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\begin{document}

\title{Coming of age for COI metabarcoding of whole organism community DNA:
towards bioinformatic harmonisation}



\author[1]{Thomas Creedy}%
\author[2]{Carmelo Andujar}%
\author[3]{Emmanouil Meramveliotakis}%
\author[2]{Victor Noguerales}%
\author[4]{Isaac Overcast}%
\author[3]{Anna  Papadopoulou​}%
\author[4]{Hélène Morlon}%
\author[1]{Alfried Vogler}%
\author[2]{Brent Emerson}%
\author[2]{Paula Arribas}%
\affil[1]{Natural History Museum}%
\affil[2]{Instituto de Productos Naturales y Agrobiologia}%
\affil[3]{University of Cyprus}%
\affil[4]{CNRS}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
Metabarcoding of DNA extracted from community samples of whole organisms
(whole organism community DNA, wocDNA) is increasingly being applied to
terrestrial, marine and freshwater metazoan communities to provide
rapid, accurate and high resolution data for novel molecular ecology
research. The growth of this field has been accompanied by considerable
development that builds on microbial metabarcoding methods to develop
appropriate and efficient sampling and laboratory protocols for whole
organism metazoan communities. However, considerably less attention has
focused on ensuring bioinformatic methods are adapted and applied
comprehensively in wocDNA metabarcoding. In this study we examined over
600 papers and identified 111 studies that performed COI metabarcoding
of wocDNA. We then systematically reviewed the bioinformatic methods
employed by these papers to identify the state-of-the-art. Our results
show that the increasing use of wocDNA COI metabarcoding for metazoan
diversity is characterised by a clear absence of bioinformatic
harmonisation, and the temporal trends show little change in this
situation. The reviewed literature showed (i) high heterogeneity across
pipelines, tasks and tools used, (ii) limited or no adaptation of
bioinformatic procedures to the nature of the COI fragment, and (iii) a
worrying underreporting of tasks, software and parameters. Based upon
these findings we propose a set of recommendations that we think the
wocDNA metabarcoding community should consider to ensure that
bioinformatic methods are appropriate, comprehensive and comparable. We
believe that adhering to these recommendations will improve the
long-term integrative potential of wocDNA COI metabarcoding for
biodiversity science.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Metabarcoding of DNA extracted from community samples of whole organisms
(whole organism community DNA, wocDNA) is a reliable and cost-efficient
tool to study the biodiversity of metazoan communities (Bush et al.,
2019; Ji et al., 2013; Porter \& Hajibabaei, 2018). This approach, which
has also been referred to as community DNA (e.g. And\selectlanguage{ngerman}újar et al., 2018b;
Deiner et al., 2017) or bulk sample DNA (e.g. Braukmann et al., 2019; Yu
et al., 2012) metabarcoding, primarily differs from other approaches
such as eDNA (environmental or extra-organismal DNA; Taberlet et al.,
2012) or iDNA (vertebrate DNA ingested by invertebrates; Schnell et al.,
2012) in that the source material is a community of whole organisms
collected through direct trapping or collection (e.g. malaise traps Ji
et al., 2013, canopy fogging Creedy et al., 2019) or separated from an
environmental sample (e.g. from soil Arribas et al. 2016 or water Suter
et al., 2020). As a consequence, compared with eDNA and iDNA, wocDNA
samples are characterised by (i) a comparatively low level of DNA
degradation in the target species, (ii) a low proportion of non-target
species, and (iii) the possibility for complementing, refining and/or
validating metabarcoding-derived community data against other
conventional morphological and molecular methods.

Metabarcoding of wocDNA samples is increasingly employed in community
ecology, evolutionary ecology, biogeography, conservation biology,
environmental management, and policy and decision-making (e.g. Bush et
al., 2020; deWaard et al., 2019; Leese et al., 2018). Metazoan wocDNA
metabarcoding has been adapted from pioneering approaches developed to
inventory and characterise microbial diversity (e.g. Gilbert et al.,
2010; Sogin et al., 2006).~ The majority of these adaptations have
focused on sampling, and molecular laboratory steps, including adapted
protocols to (i) sample, separate, enrich and/or clean animal wocDNA
samples (Creedy et al., 2019; Fonseca et al., 2010, 2011), (ii) perform
wocDNA extractions (Marquina et al., 2019; Nielsen et al., 2019), (iii)
design and evaluate primers (Braukmann et al., 2019; Elbrecht \& Leese,
2017, Elbrecht et al. 2019), optimise amplification (Krehenwinkel et
al., 2017) and prepare libraries (Yang et al., 2020). There is a growing
consensus on the use of the mitochondrial cytochrome oxidase subunit I
(COI) barcode, rather than other markers widely used for metabarcoding
of non-metazoan communities, as the standard for wocDNA metabarcoding
due to the range of COI primers with demonstrated efficiency (Braukmann
et al., 2019; Elbrecht \& Leese, 2017), and the potential of COI to
improve the utility, resolution and reliability of wocDNA metabarcoding
data (Andújar et al., 2018a; Turon et al., 2020).~

However, in contrast to these advances in sampling and molecular
processing, there has been limited effort to review and evaluate how
bioinformatic processing has been adapted to metazoan wocDNA samples and
the COI barcode, nor to examine consistency in bioinformatic approaches
across the field. Broadly, bioinformatic tasks involve the computational
cleaning, filtering and analysis of raw sequence data to produce
biodiversity data comprising taxonomic units and their incidence across
samples, implemented in a particular order (a `pipeline'). There are a
wide array of software tools available for performing different
bioinformatic tasks, from standalone tools to catch-all software
packages (e.g. OBItools Boyer et al., 2016; QIIME Caporaso et al., 2010;
USEARCH/UPARSE Edgar, 2013; and its open-source derivative VSEARCH
Rognes et al., 2016). This software has been largely developed for
metabarcode loci other than the COI region, with very few tools
explicitly developed for protein coding metabarcodes (although see
Andújar et al., 2021; Nugent et al., 2020; Ramirez-Gonzalez et al.,
2013). To fully capitalise on the COI barcode for metabarcoding,
bioinformatics should be specifically tailored to its evolutionary
properties, such as the ability to interrogate the amino acid
translation, and accounting for established patterns of sequence
variation in protein coding genes for strict filtering. Additionally,
metabarcoding employs a~ number of key bioinformatic tasks for which
multiple alternative algorithms have been developed (e.g. denoising
algorithms), with considerable variation in outcomes depending on
parameters and thresholds applied.

The structure of a bioinformatic metabarcoding pipeline will depend
strongly on the research aim, amplification and sequencing protocols,
target locus, and target biodiversity fraction. The diversity of
bioinformatic tasks and the software approaches to implement them is of
course beneficial for designing appropriate pipelines, but such
heterogeneity may also restrict integrated, standardised and synergistic
growth in the field. As metazoan wocDNA metabarcoding becomes more
accessible to researchers from a range of fields and backgrounds,
harmonisation of bioinformatic approaches is important to ensure (i)
high-quality, reproducible data amenable to qualitative or~ quantitative
reviews and meta-analysis across studies, and (ii) a reliable,
consistent methodology for wider implementation, development and
expansion of wocDNA metabarcoding. We consider harmonisation not to mean
strict prescription of the tasks and software to use, nor their order.
Instead a harmonised field would recognise the diversity of approaches
available, while recording key steps and establishing the effects of
parameter choice on the outcome of metabarcoding studies. This approach
could be enabled by the adoption of universal aligned standards for data
generation and processing, while allowing for flexibility in
implementation to adapt to varying research goals and take advantage of
novel methodological development.~

Harmonisation requires comprehensive examination of current practice to
understand the aims and approaches of prior work, and a synthesis of the
successes and failures in past implementations for the purposes of
elaborating a framework to guide future research. Therefore it is our
aim to summarise the state of the art for bioinformatic processing of
metazoan wocDNA COI metabarcoding, and in doing so assess the potential
for~ harmonisation. To this end, we performed a systematic review of
peer-reviewed studies, collating information on the different
bioinformatic pipelines, tasks and tools used in wocDNA COI
metabarcoding in \textgreater{}100 recent studies (2011-2020). We use
this data to (i) describe the diversity, heterogeneity and
reproducibility~ of the bioinformatic procedures followed, (ii) identify
the extent to which these procedures are compatible with the
evolutionary properties of the COI marker, and (iii) identify the key
bioinformatic tasks, provide a framework for successful metabarcoding
bioinformatics and make recommendations towards harmonised bioinformatic
procedures for metazoan wocDNA COI metabarcoding.

\textbf{Materials and Methods}

\emph{Bibliographic search and screening}

We focused this work on studies using whole organism community DNA
(wocDNA) metabarcoding. In general, we define wocDNA samples as those
where the target organisms were: (i) likely alive at the time of
sampling, (ii) present as a largely complete specimen, and (iii)
potentially identifiable using classical methods of morphological
analysis. We exclude eDNA and iDNA metabarcoding due to the potentially
different bioinformatic processing needs associated with these samples.
In particular, eDNA and iDNA bioinformatic methods need to accommodate
degraded DNA and a potentially high proportion of non-target reads.
Furthermore, in many cases wocDNA metabarcoding is directly comparable
to direct observation of specimens and conventional methods of taxonomic
assignment not available for eDNA metabarcoding (Ji et al. 2013, Aylagas
et al. 2016). This allows for more objective stringency thresholds in
bioinformatic filtering and delimitation of operational taxonomic units
(OTU).

We conducted a systematic search of peer-reviewed studies in the Web of
Science (WOS) Core Collection (Science Citation Index Expanded,
1900-present) on 3rd November 2020, using the search ``TS =
(metabarcoding) NOT TS = (*micro* OR *bacteria* OR *myco* OR *archaea*
OR fungi OR plant OR eDNA OR environmental DNA)''. These search
parameters were selected in order to obtain~ a comprehensive set of
wocDNA metabarcoding studies limited to Metazoa.~

The systematic search resulted in 692 records, which were screened to to
select only those studies that: (i) amplified some portion of the
standard COI barcode ``Folmer'' region (Folmer et al., 1994), (ii) fit
our definition of wocDNA samples, comprising mixtures of organisms
extracted from the substrate, and (iii) provided a characterisation of
metazoan communities. Studies targeting extra-organismal DNA (i.e. eDNA,
iDNA) were excluded. We included studies of experimental mock
communities composed of mixtures of DNA extracted from individual
specimens or mixtures of specimens, and we also included studies where
the target organisms remained partially or completely within an
environmental substrate upon which DNA extraction was performed (e.g.
parasites within a host, arthropods within soil), if the principal
target was the whole organism community DNA. After reviewing the final
set of filtered papers, 24 additional papers fitting the selection
criteria but not present in the systematic WOS search were also
included. A total of 111 articles constituted the set of core papers for
subsequent assessment (see Table S2 for a complete list).

\emph{The core papers}

All papers were systematically processed to record (i) the research aim
and type of samples analysed; (ii) the bioinformatic tasks and pipelines
implemented; and (iii) the software tools used and the reproducibility
of the bioinformatic procedures employed. A detailed description of this
process is provided in the Supplementary Methods and is summarised as
follows.

The research aim was categorised according to whether the focus was (i)
the comparison of molecular and/or bioinformatic procedures for
metabarcoding, (ii) a proof-of-concept or feasibility study into the
success of metabarcoding for uncovering accurate community data in the
taxon/community/biome studied, or (iii) principally the study of
ecological patterns and processes. We recorded whether the metabarcoded
communities were sampled from marine, freshwater, terrestrial biomes or
from a host species, and finally if the targets were invertebrates or
vertebrates.~

Subsequently, the bioinformatic procedures for each paper were
systematically parsed to identify the different tasks implemented, i.e.
specific bioinformatic actions with a clearly-defined purpose and
performed by a single tool. A total of 30 distinct bioinformatic tasks
were identified starting from initial procedures on raw sequencing files
through to the generation of community tables (see Table 1 for a
description of each task). We focused solely on bioinformatic tasks that
were presented as necessary for the generation of information about the
occurrence or incidence of taxonomic units in the sampled communities
(i.e. community data), and the taxonomic identity of these units. For
example, we did not record any steps performing phylogenetics with a
final OTU set, although we recorded steps where phylogeny-based methods
were used as part of OTU delimitation and filtering. Similarly, we
recorded tasks that performed filtering of community data for the
purposes of removing OTUs or OTU records arising from erroneous
sequences or from cross-talk/contamination (Edgar, 2018), but we did not
record tasks that filtered community data for the purposes of
statistical correction, such as normalisation or rarefaction.

Once the different tasks implemented by each article were identified,
the pipeline used (i.e. the specific sequence of tasks in a particular
order), was also recorded based on the order in which the different
tasks were mentioned in the text, figures, supplementary material and/or
cited papers. Where multiple mutually exclusive tasks were employed for
the purposes of comparison of pipelines, we recorded that pipeline that
the authors concluded to be empirically superior, or from which the
authors used the output data for subsequent analysis.

For each of the bioinformatic tasks identified across the papers, we
calculated (i) the number of papers implementing the task, (ii) the
task's relative position within the pipelines, (iii) the information
reported on the software, version and parameters used, and (iv) the
homogeneity in the software tools used to implement the task. We
assessed homogeneity by calculating two indices, the software
homogeneity rate and the software dominance rate (see Fig 5). Finally,
we also summarised temporal trends in both the reporting and software
heterogeneity of each task.{}

\textbf{Results and Discussion}

\emph{Diversity~ of bioinformatic methods}

The 111 selected papers were published in 36 different journals with a
broad focus on ecology and molecular ecology. There has been a steady
increase in the number of papers published in this domain over time
(Fig. 1). The earliest year of publication was 2011, but 77\% of all
papers were published in the last four years (2017-2020, n = 86, Fig.
1). Almost all papers studied invertebrate communities (n=108).
Forty-five papers were focussed on terrestrial communities, 31 on
freshwater, 30 on marine and five on parasite communities collected from
a host vertebrate (see Table S2 for all the details on the core papers
set).~

Despite a clear trend for increased use of wocDNA COI metabarcoding, the
field remains in a relatively early stage of implementation, reflected
in the fact that in half of all papers (n=56, n=38 in the last four
years) metabarcoding was undertaken as a proof-of-concept and the
authors primarily discussed the feasibility of this method for the
studied ecological system. Only 25 papers considered the sample sizes
and metabarcoding procedures sufficiently rigorous to answer ecological
questions. Thirty papers were primarily methodological, assessing the
influences of primer choice, lab protocols and/or sequencing methods.
However, within the methodological category, no paper solely studied the
effect of bioinformatic pipeline choices. Indeed, only eight out of the
111 papers clearly stated that they compared different bioinformatic
tools for the same task, despite the use of 116 discrete pieces of
software or functions in our final count. These results illustrate the
timely nature of this review, highlighting the inconsistent
implementation of bioinformatic methods, in contrast to the relative
maturity and harmonisation of field and laboratory methodologies.

\emph{High heterogeneity in tasks and pipelines}

The~ variety of bioinformatics pipelines reported across the 111 papers
employed 108 unique pipelines, i.e. sets of bioinformatics tasks carried
out in a specific sequence. Three pipelines were used twice; in two of
these cases, a group of authors replicated their pipeline exactly, in
the other case the pipeline as reported consisted solely of a single
step of searching raw reads against a reference set. Although some of
these pipelines were similar, with minor modifications to the order, or
the addition/removal of a few tasks, the heterogeneity of pipelines is
remarkable. There was also high heterogeneity in the number of tasks
implemented within each pipeline, ranging between 1 and 18 tasks, with
half of the articles reporting fewer than 9 distinct bioinformatic tasks
(Fig. 2a). There was no~ particular trend in the number of tasks
implemented over time (Fig. 2b). The order in which~ these tasks were
implemented also differed greatly~ (Fig. 2c), although there was a
tendency for certain tasks to be performed within similar general stages
within pipelines, that is, read preparation-based tasks tend to be
implemented at the initial steps of the pipelines, followed by
filtering-based tasks and data generation tasks (Fig. 3).~

~~~ Heterogeneity in the sequence of tasks may reflect the careful
design and adaptation of bioinformatic procedures within each study to
the type and structure of sample and sequence data and/or the specific
research question, rather than the simple duplication of previously
published pipelines. However, high heterogeneity may equally result from
the omission of important tasks or their inappropriate implementation
within the pipelines, and so result in low comparability, integration~
and replication across studies. One clear example of this is associated
with the Filtering tasks of removal of erroneous sequence reads.
Denoising (i.e. the removal of sequencing errors based on models of
error frequency parameterised by between-sequence similarity, error
sensitivity and/or relative frequency), was employed in just 18 studies
and its relative position within the pipelines was highly variable (see
Table 1 and Fig. 3). While some sequencing errors will be disregarded
during OTU clustering, failure to incorporate denoising can lead to
false OTUs and thus OTU inflation (Shum \& Palumbi, 2021) Furthermore,
the trend towards examining haplotypic variation in metazoan wocDNA
metabarcoding through use of amplicon sequence variants (ASVs, Callahan
et al., 2017) requires minimising the number of spurious sequences,
relying on stringent filtering such as denoising. Similarly, filtering
to remove sequences with low copy number (that are often considered
highly likely to be erroneous) was reported in only half (n=57) of the
studies, despite being generally recommended (Calderón\selectlanguage{english}-Sanou et al.,
2020; Ficetola et al., 2017) and a critical step for reducing spurious
sequences surviving denoising including nuclear mitochondrial (NUMT,
Lopez et al., 1994) copies (Andujar et al., 2021). It should be noted
that while many task absences are cases of under-implementation, some
may also be underreporting (see below).

\emph{Infrequent adaptation of pipelines to COI}

The COI locus differs from many other metabarcoding loci (e.g. 18S, 16S,
12S, ITS) in that it is a protein coding gene, imparting strict
expectations of amplicon sequence read properties that can be exploited
in metabarcoding bioinformatics (Andujar et al., 2018b). However, the
adaptation of pipelines to this fragment are in general rarely
implemented in the papers of the core set. For example, only 22 papers
(20\%) used amino acid translations to identify erroneous sequences
(``translation filtering''),~ using 11 different software tools for the
task. The reason for low implementation of translation filtering is
likely twofold; first, none of the major metabarcoding software packages
include functions for translation filtering, and second, there is no
standard straightforward command line software for undertaking this
task. Those papers that carry out translation filtering do so by using
one of three main approaches: (i) sequences are viewed and translated in
a GUI application such as Geneious (\url{https://www.geneious.com}) or
MEGA (Kumar et al., 2018), and those with stop codons manually removed,
(ii) sequences are processed through a custom script, some of which are
available on github but none of which are used by research groups
separate from the author, and (iii) sequences are aligned against
references using MACSE (Ranwez et al., 2011) and those containing indels
or stop codons are~ removed. The first option is time consuming and
prone to human error, and custom scripts are challenging to document and
maintain for a wider number of users. While MACSE is the most frequent
single approach, it is computationally efficient only for small
datasets. There may be some potential in the recent coil R package
(Nugent et al., 2020) that uses Hidden Markov Models to identify and
filter translation-based errors and appears to scale well to large
datasets, although the R implementation presents a slight barrier to
efficient inclusion in pipelines. Furthermore, the majority of
translation filtering approaches are based solely on removing stop
codons, while there may be other potential avenues for filtering based
on amino acid translation. The extent to which expectations for protein
structural properties can be applied to metabarcoding sequences for
filtering other non-synonymous errors has been underexplored (but see
Turon et al., 2020).

In addition to the potential of amino acid translation, the protein
coding nature of COI leads to relatively stricter expectations of
amplicon length. However, only half (n=54) of papers reported using
length filtering, despite this being a relatively trivial procedure and
with functions available in all metabarcoding software packages and as
options in many more software tools. There may be some underreporting
here; given the implementation of a length filtering parameter in many
software tools that have a different primary purpose, authors may not
have explicitly reported that length thresholds had been applied as part
of a different procedure (note that we recorded when a single tool was
reported to have fulfilled multiple tasks). Despite length filtering
being widely available, and the relative algorithmic simplicity of
implementation, there are no length filtering tools that allow for
specification of thresholds outside of a simple minimum-maximum range,
despite the internal barcode region of protein coding genes generally
being expected to vary in length only by multiples of 3 bases. While
trivial to implement this programmatically for an experienced
bioinformatician, this lack of straightforward user-friendly
availability presents a barrier to appropriate threshold implementation
by those with less experience.

Severe underreporting and increasing heterogeneity in the tools used for
bioinformatic tasks

Of the 30 bioinformatic tasks identified (see Table 1 for a description
of the tasks), only 11 were implemented in more than half of the papers
(n\textless{}55) (Fig. 3). Quality filtering (n=92) and OTU delimitation
(n=89) were the tasks most reported. Some of the less reported tasks
were those associated with uncommon bioinformatic requirements of
metabarcoding data, such as assembly or degapping; others have become
redundant with modern computational power, such as preclustering. Low
reporting of such tasks is likely an accurate reflection of rare
implementation; however, there are many other tasks that are fundamental
in metabarcoding bioinformatics but are poorly reported. For example,
primer trimming was only reported by just over half of the papers
(n=67), yet is a completely necessary step. Similarly, adapter trimming
was underreported (n=21); while it is likely that in the majority of
cases this is implemented by sequencing facilities prior to the authors
receiving data, its reporting, including parameters and tools used, is
fundamental to verify stringency of the read preparation procedures. The
mapping of by-sample reads to OTUs was reported by only one third (n=30)
of the papers that employed OTU delimitation, despite this being a
necessary step for the production of ecological data for downstream
analysis. Furthermore, OTU mapping is not a trivial step; the level of
filtering/processing performed on the reads used for mapping (as opposed
to filtering/processing performed on the sequences used for OTU
delimitation), and the similarity threshold and tie-breaking algorithm
employed to assign reads to OTU clusters could all substantially affect
the community data generated. The accurate reporting of this step is
important to assess the validity of a pipeline, its comparability across
studies, and/or its ability to be reproduced.

In addition to the clear underreporting of tasks within the pipelines as
discussed above, the reporting of the bioinformatic tools and parameters
used for those tasks cited in the papers was also very poor (Table 1).
Only 21 of the 111 papers reported software name, version and parameters
used for all of the bioinformatic tasks implemented, and 25 failed on
all three counts (Fig. 4a). When considering the degree of
underreporting by task (Fig. 4b), the most underreported software were
used for some of the most perfunctory tasks (e.g. frequency filtering,
length filtering, dereplication) that can be easily reproduced using
many equivalent tools. Nonetheless, there remains relatively widespread
underreporting, and this has remained unchanged over time (Fig. 5b).~

Within the reported software, we identified 93 software tools used in
metabarcoding bioinformatic pipelines (Table S3), of which 27\% (25)
were software `packages'. When taking into account distinct functions
within packages, a total of 169 unique tools were recorded, however,
this is likely an inaccurate picture given low reporting rates of
functions used within software packages across all steps. There is a
clear increase in the number of different software and software
functions employed across all papers over time (Fig. 5a). Examining the
diversity of software used within tasks over time, controlling for the
number of papers published, there is limited improvement in homogeneity
and a decrease in dominance of software (Figs 5c and 5d). Given that the
number of metabarcoding publications is increasing year-on-year, there
is thus a concomitant increase in the diversity of software used for a
given task, and previously commonly used software are being used less
(Figs 5c and 5d). These trends reflect that while new software tools are
constantly being made available for metabarcoding, uptake is not
consistent across the field and while some researchers use more recent
tools, many researchers continue to use older methods, diversifying the
field.~

\emph{Toward a bioinformatic harmonisation of COI metabarcoding for
metazoan wocDNA samples}

Our results show that the increasing use of wocDNA COI metabarcoding for
metazoan diversity is characterised by a clear absence of bioinformatic
harmonisation, and the temporal trends show little change in this
situation. The reviewed literature showed (i) high heterogeneity across
pipelines, tasks and tools used, (ii) limited or no adaptation of
bioinformatic procedures to the nature of the COI fragment, and (iii) a
worrying underreporting of tasks, software and parameters.~

The development of metabarcoding as a method for community ecology began
with microbial studies over a decade ago, which have revealed the
extensive diversity of bacteria and archaea on our planet and
demonstrated the potential of metabarcoding for global biodiversity
syntheses (Bates et al., 2013; Thompson et al., 2017). Although the
integration and meta-analysis of microbial community data from
independent studies is still challenging (e.g. Ramirez-Gonzalez et al.,
2013), the success of international consortia such as the Earth
Microbiome Project (EMP, Gilbert et al., 2010, 2014) has promoted the
development of a harmonised framework for data generation and analyses
within microbial eDNA research (see e.g. Tedersoo et al., 2015).~

Through the adaptation of the microbial metabarcoding method to wocDNA
samples, specific protocols to sample, sort and enrich community samples
for wocDNA metabarcoding have been developed, targeting different
taxonomic fractions and types of samples
\href{https://www.zotero.org/google-docs/?JlTJwX}{(e.g., Andujar et al.,
2018a; Arribas et al., 2016; Creedy et al., 2019; Elbrecht \& Leese,
2017; Fonseca et al., 2010; Yu et al., 2012)}. Additionally, recent
efforts to adapt and optimise existing methods are increasing efficiency
and versatility, for example through non-destructive DNA extraction
techniques that retain specimens for morphological vouchering
\href{https://www.zotero.org/google-docs/?DJOE3w}{(Marquina et al.,
2019; Nielsen et al., 2019)}, or library preparation techniques tailored
to metazoan samples
\href{https://www.zotero.org/google-docs/?UGxX48}{(Yang et al., 2020)}.
Although wocDNA COI metabarcoding remains in an expansive phase of
development, standardisation in field and laboratory methods are
emerging. This is in part boosted by collaborative initiatives such as
the BIOSCAN initiative and its regional extensions (e.g. BIOALPHA), the
Kruger Malaise Program, SITE-100, the Insect Biome Atlas Project,
LIFEPLAN, and iBioGen (Arribas et al., 2021).~

In contrast, there has been little advance in the development and
validation of best practices associated with the bioinformatics
processing of wocDNA COI metabarcoding data (but see Yang et al., 2020
for error reduction). Outside of taxonomic assignment, discussion of
customising or parameterising tools for the purposes of working with
wocDNA COI metabarcoding is very rare, with most papers simply reporting
using tools with default settings. Our review has revealed heterogeneity
in the number of tasks, the order of these within pipelines, and the
tools used to implement them, along with a lack of even basic
adaptations to the COI metabarcode for most of the papers. The majority
of available software and resources for metabarcoding bioinformatics are
still those that have been developed around the 16S rRNA gene (the
primary target for microbiome metabarcoding), including the most popular
software packages (e.g. USEARCH) and sets of wrapper scripts (e.g.
QIIME, OBItools).~ While in many cases these methods may carry over to
COI without issue, we observe very few studies that report consideration
or analysis that assesses or validates the suitability of software
choices for COI. These issues suggest that the expansion of wocDNA COI
metabarcoding is proceeding at a pace and manner that could lose sight
of or simply ignore the challenges inherent in producing high\selectlanguage{english}-quality
data and reproducible methods (Baker et al., 2016; Zinger et al., 2019),
and lose out on the potential for exploiting the benefits of the COI
marker for wocDNA metabarcoding of Metazoa.~

DNA metabarcoding has broad multidisciplinary potential, as demonstrated
by the expansion in use of metazoan wocDNA COI metabarcoding among users
from very diverse backgrounds. The diversity of applications of
metabarcoding requires the concomitant bioinformatic techniques to be
flexible and adaptable, and the field remains under active development.
Thus it would not be productive to attempt to prescribe pipelines, tasks
or even software tools in the name of standardisation, as there is no
one-size-fits-all approach in metabarcoding. However, some degree of
harmonisation is required to ensure quality, reproducibility and
potential integration in metastudies (Tedersoo et al., 2015).
Additionally, the absence of a harmonised framework of bioinformatic
processing can act as a barrier for potential new users (Liu et al.,
2020), hampering the growth of the field. To these ends, we thus propose
a set of recommendations that we believe all researchers in the field
should consider when designing and reporting their wocDNA COI
metabarcoding bioinformatics pipeline, with the hope that they will
catalyse harmonised implementation.

\textbf{Fully report all tasks, software, software versions and
parameters used, even if just the defaults.} Our results show that
underreporting is a recurrent problem. Comprehensive reporting of the
tasks, pipelines and software used is essential for further integrating
results in future reviews or meta-analyses (Tedersoo et al., 2015).
Furthermore, care should be taken to report not just the name of the
software package, but also the exact function, and if wrapper scripts
are used then the underlying functions should be reported. Considering
the trade-off with current constrictions for manuscript length, this
could be achieved by the inclusion of a supporting table following the
STAR-METHODs philosophy (Marcus, 2016), where task reference, order
within the pipeline and software used are included. Note that the task
lexicon and software lists compiled in this review (see Table 1) are a
very useful resource for this purpose. This reporting effort for all the
wocDNA COI metabarcoding will promote rigour and robustness with an
intuitive, consistent framework that makes reporting easier for the
author and replication easier for the reader.

\textbf{Implement filtering tasks such that spurious sequences are
sufficiently removed to meet the assumptions of the research question.}
The quality of metabarcoding results is likely to depend most on the
appropriate inclusion of filtering into a pipeline (Calderon\selectlanguage{english}-Sanou et
al., 2020; Elbrecht et al., 2018; Zinger et al., 2019), so proper
implementation of filtering tasks are critical for robust and harmonised
use of COI metabarcoding. In metabarcoding, real amplicon sequence
variants (ASVs, Callahan et al., 2017) amplified from target genes are
inherently accompanied by spurious sequences, arising from multiple
sources. Indeed, taxonomic inflation is a recurring issue demonstrated
in communities with known haplotype composition (Creedy et al., 2020;
Elbrecht et al., 2018). This can be exacerbated for mitochondrial
markers like COI, due to the co-amplification of NUMTs and other
non-authentic ASVs that are missed by denoising and require stringent,
optimised filtering based on read abundances such as that implemented by
the metaMATE software (Andujar et al., 2021). To ensure quality and
reproducibility, metabarcoding studies should consider implementing the
six most common filtering approaches, i.e Quality, Length, Chimera,
Translation, and Frequency filtering, plus Denoising. For each of these
tasks, appropriate thresholds should be considered, implemented and
fully reported to a level that ensures reproducibility. Given the
demonstrated importance of these tasks for most wocDNA metabarcoding
studies, if any are not employed by a study the omission should be
explained.

~\textbf{Adapt pipelines to the COI fragment.} Suitable adaptations
include read processing and filtering steps that leverage evolutionary
properties of the protein coding nature of this fragment, or determining
appropriate parameters for tools originally designed for other DNA
regions. Some recent advances have been made in filtering tasks
(metaMATE, Andujar et al., 2021; coil, Nugent et al., 2020;
entropy-based denoising, Turon et al., 2020) but further development in
these promising areas is essential to fully exploit the potential of the
COI gene for metabarcoding. As mentioned previously, there are no tools
that enable simple length filtering variation that accounts for
codon-level insertion or deletion. To our knowledge there is limited
work exploring the extent to which protein structure inference might
allow identification of erroneous sequences: for example the SOAPbarcode
pipeline (Liu et al., 2013) includes a script that filters sequences
based on translation hydrophilicity, but this is not comprehensively
documented or discussed in the associated publications. Computation of
protein structural properties is relatively trivial to perform, and
seems like a fertile ground for novel development of filtering tools for
protein coding markers.~

\textbf{For each task, consider all software available and try to select
the most appropriate tool(s).} This can only be approached with
sufficient information about available software, and to this end we
include a list of all software used for each task within Table S1, and
Table S3 includes links to documentation and publications. The selection
of the most appropriate tool is not always straightforward, but we
suggest considering (i) the extent to which the tool was designed for
the intended barcode region, purpose or dataset, (ii) the detail of
available documentation and explanation to ensure a tool performs as
expected, (iii) the availability and flexibility of options to
appropriately apply the tool, (iv) the frequency of use of a tool in
other studies with similar research aims, and (v) all else being equal,
the simplest approach. Ideally, where multiple approaches exist,
reasonable comparison between key methods should take place to fully
understand the potential variation in conclusions that might arise from
different bioinformatic choices, and the results of these comparisons
should be reported. This is particularly the case when considering
alternative, conceptually distinct algorithms for more bioinformatically
complex tasks, such as denoising and OTU delimitation. The development
of software packages and open access platforms integrating a catalogue
of common bioinformatic tools, such mBRAVE
(\url{http://www.mbrave.net/}), may play a fundamental role towards a
proper selection and harmonisation of the software used. However,
software choices should be made on the basis of appropriateness and
usefulness, rather than simply ease of availability and implementation
due to inclusion in these packages/platforms.~

\textbf{Verify the compatibility of the tasks within a pipeline,
especially with respect to task order.} It is important to ensure that
the assumptions of one task have not been violated by upstream
processing; for example, UNOISE denoising employs a model of error rates
in Illumina sequencing, and if errors have been removed by prior length
or frequency filtering this model may not accurately fit to the data.
Further, linked processes should be compatible: for instance, if OTU
delimitation is based on a linkage algorithm such as swarm (Mahe et al.,
2015), it is inappropriate to employ a simple similarity-based mapping
method to assign reads to the resultant OTUs.~

Aside from these recommendations, we also urge researchers to make data
publicly available, both raw reads and final ASV and/or OTU sequences.
Raw read datasets will become an invaluable resource for future work
integrating many wocDNA metabarcoding studies across spatial and
temporal scales, with continuing development and improvement of
bioinformatic pipelines allowing for forward-compatibility of the data
as analytical tools continue to evolve. Uploading ASV and/or OTU
sequences, even with incomplete taxonomy, improves the capability of
methods for taxonomic assignment that draw on these resources and
provides fertile datasets for future development of bioinformatic
methods.

\textbf{Conclusions}

The past decade has seen rapid growth in the development, testing and
use of wocDNA COI metabarcoding. Much effort has been expended in the
development of laboratory, sequencing and bioinformatic methodologies
for wocDNA COI metabarcoding and for metabarcoding as a whole. However,
while much progress has been made towards harmonisation of lab and
sequencing methods, bioinformatic processes have remained a tangle of
varying software, pipelines and theoretical approaches, often suffering
from underreported detail. This diversity allows for versatility,
especially for those who are well-informed and experienced in
bioinformatics and able to pick and choose the appropriate approach.
However, choosing from the range of approaches could easily hinder new
applications of metabarcoding for researchers coming from a limited
bioinformatic background, and high heterogeneity can stymie the
potential for future reviews and meta-analyses. Our review, which is the
first evaluating the state of the art on this topic, highlights that
this danger is clearly present in the field of metazoan wocDNA COI
metabarcoding. The results of our assessment and the recommendations
derived from it may help to improve bioinformatic harmonisation and thus
the long-term integrative potential of wocDNA COI metabarcoding for
biodiversity science.~

\textbf{Acknowledgements}

This research was supported by the iBioGen project, which has received
funding from the European Union's Horizon 2020 research and innovation
programme under grant agreement No 810729.

\textbf{Conflict of interest}

A.P.V. is a co\selectlanguage{english}-founder and scientific advisor of NatureMetrics, a
private company providing commercial services in DNA\selectlanguage{english}-based monitoring.
The authors declare that they have no other conflicts of interest.

\textbf{Author contributions}

T.J.C. and P.A, conceived the study. T.J.C. and P.A. assessed the
initial paper set for inclusion, T.J.C. evaluated the methods of the
core paper set and analysed the data. T.J.C. and P.A. wrote the initial
draft and all co authors contributed to the final manuscript.

\textbf{Data accessibility}

Supporting Materials (methods, figures and tables) give the full details
and methodological evaluation of the 111 publications making up the core
papers.

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