated and analyzed before being released.
Additional databases do exist, but they are smaller in size
and usually created for specific purposes (e.g., zooplank-
ton identification, Bucklin et al. 2021).

Along with this expansion, reports of inaccurate or dis-
cordant data have become more common (Meiklejohn et
al. 2019, Ramirez et al. 2020, Fontes et al. 2021), especial-
ly when comparing data of morphological and molecular
origin. This is recognized as a critical concern for the ac-
curacy of existing and future DNA-based biomonitoring
(Leese et al. 2018, Grant et al. 2021). Whereas any data
discordances or inaccuracies in reference libraries would
be more apparent using the “conventional” low-through-
put DNA barcoding, they can be easily overlooked when
applying high-throughput metabarcoding, where automat-
ed bioinformatics tools assign taxonomy to millions of
sequences (Cristescu 2014). Because taxonomic assign-
ments for metabarcoding data often do not undergo further
scrutiny, any faulty assignments due to inaccurate refer-
ence library data can be repeated over and over without
being noticed (Leese et al. 2016). Since conflicting find-
ings cannot typically be controlled by algorithms, errors in
the reference libraries for a particular taxon (i.e., incorrect
sequence assigned to a species) might invalidate genuine
sequences. The long-term consequences of this erroneous
data for biomonitoring and ecological evaluation might
be considerable, especially with the planned installation
of semi-automated and large-scale metabarcoding-based
biomonitoring systems (e.g., BIOSCAN, Hobern 2021).

Data discordances in reference libraries have multiple
origins that can be split into two broad categories. First,
there are discordances that are due to real biological com-
plexities. Some of these are merely a reflection of the
inherent uncertainties and dynamics of alpha taxonomy
(e.g., Padial and De La Riva 2006, 2020). Others result
from a mismatch between morphological and molecular
diagnosis of species boundaries, that is, species that can
be identified morphologically but not through short DNA
sequences, or vice versa, for intrinsic reasons. The second
category of discordances are those introduced through
operational errors. For instance, morphology-based spec-
imen misidentifications have been often reported as a
source of error (Lis et al. 2016, Pentinsaari et al. 2020).
The experience level of the identifier may play a role in
such errors, but often taxonomic keys, drawings and de-
scriptions are incomplete or have poor quality, contrib-
uting to misidentifications. This may occur in the case
of some species that are frequently, but incorrectly, re-
ported as cosmopolitan species across the world because
the original description is imprecise enough to include a
variety of other species that remain undetected (Gomez
et al. 2007, Padial and De la Riva 2020). Taxonomic

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Adriana E. Radulovici et al.: Hackathon on marine invertebrates

variants such as synonyms and alternate representation
designating the same taxon, are an additional source of
mismatches (e.g., Magallana gigas and its alternate rep-
resentation, but widely used name, Crassostrea gigas,
Salvi et al. 2014, Bayne et al. 2017, Backeljau 2018).
These types of inaccuracies and limitations are custom-
arily shared and experienced by biodiversity databases
(Bidartondo 2008, Patterson et al. 2010, Meiklejohn et
al. 2019). Data discordances due to operational errors are
also known to arise during collection, sampling and labo-
ratory procedures, such as specimen and/or tissue sample
mislabeling, cross-contamination, or non-targeted PCR
amplification (Buhay 2009, Siddall et al. 2009, Evans and
Paulay 2012). Poor sequence quality, sequencing errors
or usage of reverse strand sequences may also contribute
to discordances (reviewed by Pentinsaari et al. 2020).

A workbench such as BOLD, where data can be easily
corrected if needed, brings great value to the barcoding
and metabarcoding pipelines. Several tools for automated
data quality control have been implemented in BOLD, in-
cluding flags to indicate if sequences of barcode markers
(COI, Matk, RbcL, RbcLa, trnH-psbA, ITS, ITS2) are
barcode compliant or if the protein-coding genes include
stop-codons or common contaminants (e.g., human, cow,
mouse, pig, bacteria). In addition, several analytical tools
allow data congruence verification. For instance, discord-
ances between species names attributed by BOLD users
and the Barcode Index Numbers (BINs, Ratnasingham
and Hebert 2013), which are automatically assigned to
COI sequences uploaded in BOLD, can be easily re-
vealed through the BIN discordance report. While auto-
mated bioinformatic procedures can readily include flags
and verification tools (e.g., Andujar et al. 2021), some
inconsistencies will eventually require human-mediated
inspection and judgement. In fact, if a potential misiden-
tification or contamination is detected, the BOLD team
review data and flag records for exclusion from the iden-
tification engine (BOLD IDS). The sheer volume and di-
versity of data to be handled, on the other hand, preclude
a complete examination and necessitate a more structured
and feasible approach.

Reference libraries have been populated in part
through dispersed contributions, despite a few central
core facilities providing major inputs (e.g., Canadian
Centre for DNA Barcoding, https://ccdb.ca). As a result,
DNA sequence data, and respective metadata, which are
uploaded to genetic data repositories such as BOLD or
GenBank, have varied components and levels of com-
pliance. The research practice also differs among target
taxonomic groups, affecting even the type of vouchering
system and metadata typically collected and accompany-
ing each specimen (Rimet et al. 2021). The diversity of
contributors and the peculiarities of their research prac-
tices create chances for operational discordances and
shortcomings, resulting in greater difficulties in reference
library revision and curation.

The rationale for reviewing barcode data for marine 1n-
vertebrates is particularly relevant. Marine invertebrates

Metabarcoding and Metagenomics 5: e67862

are often studied as a community and are one of the cus-
tomary targets for marine biomonitoring using metabar-
coding (Duarte et al. 2021). Some taxonomic groups or
geographical regions have reference libraries that are far
from being comprehensive (McGee et al. 2019, Leite
et al. 2020). For instance, only 22%—48% of European
marine species, depending on the species list used, had
at least one barcode in BOLD in 2019 (Weigand et al.
2019). Furthermore, it was estimated that a robust pop-
ulation of DNA reference libraries may require even
decades in some cases (Vieira et al. 2021). Beyond these
difficulties, data inaccuracies or discordances in marine
invertebrates might be caused by the more incipient sta-
tus of the overall taxonomic knowledge and various other
difficulties including the taxon-specific research practice
and reduced number of available taxonomic experts,
compared to other more well-studied groups such as ver-
tebrates and insects (Radulovici et al. 2010, Mammola et
al. 2020). As a result, a complete human-mediated review
and annotation of barcode data might be extremely bene-
ficial for marine invertebrates.

To accomplish this ambitious goal, of manually curat-
ing marine invertebrate barcode data, a hackathon was
organized in the scope of the 8" International Barcode of
Life (IBOL) conference (Trondheim, Norway, 2019). A
group of researchers involved in marine invertebrate bar-
coding were convened with the purpose of undertaking
a comprehensive review and annotation of the barcode
records of the most representative marine invertebrate
taxa currently available in BOLD. The choice to focus
exclusively on this platform was based on it being the
largest database designed primarily for DNA barcodes
and their metadata, the existence of analytical tools em-
bedded in the platform, and the routine process of mining
data from GenBank into BOLD, thus ensuring that all
DNA barcodes are hosted in one place and circumventing
the preference of various researchers for different data re-
positories. This is a report on the approach, findings and
implications for issues related to the curation of reference
libraries of DNA barcodes.

Methods

BOLD is a global database structured by few mandatory
fields (e.g., phylum, country of collection, and institution
storing voucher specimens), including habitat as an op-
tional field overlooked in many records, therefore a spe-
cific workflow (Fig. 1) was developed to consolidate only
marine data for curation purposes. A copy of the World
Register of Marine Species (WoRMS), the most com-
prehensive species list for the marine realm, was down-
loaded on June 1, 2019. Only accepted species names for
extant marine animals were retained, and further filter-
ing was performed to reduce the list to those invertebrate
taxa that are mostly used in marine biomonitoring: crus-
taceans, echinoderms, molluscs, and polychaetes. For
practical reasons to facilitate the curation process during

209

the hackathon, only limited groups were selected in the
final list: Crustacea (Malacostraca: Amphipoda, Isopoda,
Mysida, and Euphausiacea), Echinodermata (all classes),
Mollusca (Bivalvia and Gastropoda) and Polychaeta (all
orders). The resulting subset of species from WoRMS
was then compared against BOLD, and matched species
names had their public BOLD records added to pre-made
BOLD datasets. Initially split by taxonomy (phylum),
some of the larger datasets (e.g., molluscs) were further
divided to reduce the number of records reviewed by one
person during the hackathon. Only records assigned to
a BIN (Barcode Index Number) were retained in order
to use BIN-based analytical tools available in BOLD.
BINs are persistent clusters generated periodically and
algorithmically for COI sequences with certain quality
standards (<1% ambiguities, > 500 base pairs in length)
and can be visualized through individual web pages. All
public records within each BIN were included in the re-
view. For each dataset, a BIN discordance report was
generated, as well as a neighbour-joining tree (NJ tree)
with matching specimen details and images, if available,
for each sequenced organism.

The revision workflow (Fig. 1) consisted of manu-
ally inspecting the discordant BINs (1.e., one BIN with
records bearing different species names) together with
the NJ tree, followed by searching molecular databases
(GenBank and BOLD) and published articles to gather
additional information. Those records deemed uncertain,
based on the investigation conducted, were annotated
with one of four pre-established tags:

a) MIS-ID (misidentification or contamination) — re-
cords believed to be misidentified, mislabeled or
contaminated,

b) AMBIG (ambiguous) — records that could not be re-
solved with the existing data,

c) COMPLEX (species complex) — records belonging
to species with multiple BINs and, therefore, indica-
tive of hidden or undescribed diversity,

d) SHARE (shared barcodes) — records belonging to
species known to be sharing barcodes, due to incom-
plete lineage sorting or hybridization, based on exist-
ing literature.

Each uncertain record was annotated with only one tag.
MIS-ID tags were considered the most important since
all unflagged records are used for BOLD IDS, therefore
they took precedence in cases where one record was fall-
ing under multiple tags (e.g., MIS-ID and COMPLEX).
Since tags were applied to records and species were usu-
ally represented by multiple records, it follows that while
any given record can have only one tag, each species may
have multiple tags.

The hackathon included only the inspection of COI
sequences and not the inspection of morphological spec-
imens stored around the world, resulting in a small de-
gree of uncertainty related to the general findings. For in-
stance, if a BIN included dozens to hundreds of sequences

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210

WeRMs

World Register of Marine Species

Filter: Select invertebrate taxa
Matched species names WoRMS-BOLD

BOLD public records
for the hackathon

Revision of records with
BOLD analytical tools

BIN discordance
NJ tree

Annotation of uncertain records

AMBIG

COMPLEX

MIS-ID | SHARE

Figure 1. Workflow employed for the review and annotation
of selected marine invertebrate records in BOLD. A subset of
targeted invertebrate taxa was created from the initial list down-
loaded from WoRMS. This list was cross-referenced with the
available taxonomic list from BOLD. Subsequently, only public
BOLD records assigned to a BIN were integrated in datasets and
screened with two analytical tools (BIN discordance report and
neighbour-joining (NJ) tree). Records deemed to be uncertain
were annotated with four pre-established tags: MIS-ID (mis-
identification, mislabeling or contamination), AMBIG (ambig-
uous record), COMPLEX (species complex), SHARE (barcode
sharing between species). Records suspected to be misidentified
or contaminated were annotated and subsequently removed by
the BOLD team from the BOLD identification engine (BOLD
IDS). Records deemed reliable were not annotated.

of species A and one sequence of species B, the record of
species B was tagged as MIS-ID although other possibil-
ities are also viable (species B is correct and species A is
incorrect; they are both incorrect; they are both correct, in
case of unknown shared barcodes). All the records tagged

Adriana E. Radulovici et al.: Hackathon on marine Invertebrates

as MIS-ID were submitted to the BOLD team so they can
also be flagged and removed from the database used for
BOLD IDS. BOLD allows all its users to insert tags as
a tool for data curation by the barcoding community. In
contrast, flags can only be added by the BOLD team since
they affect BOLD IDS. All flags and tags can be removed
by the BOLD team, if necessary.

While detailed attention was given to discordant BINs,
records in concordant BINs (1.e., BINs including records
bearing only one species name) and singleton BINs
(i.e., BINs represented by only one record) were also
reviewed, especially in cases of species with multiple
concordant BINs (COMPLEX tag). Singletons were not
annotated unless they were part of a species complex. The
review of records (1.e., assignment of tags as well as ad-
ditional notes) was recorded directly in the spreadsheets
generated by BOLD as matching files for the NJ trees.
Formulas were inserted to summarize the findings (num-
ber of records tagged, number of records and species per
tag type, and number of taxa reviewed at each taxonomic
rank). Due to the large amount of data requiring verifica-
tion and the short time available, the work initiated dur-
ing the hackathon continued during the months following
the event. The results were illustrated through bar graphs
using GraphPad Prism 9.0 (San Diego, CA, USA).

All the records reviewed can be found in BOLD (dx.
doi.org/10.5883/DS-HACK2019 and dx.doi.org/10.5883/
DS-MOLL2019), and all the files with annotations are
available in the Suppl. material 1: Tables S1—S10.

Results

The initial WoRMS download had over 600,000 names
from all taxonomic levels, but only approximately
200,000 names were accepted animal species names.
Further filtering to invertebrate taxa of interest reduced
the species list to 79,251 names as follows: Crustacea —
15,148 species, Echinodermata — 7,404 species, Mollusca
— 44 °883 species, and Polychaeta — 11,816 species. Only
a small percentage of these species (about 10%) had bar-
code representation in BOLD (Table 1).

Globally, the hackathon effort resulted in the review
of 83,712 DNA barcode records, distributed across 8,465
BINs, corresponding to 7,576 marine invertebrate species
from four phyla, 115 orders, 595 families and 2,490 genera
(Table 1). Mollusca was by far the largest phylum tackled
during the hackathon (around 50,000 records), thus it was

Table 1. Distribution of the reviewed DNA barcode records among the major taxonomic groups, taxonomic ranks and BINs ana-
lyzed, together with the number of tagged (MIS-ID, AMBIG, COMPLEX, SHARE) DNA barcode records and species.

Taxonomic Group Phyla Orders Families Genera Species BINs DNA barcode records Tagged species Tagged records
Bivalvia Mollusca 26 71 279 741 672 10,194 330 5,088
Gastropoda Mollusca 38 233 1,066 3,982 4,235 39,749 1,582 15,581
Crustacea Arthropoda 5 107 349 828 1,129 12,647 290 6,443
Echinodermata Echinodermata 34 123 447 1,053 1,228 12,756 390 6,155
Polychaeta Annelida 12 61 349 972 1201 8,366 365 3,434
Total 4 115 595 2,490 7,576 8,465 83,712 2,957 36,701

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Metabarcoding and Metagenomics 5: e67862

split into two separate groups, Bivalvia and Gastropoda,
for all the subsequent analyses presented here.
Gastropoda was the taxonomic group with the high-
est number of reviewed records (47.5%) and the highest
number of species (53%) in the dataset (Table 1, Fig. 2).
On the other hand, Polychaeta was the taxonomic group
with the lowest number of reviewed records (ca. 10%),
whereas Bivalvia was the group displaying the lowest
number of species, comprising about 10% of the total
number of species in the dataset (Table 1, Fig. 2).

10000 Mmm Species
[3 BINs
8000 [ Discordant BINs

E= Singletons

No. of species/BINs/
singletons

Taxonomic group

Figure 2. Number of species, BINs, discordant BINs, and sin-
gletons (species with only one DNA barcode record) for all
groups analyzed and for each major taxonomic group separate-
ly. Numbers above bars indicate the percentage of discordant
BINs and singletons, respectively.

The number of BINs was highest in Gastropoda and
lowest in Bivalvia (Table 1, Fig. 2), and the ratio of BINs/
species was above 1.0 in all groups, except Bivalvia (0.9).
This indicates that the occurrence of species displaying
multiple BINs was prevalent in most groups (Table 1,
Fig. 2).

Across the entire dataset reviewed, approximately 22%
of BINs displayed discordance (Fig. 2). The highest num-
ber of discordant BINs was found in Gastropoda, and the
lowest in Bivalvia. Echinodermata was the group display-
ing the highest discordance in their BINs (ca 27%) and
Crustacea the lowest (ca 14%) (Fig. 2). Approximately

Total=7576

211

39% of the total number of analyzed species were single-
tons, 1.e., represented by a single DNA barcode record on
BOLD (Fig. 2). Echinodermata and Gastropoda were the
taxonomic groups with the highest percentage of single-
tons (41 and 42%, respectively), whereas the lowest was
recorded in Crustacea (29%) (Fig. 2).

Nearly 39% of all species in the dataset were deemed
uncertain (Fig. 3) and tagged with one of the four initially
defined tags: MIS-ID (7%), AMBIG (17%), COMPLEX
(14%) and SHARE (1%). Gastropoda had the highest pro-
portion of tagged species (ca 54%) and Crustacea the low-
est (ca 10%). Globally, the majority of the species were
tagged with AMBIG (ca 44%), and the value ranged from
ca 30% to 50%, for Crustacea and Gastropoda, respective-
ly (Fig. 4A). About 35% of the tagged species were as-
signed a COMPLEX tag, with a higher percentage found
among Polychaeta (59%), Crustacea (ca 58%) and Echi-
nodermata (43%) and the lowest within Bivalvia and Gas-
tropoda (ca 25% for each class) (Fig. 4A). On the other
hand, only about 18% and 2% of the tagged species were
classified as MIS-ID and SHARE, respectively (Fig. 4A).
The highest proportion of the MIS-ID tag was recorded
in Bivalvia (ca 29%), and the lowest in Polychaeta (9%).
For the SHARE tag, the highest percentage was observed
in Gastropoda (ca 4%), whereas Echinodermata and Poly-
chaeta had no species tagged with SHARE (Fig. 4A).

Approximately 44% of all reviewed DNA barcode re-
cords were tagged with one of the four initially defined
tags: MIS-ID (3%), AMBIG (10%), COMPLEX (29%)
and SHARE (2%) (Table 1, Fig. 4B), with Gastropoda
displaying the highest proportion of tagged records (ca
42%) and Polychaeta the lowest (ca 9%). Globally, most
records were tagged with COMPLEX (ca 66%), and the
value ranged between ca 50% and 92% for Gastropoda
and Crustacea, respectively (Fig. 4B). About 23% of the
total tagged records were AMBIG, with the highest pro-
portion observed in Gastropoda (ca 33%) and Bivalvia
(25%) and the lowest in Crustacea (ca 3%) (Fig. 4B).
On the other hand, only about 7% and 4% of the tagged
records were classified as MIS-ID and SHARE, respec-
tively (Fig. 4B). The highest percentage of the MIS-ID
tag was found in Bivalvia (ca 20%), and the lowest in

3%

MIS-IB
AMBIG
COMPLEX

SHARE
NO TAG

i

it i

Total=83712

Figure 3. Distribution of the proportion of different tags in the reviewed dataset, in terms of species (A) and DNA barcode records
(B). The total number of species (A) and records (B) are added below the chart.

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212

50

No. of species

Adriana E. Radulovici et al.: Hackathon on marine invertebrates

66

20000

10000 23

No. of records

51

EF MIS-ID
= AMBIG
[3 COMPLEX
Mmm SHARE

Taxonomic group

Figure 4. Distribution of the MIS-ID, AMBIG, COMPLEX, and SHARE tags among the major taxonomic groups, considering
either the total number of species (A) or the total number of DNA barcode records (B). Numbers above bars indicate the percentage
recorded within the whole tagged dataset and within each major taxonomic group.

echinoderms (1%). Concerning the SHARE tag, the high-
est proportion was recorded in Gastropoda (ca 7%), and
no records were tagged with SHARE in Echinodermata
or Polychaeta (Fig. 4B).

Discussion

The hackathon on marine invertebrate barcodes fulfilled
a variety of purposes beyond the immediate verification
of the congruence between morphology and molecular
data, and subsequent revision and annotation of records
submitted to BOLD. To our knowledge, it constituted the
first initiative of its kind for invertebrates (though see
Nilsson et al. 2018 for similar efforts in other taxa), acting
as a model initiative for such activities in other taxonomic
groups in the future. It also offered a first-of-its-kind hu-
man-mediated assessment of the taxonomic congruence
status of publicly available barcode data in BOLD.

The investigation highlighted an important proportion
of BOLD marine records that may lead to erroneous spe-
cies identification, particularly those records tagged with
MIS-ID and AMBIG (24% of reviewed species), in the
context in which only a fraction (10%) of the world ma-
rine invertebrate species (of taxa of interest here) had any
representation in BOLD. On the other hand, the revision

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also identified a relatively high proportion of species har-
boring undescribed intraspecific diversity (14% of spe-
cies tagged as COMPLEX). Species with MIS-ID tags
constituted a relatively small portion among the full set
reviewed here (7%), although it is possible that some
AMBIG records are MIS-ID but could not be fully re-
solved with the information available (see also discussion
further below regarding AMBIG). The review uncovered
substantial differences in the proportion of MIS-ID be-
tween taxonomic groups, with incidence percentages up
to five to six times greater in Bivalvia and certain Gas-
tropoda groups. This finding suggests that continued ef-
forts to audit these two groups in particular are required.
MIS-ID tags were below 4% in the remaining taxonomic
groups. Despite the fact that misidentifications are not a
very concerning fraction of the records, depending on the
taxonomy and context of the research where the data is
used (e.g., detection of non-indigenous marine species,
see Bortolus 2008), attempts to detect them should con-
tinue. More than 2,700 records (over 500 species) of ma-
rine invertebrates were flagged and removed from BOLD
IDS as a result of the hackathon, avoiding mistakes from
being perpetuated in future research that rely only on a
molecular identification system. However, the hope is that
most records will be corrected in time (e.g., based on tax-
onomic identification of voucher specimens) and the flags

Metabarcoding and Metagenomics 5: e67862

removed. Whereas a taxonomic update can be conducted
very easily by the data owner or the BOLD team for re-
cords originating in BOLD, a major issue concerns the
records mined from GenBank into BOLD. Such records
cannot be easily manipulated and were found to be an
important source for BIN discordances during the hack-
athon. For instance, BOLD:AAC4712 was composed of
27 records identified as Jalitrus saltator and originating
from three research labs in Canada, Germany and Portu-
gal, hence identified by different specialists, and one re-
cord mined from GenBank and identified as 7alorchestia
sp. 1 HL-2018. Regardless of the interim species name
status, in itself a source of BIN discordances, the record
was deemed to be MIS-ID due to the genus level mis-
match, hence flagged by the BOLD team and removed
from BOLD IDS. In this case, the probability of a correc-
tion and removal of the flag is slim since it would need a
complex operation in two databases (morphological ver-
ification of the voucher specimen, taxonomic correction
in GenBank by one of the authors of the study generating
that sequence, followed by taxonomic correction of the
mined record by the BOLD team).

The fact that the majority of AMBIG tags were applied
to uncertain data was not surprising because the review
took a conservative approach, and this tag was used as a
last resort when no other tag could be reliably assigned.
This might have inflated the number of AMBIG tags that
would have been assigned to other categories if this pre-
cautionary approach had not been taken, but it 1s impos-
sible to ascertain to what extent. On the other hand, a de-
tailed taxa-partitioned inspection of the AMBIG records
unraveled a highly unbalanced distribution, with some
particular taxonomic groups like Nudibranchia, Littorin-
imorpha and Pulmonata contributing disproportionately
to the global numbers of tagged species (26%, 31% and
54%, respectively; Suppl. material 1: Fig. S1), whereas
in some other groups the proportion of AMBIG tags was
comparatively low and even supplanted by other catego-
ries (e.g., Crustacea and Polychaeta). As a side note, while
Pulmonata is no longer a valid taxon in WoRMS, tt is still
considered an order in BOLD, hence its use here. Littorin-
imorpha and Nudibranchia are particularly speciose taxa
(e.g., 6,479 and 2,429 species respectively, WORMS, June
1, 2019) with some unsorted taxonomic riddles, which
underwent numerous taxonomic revisions resulting in a
high number of synonymies (e.g., Littorina obtusata (Lin-
naeus, 1758) with more than 50 synonyms in WoRMS).
Among the many Littorina species represented in BOLD,
Littorina aleutica and L. natica were found in the same
BIN (BOLD:ACB8366) and since no additional informa-
tion was found regarding barcode sharing, the correspond-
ing records were tagged as AMBIG. BOLD 1s performing
routine taxonomic updates based on WoRMS taxonomy,
therefore synonyms were not solved during this review to
avoid interference in operational procedures.

It is important, but challenging, to discern between mis-
leading data resulting from errors in the barcoding work-
flow, and inaccurate data resulting from a lack of basic

23

taxonomic knowledge, unsolved taxonomic conundrums,
unrecognized synonyms or a taxon’s status being in flux.
Some of the AMBIG tags may result from misidentifica-
tions, while others may simply indicate unsolved taxon-
omies that, if sorted out, may reveal congruence between
molecular and non-molecular data. Eventually, part of the
molecular data may even be evidencing the “true” species
boundaries currently masked by complex morphological
traits. AMBIG tagged records should therefore be taken
as a signal for caution in their use unless the end-user
can find additional information for their clarification. A
potential solution would be to avoid species-level identi-
fication when using these tags, giving preference to high-
er rank assignments (although even errors at these ranks
cannot be excluded with certainty). Recognition of taxo-
nomic groups which have a large number of AMBIG tags
could provide a focus for more detailed taxonomic work
to clarify the status of various species.

The COMPLEX< tag is the second most prevalent over-
all, but it is also the only one that does not necessarily
preclude the accurate identification of specimens. It sim-
ply signals cases where possible undescribed intraspecific
diversity was found. While usually COMPLEX meant a
Species split into two BINs, some cases of multiple splits
were also found (e.g., Capitella neoaciculata with five
BINs or Paracorophium excavatum with 15 associat-
ed BINs). Occurrences of multiple and highly divergent
intraspecific lineages have been abundantly and increas-
ingly reported in diverse groups of marine invertebrates,
suggesting the existence of considerable hidden diversity
(e.g., Nygren and Pleijel 2011, Lobo et al. 2017, Borges
and Merckelbach 2018, Nygren et al. 2018, Desiderato et
al. 2019, Teixeira et al. 2020). Several studies employed
additional markers, mitochondrial and nuclear, essentially
confirming the patterns observed with DNA barcodes (e.g.,
Borges and Merckelbach 2018, Hupato et al. 2019, Vieira
et al. 2019). Despite the fact that most phyla and class-
es displayed values close to 10% or higher, the Crustacea
(particularly Amphipoda) and Polychaeta had a higher and
substantial proportion of species tagged with COMPLEX,
even more than MIS-ID and SHARE (Fig. 4), indicating
that this appears to be a common occurrence across the
examined taxa. Curiously, the Gastropoda displayed com-
paratively low values with this tag. This may reflect in fact
lower incidence of high-intraspecific divergences in this
group but may also result in part from truly COMPLEX
tags masked in the AMBIG category due to the high num-
ber of taxonomic discrepancies in the group.

Although not so critical for the accuracy of identifica-
tions, at least according to the current status of taxonomic
knowledge, there are important aspects of the COMPLEX
tag to consider. Most notably, it helps when perceiving the
overall quantity of presumptive marine invertebrate spe-
cies awaiting verification and eventual consolidation and
description. Failing to recognize this considerable amount
of hidden diversity may be just as detrimental for bio-
assessment and monitoring as the MIS-ID or AMBIG cases
(Bickford et al. 2007). In general, very little is known about

https://mbmg.pensoft.net

214

potential biological and ecological differences among the
highly divergent lineages, some of them confirmed spe-
cies. Some of the species assigned with COMPLEX are
prominent indicator species included in biotic indexes for
bioassessment (e.g., AZTI Marine Biotic Index species list,
Borja et al. 2000, https://amb1.azti.es/) and their overlooked
intraspecific diversity may contribute to imprecisions and
lower performance of the ecological status assessments.

A number of marine invertebrates with cosmopolitan
or wide distributions are being discovered to be complex-
es comprising several units with narrower or restricted
distributions (e.g., Gomez et al. 2007, Borges and Merck-
elbach 2018, Teixeira et al. 2020). Divergent lineages are
frequently grouped together spatially, allowing genetic
markers to be utilized to identify not just morphospecies
but also regional or local lineages (e.g., Hupato et al.
2019, Vieira et al. 2019). The comprehensive detection
of highly diverse intraspecific lineages is required for an
accurate sense of changes in species ranges and occur-
rence, particularly in the scope of global change-induced
responses. Those with smaller ranges are also more 1m-
portant in terms of biodiversity conservation (Bickford et
al. 2007), as well as the consideration of marine protected
zones and other conservation measures.

Species and records tagged with SHARE are by far the
lowest proportion globally and within each taxonomic
group. SHARE tags are associated with situations of low
interspecific divergence coupled with incomplete sorting
and haplotype sharing, as well as hybridization and intro-
gression. As a result, rather than a reference library issue
arising from flaws in the barcoding procedure, these indi-
cate situations where the COI barcode sequences are unable
to differentiate species based on values of genetic distanc-
es. They can, however, be used in situations of fully sorted
and well-established species with records in the same BIN,
sometimes separated by very low genetic distances. Either
way, these results indicate that the occurrence of SHARE
cases is minimal and can be promptly identified, or, in the
latter case, circumvented through the accumulation of re-
cords into the libraries and refinement of the BIN assign-
ment for that particular group. Gastropoda was again the
group with the highest incidence of SHARE tags, reinforc-
ing the perception that greater research effort is needed for
taxonomic clarification of marine members of this group.
As an example, Littorina saxatilis (BOLD:AAG1552) was
found to share barcodes with L. compressa and L. arca-
na. Previous studies using various mitochondrial markers
(NADHI1, tRNApro, NADH6 and partial cytochrome b by
Doellmann et al. 2011, COI by Borges et al. 2016) found
L. saxatilis to be non-monophyletic with two mitochondri-
al lineages shared with the two sister species.

The BIN discordance report generated in BOLD is a
highly valuable validation tool which easily highlights
uncertain cases in need of careful examination. However,
concordant BINs are not exempt from misidentification,
especially less represented BINs, with barcodes from one
project, thus probably identified by one person, or from mul-
tiple projects where BOLD users did not hold taxonomic

https://mbmg.pensoft.net

Adriana E. Radulovici et al.: Hackathon on marine invertebrates

information for their specimens and relied solely on the ex-
isting information in BOLD which, if erroneous initially,
could have been propagated into their projects. In addition,
singletons are very difficult, if not impossible, to verify.
As the hackathon data included about 30-40% singletons
for each taxonomic group investigated, it is possible that a
larger proportion of the current marine data might need to
be tagged with one of the four labels discussed above.

The challenges found in evaluating barcode data, par-
ticularly marine barcode data, point to the need for bet-
ter practices when generating, analyzing, and publishing
barcode data. BIN discordances owing to synonyms
might be avoided with greater synchronization between
WoRMS and BOLD. Interim species names, whether de-
rived from original BOLD records or data mined from
GenBank and accounting for a substantial proportion of
BIN discordances, would benefit from being checked us-
ing BOLD IDS on a regular basis and updated if matches
are found (although difficulties of taxonomic updates for
GenBank-mined data have been already mentioned).

Conclusions

The one-day hackathon and the following months of an-
notation work contributed significantly to the curation of
the BOLD DNA barcode reference libraries for key ma-
rine invertebrate groups. Although numerous significant
taxonomic groups were omitted from analyses, it was still
a massive undertaking that required the individual review
and annotation of a large number of records and species.
Despite the significant efforts, the hackathon only pro-
vided a snapshot of BOLD marine data from June 2019.
Records that were flagged or tagged during the hackathon
would ideally be cleared in a short period of time, by a
coordinated effort by BOLD data owners together with
the BOLD team, allowing them to be included in reliable
and trustworthy barcode libraries.

Ideally, this kind of event should be repeated on a reg-
ular basis, in tandem with the addition of new entries to
reference libraries. However, as a corollary of this enter-
prise, it was very evident that the immense effort required
to complete this task cannot be underestimated, and that it
could hardly be repeated in the same format.

Indeed, a much more practical approach is needed in
future endeavors, and this pilot exercise provided some
possible solutions to substantially simplify the review pro-
cedure. For instance, recent applications such as BAGS
(Fontes et al. 2021), could help package data based on
quality and prepare it for the review. On the other hand,
a long-term solution would include the development of
intelligent systems that can screen out the most obvious
discordances and misleading records, and thus dispense
human-mediated verification. The results presented here
indicate that a considerable fraction of the discordances
could benefit from such developments. The outstanding
capabilities of machine learning (ML) and artificial intel-
ligence (AI) systems have been extensively demonstrated

Metabarcoding and Metagenomics 5: e67862

in various research fields (Davenport and Kalakota 2019)
including in image-based species identification (e.g., Arje
et al. 2020, Hoye et al. 2021) and in the analysis of me-
tabarcoding data for ecological status assessment (e.g.,
Cordier et al. 2019, Fritthe et al. 2020). Nonetheless, it
was also evident from our results that there would always
be a fraction of discordances that cannot be addressed
through automated systems and would thus require hu-
man intervention to be properly annotated.

Therefore, whereas ML and Al-type of approaches
may help to considerably reduce the number of records
requiring review, turning hackathon-like initiatives into
practical and feasible commitments, at the end of the line
there will be the need for human-mediated verification
at least, and hopefully, for a minor set of records. In this
regard, DNA barcode reference libraries are no different
from other biodiversity data, and, ideally, strategies for
data curation through community involvement, similar
to the community of editors curating taxonomic data on
WoRMS, could be used as inspiration and transposed to
the DNA barcoding practice.

Acknowledgements

The hackathon was organized with financial support from
the European Union COST Action DNAqua-Net (CA
15219 https://dnaqua.net/) in the scope of the 8" Interna-
tional Barcode of Life Conference in Trondheim, Norway
on 16 June 2019. DNAqua-Net is acknowledged for the
funding provided and the local conference organizers for
all the logistical support that ensured a successful event.
Tyler Elliot and the rest of the BOLD team are acknowl-
edged for their help with data queries and analytics. The au-
thors also thank the hackathon participants for vibrant dis-
cussions during and after the event: Berry van der Hoorn,
Katrine Konsghavn, Guy Paz, Mouna Rifi, Malin Strand,
Anne Helene Tandberg, Adam Wall, and Endre Willassen.
Marcos A. L. Teixeira was supported by a PhD grant from
the Portuguese Foundation for Science and Technology
(FCT IP.) co-financed by ESF (SFRH/BD/131527/2017).
Financial support granted by FCT to Sofia Duarte (CEEC-
IND/00667/2017) and to Pedro E. Vieira (project NIS-
DNA, PTDC/BIA-BMA/29754/2017) is also acknowl-
edged. Sanna Majaneva was financially supported by the
Norwegian Taxonomy Initiative (project no. 70184235).
The authors thank the five reviewers who provided valu-
able input into the earlier version of the manuscript.

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Supplementary material 1

Figure S1 and Tables S1—S10

Author: Adriana E. Radulovici, Pedro E. Vieira, Sofia Duarte,
Marcos A. L. Teixeira, Luisa M. S. Borges, Bruce E. Dea-
gle, Sanna Majaneva, Niamh Redmond, Jessica A. Schultz,
Filipe O. Costa

Data type: Image and tables (in zip. archive)

Explanation note: Figure S1. Number of species tagged with
AMBIG within each order in the Gastropoda. Table S1. Am-
phipoda. Table S2. Bivalvia. Table S3. Gastropods1. Table
S4. Gastropods2. Table S5. Gastropods3. Table S6. Gastro-
pods4. Table S7. Gastropods5. Table S8. Crustacea. Table
S9. Echinodermata. Table S10. Polychaeta.

Copyright notice: This dataset is made available under the
Open Database License (http://opendatacommons.org/li-
censes/odbl/1.0/). The Open Database License (ODbL) is
a license agreement intended to allow users to freely share,
modify, and use this Dataset while maintaining this same
freedom for others, provided that the original source and
author(s) are credited.

Link: https://do1.org/10.3897/mbmg.5.67862.suppl1

https://mbmg.pensoft.net