# A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses

Ali Hakimzadeh, Alejandro Abdala Asbun, Davide Albanese, Maria Bernard, Dominik Buchner, Benjamin Callahan, J. Gregory Caporaso, Emily Curd, Christophe Djemiel, Mikael Brandström Durling, et al.

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## To cite this version:

Ali Hakimzadeh, Alejandro Abdala Asbun, Davide Albanese, Maria Bernard, Dominik Buchner, et al.. A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses. Molecular Ecology Resources, 2023, 10.1111/1755-0998.13847. hal-04183770

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DOI: 10.1111/1755-0998.13847

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MOLECULAR ECOLOGY
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# A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses

Ali Hakimzadeh<fnref n="1" /> ID | Alejandro Abdala Asbun<fnref n="2" /> | Davide Albanese<fnref n="3" /> | Maria Bernard<fnref n="4" />,<fnref n="5" /> ID |

Dominik Buchner<fnref n="6" /> ID | Benjamin Callahan<fnref n="7" /> | J. Gregory Caporaso<fnref n="8" /> | Emily Curd<fnref n="9" /> ID |

Christophe Djemiel<fnref n="10" /> ID | Mikael Brandström Durling<fnref n="11" /> ID | Vasco Elbrecht<fnref n="6" /> ID |

Zachary Gold<fnref n="12" /> | Hyun S. Gweon<fnref n="13" />,<fnref n="14" /> | Mehrdad Hajibabaei<fnref n="15" /> ID | Falk Hildebrand<fnref n="16" />,<fnref n="17" /> ID |

Vladimir Mikryukov<fnref n="1" /> | Eric Normandeau<fnref n="18" /> | Ezgi Özkurt<fnref n="16" />,<fnref n="17" /> | Jonathan M. Palmer<fnref n="19" /> |

Géraldine Pascal<fnref n="20" /> ID | Teresita M. Porter<fnref n="15" /> | Daniel Straub<fnref n="21" /> | Martti Vasar<fnref n="1" /> ID |

Tomáš Větrovský<fnref n="22" /> | Haris Zafeiropoulos<fnref n="23" /> | Sten Anslan<fnref n="1" /> ID

**Correspondence**

Sten Anslan, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia.

Email: sten.anslan@ut.ee

**Present address**

Jonathan M. Palmer, Genencor Technology Center, IFF, Palo Alto, California, USA

<footnote n="1">¹Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia</footnote>

<footnote n="2">²Department of Marine Microbiology and Biogeochemistry, NIOZ Royal Netherlands Institute for Sea Research, Texel, Netherlands</footnote>

<footnote n="3">³Unit of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, Italy</footnote>

<footnote n="4">⁴INRAE, AgroParisTech, GABI, Université Paris-Saclay, Jouy-en-Josas, France</footnote>

<footnote n="5">⁵INRAE, SIGENAE, Jouy-en-Josas, France</footnote>

<footnote n="6">⁶Aquatic Ecosystem Research, University of Duisburg-Essen, Essen, Germany</footnote>

<footnote n="7">⁷Department of Population Health and Pathobiology, College of Veterinary Medicine and Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA</footnote>

<footnote n="8">⁸Center for Applied Microbiome Science, Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, Arizona, USA</footnote>

<footnote n="9">⁹Vermont Biomedical Research Network, University of Vermont, Burlington, Vermont, USA</footnote>

<footnote n="10">¹⁰Agroécologie, INRAE, Institut Agro, Univ. Bourgogne Franche-Comté, Dijon, France</footnote>

<footnote n="11">¹¹Department of Forest Mycology and Plant Pathology, Swedish University of Agricultural Sciences, Uppsala, Sweden</footnote>

<footnote n="12">¹²Zachary Gold, NOAA Pacific Marine Environmental Laboratory, Seattle, Washington, USA</footnote>

<footnote n="13">¹³UK Centre for Ecology & Hydrology, Oxfordshire, UK</footnote>

<footnote n="14">¹⁴School of Biological Sciences, University of Reading, Reading, UK</footnote>

<footnote n="15">¹⁵Department of Integrative Biology and Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, Canada</footnote>

<footnote n="16">¹⁶Gut Microbes & Health, Quadram Institute Bioscience, Norfolk, UK</footnote>

<footnote n="17">¹⁷Earlham Institute, Norwich Research Park, Norfolk, UK</footnote>

<footnote n="18">¹⁸Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, Québec, Canada</footnote>

<footnote n="19">¹⁹Center for Forest Mycology Research, Northern Research Station, US Forest Service, Madison, Wisconsin, USA</footnote>

<footnote n="20">²⁰GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France</footnote>

<footnote n="21">²¹Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany</footnote>

<footnote n="22">²²Laboratory of Environmental Microbiology, Institute of Microbiology of the Czech Academy of Sciences, Praha, Czech Republic</footnote>

<footnote n="23">²³KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, Leuven, Belgium</footnote>
**Funding information**

Deutsche Forschungsgemeinschaft (DFG), Grant/Award Number: EXC2124; European Regional Development Fund and the programme Mobilitas Pluss, Grant/Award Number: MOBTP198; Genome Canada and Ontario Genomics; Grantová Agentura České Republiky, Grant/Award Number: 21-17749S; National Institute of General Medical Sciences, Grant/Award Number: P20GM103449

Handling Editor: Simon Creer

**Abstract**

Environmental DNA (eDNA) metabarcoding has gained growing attention as a strategy for monitoring biodiversity in ecology. However, taxa identifications produced through metabarcoding require sophisticated processing of high-throughput sequencing data from taxonomically informative DNA barcodes. Various sets of universal and taxon-specific primers have been developed, extending the usability of metabarcoding across archaea, bacteria and eukaryotes. Accordingly, a multitude of metabarcoding data analysis tools and pipelines have also been developed. Often, several developed workflows are designed to process the same amplicon sequencing data, making it somewhat puzzling to choose one among the plethora of existing pipelines. However, each pipeline has its own specific philosophy, strengths and limitations, which should be considered depending on the aims of any specific study, as well as the bioinformatics expertise of the user. In this review, we outline the input data requirements, supported operating systems and particular attributes of thirty-two amplicon processing pipelines with the goal of helping users to select a pipeline for their metabarcoding projects.

**KEYWORDS**

amplicon data analysis, bioinformatics, environmental DNA, metabarcoding, pipeline, review

# 1 | INTRODUCTION

Advances in high-throughput sequencing (HTS) technologies have boosted the application of molecular methods for species identifications. Metabarcoding, the simultaneous tagging, sequencing, and identification of multiple species within a single environmental sample (Taberlet, Coissac, Hajibabaei, & Rieseberg, 2012) is now a widely applied technique in biodiversity research (Compson et al., 2020). Metabarcoding involves PCR-based amplification of taxonomically informative gene fragments ('DNA barcodes', markers) that are subsequently sequenced to be used for species identifications in the presence of reference sequence data (DNA barcodes). Before identification, the sequencing data are processed in several steps (Figure 1) where one of the first steps is usually performing quality control on the data. A sequence analysis pipeline is generated by applying various steps using a collection of software and algorithms with the ultimate goal of producing an accurate features table with potential taxon annotations by sample (i.e. with features metadata). In metabarcoding, features refer to amplicon sequence variants (ASVs), operational taxonomic units (OTUs) or annotated taxa; and their sample-wise distribution matrix can be further utilized in relevant biostatistical analyses.

With the emergence of practical guidelines (e.g. Bruce et al., 2021; Lear et al., 2018; Tedersoo et al., 2022), the scalability and throughput of environmental DNA (eDNA; a mixture of DNA from different organisms in an environmental sample; Taberlet, Coissac, Pompanon, et al., 2012) sample processing has contributed to the popularity of the metabarcoding approach among ecologists. However, one of the bottlenecks of metabarcoding is choosing how

to process sequencing data sets into relevant feature tables bioin-
formatically. Among the first highly successful software developed
for that purpose have been mothur (Schloss et al., 2009), USEARCH
(Edgar, 2010) and QIIME 1 (Caporaso et al., 2010), which consists of
algorithms that can be combined to create full metabarcoding data
analysis pipelines. Over the years, these programs have been sup-
plemented with additional algorithms to help reduce artefactual se-
quences and implement different sequencing clustering approaches.
These pipelines were initially developed for microbial 16S rRNA
amplicon analysis, but the applications of metabarcoding have been
expanded to a wide range of taxa from various environmental sam-
ples, resulting in a boom in pipeline development. Some workflows
include a set of newly designed algorithms, but others represent a
combination of different open-source tools used for the different
analysis steps bound into executable pipelines. From the lack of
easy-to-use bioinformatics tools from the early age of metabarcod-
ing, we have reached a phase where the choices are so numerous
that it may be difficult to select among the multitude of analytical
workflows.

Below, we delve into the properties of thirty-two software pack-
ages that can be used for the bioinformatics processing of metabar-
coding data. In this review, we outline several key aspects of those
metabarcoding software, including which ones represent software
suites or precompiled pipelines, consideration of the software de-
pending on the utilized sequencing platform, available operating
system and interface preference (Figure 2). By addressing these
components, we seek to offer a comprehensive understanding of
the software landscape for metabarcoding projects. Since differ-
ent users will have different needs, we do not seek to recommend
<figure><img src="image_4.png" /><figcaption>FIGURE 1 Examples of basic bioinformatics workflows for metabarcoding data. The workflow begins with demultiplexing, assigning reads to respective samples based on unique molecular identifiers. Next, quality filtering removes low-quality reads to reduce errors and improve reliability. Denoising algorithms identify and correct sequencing errors while preserving biological variation. For paired-end reads, merging combines forward and reverse reads into single-end sequences. Artifacts filtering aims to remove artifacts such as chimeras and NUMTs. Clustering groups of sequences into features. Finally, taxonomic assignment of the features against a reference database. * Primer trimming between any of these steps can be applied. *1 Only for paired-end data (may be performed before or after quality filtering). *2 Error correction; formation of ASVs. *3 Including chimera filtering, off-target gene removal (pseudogene removal, ITS extraction). *4 Formation of OTUs/swarm-clusters.</figcaption></figure>

the best-performing pipeline, a task that would be highly context-dependent, but rather to give an overview of the software that are available for metabarcoding data analysis. Table 1 lists the software discussed here and their extended description and specific capabilities are outlined in Appendix S1.

## 2 | SOFTWARE SUITES AND PRECOMPILED PIPELINES

The metabarcoding data processing software may be roughly divided into two categories based on their structure for executable algorithms—software providing a set of algorithms, and pipelines providing a predefined chain of algorithms. USEARCH, VSEARCH (Rognes et al., 2016), DADA2 (Callahan et al., 2016), OBITools (Boyer et al., 2016), mothur and QIIME 2 (Bolyen et al., 2019) are software suites that host numerous algorithms for sequence data analysis, thus are highly customizable to construct user-defined pipelines with a specific chain of commands and settings. VSEARCH largely mirrors the diverse functionalities of USEARCH, but without the requirement to purchase a licence for a version that can handle large data sets and use more than 4GB of a computer's memory. Besides consisting of a large set of unique data processing algorithms, mothur and QIIME 2 wrap some functionalities of VSEARCH and/or DADA2.

Software providing a predefined chain of algorithms represents full analytical pipelines with specific workflow steps, as depicted in Figure 1. The predefined pipelines consist of workflow steps validated on certain sequencing data to facilitate the metabarcoding

data analysis, which may be especially convenient for users with few bioinformatics skills. Some workflows include a set of newly designed algorithms, but others represent a combination of different open-source tools used for the different steps that are bound into easily executable pipelines. Although, these pipelines are predefined, they often allow the user to customize the settings depending on the characteristics of the sequencing data set.

## 3 | BASIC STRUCTURE OF A METABARCODING PIPELINE

### 3.1 | Demultiplexing

Demultiplexed sequences are often provided to users since this process has been integrated into sequencing provider software, such as bcl2fastq (for Illumina raw data) and SMRT Tools (for Pacific Biosciences [PacBio] reads). Demultiplexing distributes the sequences into individual files, most often corresponding to the experiment's samples. However, when requesting multiplexed data (pooled sequences from multiple samples), the reads from a sequencing run may need to be demultiplexed before using some of the application software. The demultiplexing step is not incorporated into all software (Table 1). In cases where it is not, other programs such as cutadapt (Martin, 2011), sdm (Falk Hildebrand et al., 2014) or lima (https://lima.how/; for single-end reads only) may be used. In cases where multiple markers are used per sample (via multiplex PCR), amplicons from different primer sets should also be split. The latter step is included in the Anacapa and VTAM pipeline
Single-end data

AMPtk Anacapa BIOCOM-PIPE Cascabel CoMA DADA2 dadasanke

eDNAflow FROGS gDAT JAMP LotuS2 MetaWorks MICCA mothur

NextITS nf-core/ampliseq OBITools PipeCraft2 QIIME 2 SCATA

SEED2 USEARCH VSEARCH Tourmaline VTAM

APSCALE
Barque
Dadaist2
DAnIEL
PIPITS
PEMA

Paired-end
data

<figure><img src="image_5.png" /></figure>

Linux

macOS

eDNAflow
dadasnake
MetaWorks
NextITS

AMPtk Barque BIOCOM-PIPE
Cascabel Dadaist2 JAMP
OBITools PIPITS VSEARCH

Anacapa APSCALE CoMA
DADA2 gDAT MICCA
mothur nf-core/ampliseq
PEMA PipeCraft2 USEARCH
Tourmaline VTAM

DAnIEL

FROGS LotuS2 QIIME 2

Windows

Web-based (including Galaxy)

SEED2

<caption>FIGURE 2 Software for metabarcoding data bioinformatics processing categorized by input read type (paired-end, single-end (the tools in electric blue are capable of handling both paired-end and single-end reads)), software type (suite, precompiled pipeline), interface (CLI, GUI, Web, Galaxy web platform), produced feature type (OTU, ASV, swarm-cluster) and operating system (Linux, macOS, Windows).</caption>

(Curd et al., 2019; González et al., 2023), where different markers are automatically separated based on the primer sequences.

3.2 | Primer trimming

Sequencing adapters, indexes and primers should be removed before the following analyses. Depending on the data structure, the former two may be absent, but the programs mentioned above (for demultiplexing) may be used to double-check this and remove primers. Adapter/primer clipping is often implemented into a pipeline by wrapping the cutadapt, Trimmomatic (Bolger et al., 2014) or AdapterRemoval (Lindgreen, 2012) functionality, in others done during quality filtering and demultiplexing steps (e.g. sdm; Table S1).

3.3 | Quality filtering & merging paired-end reads

The following phases of a standard DNA metabarcoding pipeline is sequence filtration based on the read quality scores, removal of putative chimeric/artefactual sequences, the definition of features (e.g. ASVs, OTUs) and taxonomic annotation of the features (Figure 1). In the case of paired-end data, the merging process of the overlapping sequences may be performed before or after the quality filtering step and even sometimes after the sequence clustering step. There are a multitude of strategies for performing the above-listed processes, where the selection of an approach may depend on the specific characteristics of the sequencing data or the aims of the study. The strategies for quality filtering include per-sequence or per-nucleotide(s)-based filtering. Per-sequence filtering includes
<table><caption>TABLE 1 A list of the reviewed metabarcoding data analyses pipelines (in alphabetical order).</caption><thead><tr><th>Pipelines</th><th>First/latest release</th><th>Software type</th><th>Feature</th><th>Demux</th><th>Primer/adapter removal</th><th>Marker</th></tr></thead><tbody><tr><td>AMPtk</td><td>Nov 2017/Jan 2023</td><td>Precompiled</td><td>ASV, OTU</td><td>Yes</td><td>Yes</td><td>16S, 28S, COI, ITS</td></tr><tr><td>Anacapa</td><td>June 2018/June 2018</td><td>Precompiled</td><td>ASV</td><td>Multilocus demux based on primers</td><td>Yes</td><td>Multimarker</td></tr><tr><td>APSCALE</td><td>Jan 2022/Feb 2023</td><td>Precompiled</td><td>ASV, OTU</td><td>No</td><td>Yes</td><td>Multimarker</td></tr><tr><td>Barque</td><td>Sep 2016/Dec 2022</td><td>Precompiled</td><td>ASV, OTU</td><td>No</td><td>Yes</td><td>Multimarker</td></tr><tr><td>BIOCOM-PIPE</td><td>Aug 2020/Jan 2021</td><td>Precompiled</td><td>ASV, OTU</td><td>Yes</td><td>Yes</td><td>16S, 18S, 23S</td></tr><tr><td>Cascabel</td><td>Nov 2020/Dec 2022</td><td>Precompiled</td><td>ASV, OTU, swarm-cluster</td><td>Yes</td><td>Yes</td><td>Multimarker</td></tr><tr><td>CoMA</td><td>Dec 2020/Jan 2022</td><td>Precompiled</td><td>ASV, OTU, swarm-cluster</td><td>No</td><td>Yes</td><td>Multimarker</td></tr><tr><td>DADA2</td><td>May 2016/Nov 2022</td><td>Set of algorithms</td><td>ASV</td><td>No</td><td>Yes</td><td>Multimarker</td></tr><tr><td>Dadaist2</td><td>May 2020/Jul 2022</td><td>Precompiled</td><td>ASV</td><td>No</td><td>Yes</td><td>Multimarker</td></tr><tr><td>dadasnake</td><td>Apr 2020/Feb 2023</td><td>Precompiled</td><td>ASV, OTU</td><td>No</td><td>Yes</td><td>Multimarker (with ITSx)</td></tr><tr><td>DAnIEL</td><td>Apr 2021/May 2021</td><td>Precompiled</td><td>ASV</td><td>Yes</td><td>Yes</td><td>ITS</td></tr><tr><td>eDNAflow</td><td>Jul 2020/Jan 2021</td><td>Precompiled</td><td>ASV</td><td>Yes</td><td>Yes</td><td>Multimarker</td></tr><tr><td>FROGS</td><td>Jan 2016/Apr 2023</td><td>Set of algorithms</td><td>ASV, swarm-cluster</td><td>Yes</td><td>Yes</td><td>Multimarker (with ITSx)</td></tr><tr><td>gDAT</td><td>Mar 2020/Jan 2021</td><td>Precompiled and set of algorithms</td><td>OTU</td><td>No</td><td>Yes</td><td>18S, ITS</td></tr><tr><td>JAMP</td><td>Jan 2017/Jan 2022</td><td>Precompiled</td><td>OTU</td><td>Yes</td><td>Yes</td><td>Multimarker (without taxonomic assignment)</td></tr><tr><td>LotuS2</td><td>May 2021/Apr 2023</td><td>Precompiled</td><td>ASV, OTU, swarm-cluster</td><td>Yes</td><td>Yes</td><td>Multimarker (16S, 18S, 23S, 28S, ITS with ITSx)</td></tr><tr><td>MetaWorks</td><td>Jun 2020/Mar 2023</td><td>Precompiled</td><td>ASV, OTU</td><td>No</td><td>Yes</td><td>Multimarker; SSU (12S, 16S, 18S), ITS (with ITSx), LSU (28S), COI (with pseudogene removal), rbcL (with pseudogene removal)</td></tr><tr><td>MICCA</td><td>Oct 2014/Aug 2018</td><td>Precompiled</td><td>ASV, OTU, swarm-cluster</td><td>Yes</td><td>Yes</td><td>16S, 18S, 28S, ITS</td></tr><tr><td>mothur</td><td>Feb 2009/May 2022</td><td>Set of algorithms</td><td>ASV, OTU</td><td>Yes</td><td>No</td><td>Multimarker</td></tr><tr><td>NextITS</td><td>July 2022/May 2023</td><td>Precompiled</td><td>ASV, OTU, swarm-cluster</td><td>Yes</td><td>Yes</td><td>ITS (with ITSx)</td></tr><tr><td>nf-core/ampliseq</td><td>Dec 2018/Mar 2022</td><td>Precompiled</td><td>ASV</td><td>No</td><td>Yes</td><td>Multimarker (with ITSx)</td></tr><tr><td>OBITools3</td><td>Sep 2019/Sep 2022</td><td>Set of algorithms</td><td>ASV</td><td>Yes</td><td>Yes</td><td>Multimarker</td></tr><tr><td>PEMA</td><td>Feb 2020/Dec 2021</td><td>Precompiled</td><td>OTU, swarm-cluster</td><td>No</td><td>Yes</td><td>Multimarker</td></tr></tbody></table>

(Continues)
<table><caption>TABLE 1 (Continued)</caption><thead><tr><th>Pipelines</th><th>First/latest release</th><th>Software type</th><th>Feature</th><th>Demux</th><th>Primer/adapter removal</th><th>Marker</th></tr></thead><tbody><tr><td>PipeCraft2</td><td>Dec 2021/Dec 2022</td><td>Precompiled and set of algorithms</td><td>ASV, OTU</td><td>Yes</td><td>Yes</td><td>Multimarker (with ITSx and pseudogene removal)</td></tr><tr><td>PIPITS</td><td>Dec 2014/Nov 2022</td><td>Precompiled</td><td>OTU</td><td>No</td><td>Yes</td><td>ITS (with ITSx)</td></tr><tr><td>QIIME 2</td><td>July 2016/May 2023</td><td>Set of algorithms</td><td>ASV, OTU</td><td>Yes</td><td>Yes</td><td>Multimarker (with ITSxpress)</td></tr><tr><td>SCATA</td><td>Feb 2010/Oct 2021</td><td>Precompiled</td><td>OTU</td><td>Yes</td><td>Yes</td><td>Multimarker (with ITSx)</td></tr><tr><td>SEED2</td><td>Feb 2018/Oct 2020</td><td>Precompiled</td><td>OTU</td><td>Yes</td><td>Yes</td><td>16S, ITS (with ITSx)</td></tr><tr><td>Tourmaline</td><td>July 2022/Apr 2023</td><td>Precompiled</td><td>ASV, OTU</td><td>Yes</td><td>Yes</td><td>Multimarker</td></tr><tr><td>USEARCH</td><td>2010/Mar 2020</td><td>Set of algorithms</td><td>ASV, OTU</td><td>No</td><td>No</td><td>Multimarker</td></tr><tr><td>VSEARCH</td><td>Nov 2014/Sep 2022</td><td>Set of algorithms</td><td>ASV, OTU</td><td>No</td><td>No</td><td>Multimarker</td></tr><tr><td>VTAM</td><td>Oct 2020/May 2022</td><td>Precompiled</td><td>ASV, OTU</td><td>Yes</td><td>Yes</td><td>Multimarker (with pseudogene removal)</td></tr></tbody></table>

Note: Column "software type," denotes the structure of the pipeline. "Feature" denotes the pipeline output unit, operational taxonomic units (OTU), amplicon sequence variants (ASV) or swam-clusters (using swarm). "Demux" indicates whether the demultiplexing step is implemented. "Primer/adapter removal" indicates whether primer/adapter removal step is implemented. Column "Marker" states the gene region for which the pipeline has been benchmarked. More details about software in Appendix S1.

discarding the whole sequence if it does not meet the threshold requirements, whereas the per-nucleotide(s) approach truncates the sequence from the position below the threshold to keep a partial amplicon. Among the quality threshold calculation methods, the filtering based on the expected number of errors (sum of the error probabilities) is preferred over the average quality score threshold (Edgar & Flyvbjerg, 2015), because a 'good' average quality score may mask several bases with relatively high error probabilities that can subsequently propagate into false positive features. Haplotype-level (ASV) analyses may require relatively stringent quality cutoffs for accurate fine-resolution analyses, whereas cutoffs may be more lenient when generating OTUs (because clustering collapses many of the accumulated errors during sequencing) or if summarizing data to more inclusive taxonomic ranks (species, genera, etc.).

## 3.4 | Artefacts filtering

Putative chimeric sequences are most commonly removed by comparing sequences against each other (de novo method), but with the existence of an appropriate (curated, chimera-free) reference database, additional reference-based chimera filtering is recommended (Tedersoo et al., 2022). De novo methods tend also to discard sequences that are incorrectly flagged as chimeric (false-positive chimeric sequences; Pauvert et al., 2019; Tedersoo et al., 2022). The loss of these false positive chimeric sequences detection may be more 'costly' for data sets with low sequencing depths. To attempt to rescue those real members of the sequenced community (false-positive chimeras), NextITS (Mikryukov et al., 2022) and FROGS (Bernard et al., 2021; Escudié et al., 2018) pipelines have

implemented an approach to recover sequences that occur in multiple samples (because the formation of an identical chimera in different PCR runs is highly unlikely). With NextITS, it is also possible to inspect the distribution of UCHIME scores (Edgar et al., 2011) of putative chimeras, which allows adjustment of sensitivity-specificity trade-offs in chimera discrimination according to the study aims. A custom false-positive chimeras recovery method is also implemented in BIOCOM-PIPE (Djemiel, Dequiedt, et al., 2020), where initially discarded chimeras can be recovered based on their taxonomic assignments.

## 3.5 | Denoising & clustering

The formation of features in many pipelines includes both ASVs and OTUs (Table 1). ASVs are identical denoised reads with as few as 1 base pair difference between variants, representing an inference of the biological sequences prior to amplification and sequencing errors (Callahan et al., 2017). ASVs are mainly formed through the two most popular denoising algorithms, DADA2 and UNOISE (Edgar, 2016b). Although features formed via UNOISE are referred as zOTUs (zero-radius OTUs; Edgar, 2016b), sometimes also as ESVs (exact sequence variants; Buchner et al., 2022; Porter & Hajibabaei, 2022), we herein denote those with a unified term—ASVs. Although less frequently implemented in the pipeline, deblur (Amir et al., 2017) and obiclean (Boyer et al., 2016) are other denoising algorithms for ASVs formation (Table S1). The OTU clustering approaches include a much wider set of algorithms across different software (Table S1), which typically rely on global sequence similarities. Notably, the clustering process in SCATA (Durling et al., 2011) includes collapsing of homopolymer
regions to account for homopolymer-length errors during sequencing (which are especially common on 454 and Ion Torrent platforms; Laehnemann et al., 2016). Similarly, before the formation of features, NextITS implemented the correction of homopolymer errors in PacBio reads. Swarm (Mahé et al., 2022) is a notably different sequence clustering approach. It relies on the maximum number of differences between reads (local linking threshold), where clusters are resilient to input-order changes, therefore, forming stable, high-resolution features (herein referred to as swarm-clusters). Swarm is currently implemented in Cascabel, CoMA, FROGS, LotuS2 (Özkurt et al., 2022), MICCA (Albanese et al., 2015), NextITS, and PEMA (Zafeiropoulos et al., 2020) pipelines (Table S1; Appendix S1).

Which type of features to prefer may be context-dependent, and both may even be used in the same study. Denoised ASVs provide a biologically informative fine-scale resolution that are collapsed during the OTU formation process (Callahan et al., 2016). For example, by testing several ASVs and OTUs-based workflows for detecting the *Botrylloides* (Ascidiacea) haplotypes, Couton et al. (2021) reported that ASVs pipeline (DADA2) retrieved all expected haplotypes, whereas OTUs datasets (99.5% threshold for clustering) missed several expected haplotypes by collapsing very closely related ones into a single OTU. By default, denoisers tend to discard low-abundant sequence variants, which are more likely to be artefacts (Anslan et al., 2021; Reitmeier et al., 2021). Although denoising greatly lowers the fraction of spurious features (e.g. de Santiago et al., 2022), in some contexts it may be difficult to separate noise from a real signal in low abundant ASVs. For example, the denoising process might discard some rare taxa, that is, ASVs with a low number of sequences (Edgar, 2016b; Nearing et al., 2018). This may have a larger impact when working with a data set with a relatively low sequencing depth. Nevertheless, in some pipelines (e.g., DADA2, FROGS, VSEARCH, and USEARCH), the sensitivity to rare ASVs can be modified according to the user's needs. Importantly, ASVs represent stable and reproducible units across studies whereas OTUs are dataset-specific features (Callahan et al., 2017). However, the ASVs approach may not accurately reflect species composition in the community of, for example, metazoans with highly variable levels of intraspecific polymorphism in the COI gene (Brandt et al., 2021) and fungi with multiple different ITS copies per genome and their size polymorphism (Estensmo et al. 2021; Tedersoo et al., 2022) except when the treatment of ITSs is particularly taken into account (as, e.g., in FROGS; Bernard et al., 2021). If relevant, upon formation of ASVs, those may be subjected to further clustering (Antich et al., 2021; Brandt et al., 2021; Porter & Hajibabaei, 2020). The latter approach is implemented in, for example, MetaWorks (Porter & Hajibabaei, 2022), PipeCraft2 (Anslan et al., 2017), and dadasnake (Weißbecker et al., 2020). Additionally, QIIME 2, nf-core/ampliseq (Ewels et al., 2020; Straub et al., 2020) and LotuS2 support the features collapsing by annotated taxon levels, resulting in taxa features. Overall, the resulting community patterns of a study are often highly similar regardless of the utilized feature (e.g. Glassman & Martiny, 2018; Kang et al., 2021; Porter & Hajibabaei, 2020), but may vary in recovering rare taxa (Nearing et al., 2018).

After the formation of features, the presence of a long tail of low-abundant units is common. This tail is often discarded, assuming that a large proportion of low-abundant features are artefactual (Huse et al., 2010; Reeder & Knight, 2009). However, without applying arbitrary cutoff levels (e.g. removing features with <10 reads per-sample; Brown et al., 2015), the postclustering process aids in removing the erroneous features but keeping the rare, potentially real ones. Postclustering tools, such as LULU (Frøslev et al., 2017) are implemented in AMPTk (Palmer et al., 2018), eDNAflow (Mousavi-Derazmahalleh et al., 2021), APSCALE (Buchner et al., 2022), LotuS2, PipeCraft2 and ReClustOR (Terrat et al., 2020) in BIOCOM-PIPE.

Postclustering, however, does not resolve the tag-switching phenomena, where some low-abundant nonartificial features may represent false-positive occurrences across samples. Tag-switching is a well-documented issue (e.g. Carlsen et al., 2012; Rodriguez-Martinez et al., 2022; Schnell et al., 2015), but is rarely considered in practice because the low proportions of tag-switching errors do not heavily impact the community-level analyses (e.g., Anslan et al., 2021). Nevertheless, the incorrect sample assignments of features artificially inflate the richness. For discarding potential tag-switching errors from the feature table, pipelines such as NextITS, LotuS2, PipeCraft2 and Dadaist2 (Ansorge et al., 2021) wrap the UNCROSS2 (Edgar, 2018b) algorithm (from USEARCH). Based on the included control samples, AMPtk and VTAM attempt to automatically correct for tag-switching errors. Notably, the tag-switching issue can be minimized by accounting for this in the laboratory work protocol (Carøe & Bohmann, 2020; Taberlet et al., 2018). However, for further feature occurrence filtering to filter out low-confidence detections biological/technical replicates per sample are recommended (Gold et al., 2021). This allows examining the feature co-occurrence patterns across replicates to estimate detection probabilities and retain only high-confidence detections (by applying, e.g., site occupancy modelling). Among the precompiled pipelines, VTAM implements a feature occurrence filtering procedure based on the user-defined number of technical replicates they appear in, and samples may be discarded when the sequence composition in the replicate samples is too dissimilar. Not incorporated to the pipelines discussed here, but the MetabaR package (Zinger et al., 2021) aids to detect different types of artefactual sequences, such as potential contaminants, tag-switches, and dysfunctional PCRs (on the basis of similarities between replicate samples).

## 3.6 | Taxonomy assignment

In the reviewed pipelines, the most common taxonomy assignment methods include alignment-based (such as BLAST; Altschul et al., 1997) and sequence composition-based approaches (e.g. RDP Naïve Bayesian classifier; Wang et al., 2007; see Table S1). Several studies have tested the accuracy of different taxonomy assignment methods (e.g. Bokulich et al., 2018; Curd et al., 2019; Edgar, 2018a; Hleap et al., 2021; Richardson et al., 2017) and have recognized a relationship between the reference database completeness and
the classification accuracy. Regardless of the taxonomic group, the reference databases are far from being complete (Gold et al., 2021; McGee et al., 2019; Nilsson et al., 2016; Weigand et al., 2019). Therefore, a trade-off between the detection of true-positives (correctly assigned sequences) and false-positives (incorrectly assigned sequences), that is, the precision and the recall rate, should be considered when choosing a threshold for the classification (Bokulich et al., 2018; Edgar, 2018a). Hleap et al. (2021) suggested that a multilayer approach could enhance the effectiveness of similarity-based methodologies. The goal of this strategy is to improve the precision of taxonomic assignments, minimize the occurrence of false positives, and boost the efficiency of the classification process. VTAM's taxonomy assignment function has incorporated elements of this strategy. It begins the assignment process with a high percentage identity threshold, which is gradually lowered until the lowest taxonomic group is established.

Although composition-based (and other 'complex') methods may be more sensitive to the patchy coverage databases than 'simple' alignment-based methods (Hleap et al., 2021), in certain circumstances Naïve Bayesian classifiers may outperform BLAST (Rosen et al., 2011). However, the assignment accuracy to higher taxonomic ranks (such as Family level) generally has similar performance across the approaches (Hleap et al., 2021). A recent development in QIIME 2 involves utilizing public microbiome data for probabilistic taxonomy assignment (Kaehler et al., 2019). This method offers several advantages, including the potential for higher resolution taxonomic classification for instance, it can enable species-level classification when previously only genus-level classification was possible. Other pipelines, such as LotuS2, can assign features from multiple taxonomic databases, to preferentially assign taxonomies based on databases that are specific to a given environment. FROGS returns an original multiaffiliation output to highlight databases conflicts and uncertainties taxonomic affiliations. AMPtk implements a hybrid taxonomy assignment that utilizes global alignment (VSEARCH) and SINTAX (Edgar, 2016a) to calculate a consensus LCA (last common ancestor) taxonomy. Regardless of the taxonomy assignment methods used, the reference database should also include a proportion of nontarget taxa (including potential contaminants) to limit the overclassification of features to the target taxa (e.g. Anslan et al., 2018).

## 4 | SEQUENCING PLATFORM

The most commonly utilized high-throughput sequencing approaches for metabarcoding are short-read, second-generation technologies, such as those provided by Illumina platforms. These platforms produce a high number of high-quality paired-end short reads (up to 300 bp for single-end) with a relatively low cost per sample. Therefore, most amplicon data analysis pipelines are set up to be able to handle paired-end sequencing data (Table 1; Figure 2). As the MGI-Tech platforms may also produce paired-end reads (with comparable data quality and throughput properties compared with

Illumina; Anslan et al., 2021), the paired-end compatible pipelines may be used to analyse data from the latter platforms as well.

Some pipelines are restricted to paired-end input, that is, the analytical pipeline cannot be completed using only a single-end part of the data, or sequencing data from the long-read (third-generation) sequencing platforms (Table 1; Appendix S1). With the rapid developments in third-generation sequencing accuracy and throughput, there is increasing interest to generate longer metabarcodes, which potentially increases the taxonomic resolution (Tedersoo et al., 2021, 2022) and has lower sequencing bias toward short amplicons (Castaño et al., 2020). Therefore, some software developed for short reads have been updated to also process longer sequences (specifically PacBio reads; Appendix S1). Although, some of the software considered here have performed well for sequencing data (HiFi reads) processing from PacBio platforms (e.g., Castaño et al., 2020; Heeger et al., 2018; Tedersoo & Anslan, 2019), the data from Oxford Nanopore Technologies (ONT) platform may require other customized approaches (Baloğlu et al., 2021). Herein listed software (Table 1) have not been specifically developed for analysing ONT data, thus care should be taken when applying these tools for nanopore reads.

The sequencing depth may vary considerably between sequencing platforms. For example, Illumina MiSeq system may produce up to 25 M 2×300 bp reads and NovaSeq up to 1600 M 2×250 bp reads per flow cell, whereas the throughput of PacBio Sequel II(e) system is up to 4 M HiFi reads. Since the denoising tools are sensitive (by default) to low abundant sequences, then one must be wary that strict denoising of low sequencing depth samples increases the number of false negatives, that is, rare true positives may be denoised out (Furneaux et al., 2021), especially if the samples contain complex communities, such as found in soil. Besides sequencing depth, the detection of rare sequence variants may be affected by the different chemistry utilized by different platforms (e.g. NovaSeq vs. MiSeq; Singer et al., 2019). Importantly, denoising algorithms, such as UNOISE and deblur, are designed for Illumina reads and may not perform well with data from other sequencing platforms. Therefore, opting for an OTU clustering approach may be more appropriate for analysing complex communities sequenced by the third-generation platforms. The remaining bioinformatically unscreened low-abundance spurious OTUs could then be abandoned after the post-clustering step, by, for example, filtering out unclassified features at the phylum level, and based on the number of samples they occur in (e.g. discard features only observed in one sample). Although, DADA2 has a specific denoising function to estimate errors from PacBio reads (Callahan et al., 2019) which performs well also on synthetic long reads (Callahan et al., 2021), its application may still require higher sequencing depth for high diversity samples (Furneaux et al., 2021). However, the throughput of the most recent PacBio long-read sequencing system, Revio (commercially available from the first half of 2023), is expectedly up to 15 times higher compared with Sequel II. But the performance of the denoisers with the greatly increased throughput of long-read data (exceeds the throughput of, e.g., Illumina MiSeq) is yet to be tested.
In case the amplicon is shorter than the sequencing cycle (e.g. expected amplicon is ~130 bp, but one cycle synthesizes 250 bp), the Illumina NovaSeq and NextSeq platforms may extend the icon by adding a poly-G tail (with 'good' quality scores). Therefore, trimming primers from amplicon reads should be used by default, as this will discard the overhanging sequence parts. Fastp tool (Chen et al., 2018), wrapped also in PipeCraft2 and NextITS, may be used to specifically trim these non-biological poly-G (or poly-X) tails. Additionally, Phred scores from third-generation sequencing platforms range from 0 to 93, thus may require adjustments of the maximum quality score setting when using, for example, VSEARCH or USEARCH software (where the default is 41, for Illumina).

## 5 | OPERATING SYSTEMS AND WORKFLOW MANAGERS

Unix-based operating systems (OS), such as Linux and macOS, are the most common and convenient platforms in bioinformatics, as users may run software with a comparable interface on a personal computer or high-performance computing (HPC) system. As a result, they are by far the most widely used for the development and use of bioinformatics tools. Accordingly, almost all the presented pipelines can be executed in Linux and/or macOS operating systems (Table 1; Figure 2). Since many users have computers running on Windows-based operating systems, several pipeline developers have gone through the effort of making the Unix-based workflows executable in Windows; sometimes through native code adaptations or by making their software available in either containers or through websites (such as Galaxy; Galaxy Community, 2022), making the pipelines independent of the OS (Table 1).

Some metabarcoding data analysis tools, such as DADA2 rely exclusively on R and are thus also compatible with any OS that can execute R. JAMP (https://github.com/VascoElbrecht/JAMP) is another R package that wraps full metabarcoding and haplotyping pipelines, although it is only available for Linux and macOS. Additionally, with the development of containerization technology (e.g. Docker, Singularity), it becomes easier to develop bioinformatics pipelines that can run on the three major operating systems, Windows, macOS, and Linux. A container encapsulates the code and dependencies needed for the data analyses so that the pipeline may run reliably on any OS. Once the containerization software is installed, users are free to install all the underlying dependencies. For a few of the presented pipelines, the developers have included the prebuilt containers and/or virtual machine images required to run it (Table 1; Appendix S1). Pipelines such as those distributed by nf-core/ampliseq, PipeCraft2, PEMA, and Tourmaline require utilizing Docker/Singularity containers at the back-end, so the core bioinformatics processes are running on a Linux environment but may also be executed on Windows and macOS systems. Moreover, containerized pipelines resolve the numerical instability issue occurring while running software on different computational platforms (Di Tommaso et al., 2017), ensuring the consistency of results and allowing more reproducible computational workflows.

Essentially all the pipelines can be run on any OS via containers or virtual machines. However, containers are preferred to virtual machines (e.g. VirtualBox), as virtualization (i.e. running a second OS on top of the main OS) has high overhead and comes at the cost of a computer's RAM usage, which ultimately limits the amount of data that can be processed. Considering container engines, Docker is usually unavailable on HPC clusters, as potential vulnerability could provide means to gain root access to the system they are running on. Therefore, Singularity (Kurtzer et al., 2017) is generally more widespread on HPC clusters as it was specifically developed for it.

With the capacity to provide computational resources, web-based platforms, such as DAnIEL (Loos et al., 2021) and SCATA may be simply used through a web browser on any operating system. Additionally, some other pipelines, such as FROGS and LotuS2, can also be accessed through Galaxy websites, and nf-core/ampliseq (Straub et al., 2020) can be launched via Nextflow Tower (a monitoring and management platform for Nextflow workflows).

The increasing complexity of bioinformatics pipelines, which consist of a large number of computational steps, encouraged the development of workflow management systems capable of orchestrating in a scalable and reproducible manner (Mölder et al., 2021; Wratten et al., 2021). Workflow managers allow pipelines to resume after a failure and start from the last successfully completed step, automate pipeline execution triggered by input or reference data updates and perform parameter exploration. Nextflow (Di Tommaso et al., 2017) and Snakemake (Koster & Rahmann, 2012; Mölder et al., 2021) are among the most prominent workflow management systems in the field of bioinformatics. They simplify pipeline development, maximize resource usage efficiency and handle installation and versioning of the software dependencies (e.g. using Docker and Singularity containers or conda environments). These systems allow running workflow steps in parallel locally or using resources of HPC clusters or commercial cloud computing providers (Amazon web services (Bai et al., 2019), Microsoft Azure (Copeland et al., 2015), Google Cloud (Hussain & Aleem, 2018)) almost without the need to adapt pipeline code to a specific platform architecture. MetaWorks, dadasnake, Tourmaline (Thompson et al., 2022), and Cascabel (Asbun et al., 2020) are examples of Snakemake-based pipelines, while nf-core/ampliseq, eDNAflow and NextITS were developed using Nextflow.

## 6 | THE INTERFACE

Generally, the Unix-based command-line interfaces (CLI; commands are typed into a terminal) are often preferred by analysts with bioinformatics experience. That is because most of the pipelines are developed as CLI-runnable software that can be operated on HPC clusters, but also due to the flexibility and availability of applying various custom processes to manage the data effectively. Although the CLI tools offer numerous advantages, using a CLI might be intimidating for users with less programming experience. To facilitate the analysis of metabarcoding data by nonbioinformaticians, APSCALE,
CoMA (Hupfauf et al., 2020), gDAT (Vasar et al., 2021), PipeCraft2 and SEED 2 (Vetrovský et al., 2018) provide a graphical user interface (GUI; interaction via clickable graphical icons; Table 1; Figure 2) as a front-end for specifying the settings of the bioinformatics analyses, which will be executed on the back-end. Depending on the architecture, the GUI-based applications may require more RAM than CLI pipelines. Pipelines that have web server support (DANIEL, SCATA) or have been implemented into Galaxy server (LotuS2, FROGS, QIIME 2) naturally possess a web-based GUI for specifying the settings of the analysis. Some software that is wrapped into GUI may also be executed through CLI (Table 1).

# 7 | MARKER-SPECIFIC PIPELINES

A marker (i.e., 'DNA barcode') is a taxonomically informative gene fragment that is utilized for species identifications in the presence of reference sequence data. Bioinformatics processes combined in a pipeline may be specifically designed to analyse amplicons from a specific marker, that is, the analytical steps may depend on the characteristics of the amplicons. For example, when processing ITS amplicon data, it is common to remove conservative flanking genes of ITS for accurate taxonomic classification purposes (Tedersoo et al., 2022; Vu et al., 2022). When processing sequences from the COI gene, removing the co-amplified putative nuclear mitochondrial pseudogenes (NUMTs) is highly recommended (Creedy et al., 2022; Porter & Hajibabaei, 2021; Song et al., 2008). The subsections below outline the herein-considered marker-specific and multimarker pipelines and highlight some of the results from their benchmarking trials.

## 7.1 | Prokaryotic 16S rRNA

Amplicon sequencing targeting the 16S rRNA gene is commonly utilized to investigate microbiomes from various ecosystems/substrates (Knight et al., 2018; Pollock et al., 2018; Staats et al., 2016). The 16S gene sequence is roughly 1500 bp in length and contains nine distinct hypervariable regions (V1–V9). The V4 hypervariable region is most often used in short-read sequencing, whereas full-length 16S analyses are becoming increasingly utilized with the increased quality, availability, and decreasing costs of long-read sequencing methods. For processing 16S amplicons, mothur, USEARCH, QIIME 2 and DADA2 are the most used ones. Recently established pipelines such as dadaist2, dadasnake, nf-core/ampliseq, Tourmaline also wrap QIIME 2 and/or DADA2 functionalities and are thus optimized for 16S (but not exclusively) analyses. BIOCOM-PIPE, Cascabel, CoMA, LotuS2, MICCA, PEMA and FROGS have also benchmarked their pipelines using 16S data sets. However, since the bioinformatics processing of 16S amplicon data was at the forefront of metabarcoding data analyses before the wide-scale utilization of other markers, other multimarker pipelines (Table 1) that consist of critical filtering steps may also be used to process 16S reads.

Testing different workflows on 16S V4 amplicon mock data (known composition of taxa in a sample), Straub et al. (2020) found QIIME 2 pipeline with the DADA2 plugin being the most optimal compared to mothur, QIIME 1 and MEGAN (Huson et al., 2007) workflows. Based on the benchmarking results, the nf-core/ampliseq pipeline was developed which demonstrated a high degree of similarity with the results produced by QIIME 2. Prodan et al. (2020) reported good performance of all tested ASV workflows (DADA2, QIIME 2 deblur, UNOISE3), but with slight variations in their sensitivity and specificity to detect mock community members. In the latter study, two OTU workflows also performed well (UPARSE, mothur; but not QIIME 1-uclust), but with lower specificity than ASV pipelines. A more recent study by Özkurt et al. (2022) reported the higher accuracy of LotuS2 compared with QIIME 2, DADA2, and PipeCraft2. The LotuS2 pipeline runs with stringent read filtering and implements a unique feature, a 'seed extension' algorithm, that improves the quality of a feature's representative sequence. By introducing the CoMA pipeline that uses LotuS1/2 (Hildebrand et al., 2014) at its core, Hupfauf et al. (2020) reported a good performance of all tested pipelines (CoMA, QIIME 2, mothur). However, some degree of variability was evidently depending on the test data set. In general, the lack of consensus as to the 'best performing pipeline' illustrates the importance of the underlying dataset properties. Considering the dataset's characteristics under the operation, tweaking, and fine-tuning the settings of different pipelines may further, at least to some extent, diminish the variability in their accuracy.

## 7.2 | ITS rRNA

The nuclear ribosomal internal transcribed spacer (ITS) region is a standard marker in fungal metabarcoding studies (Nilsson et al., 2019). It is also taxonomically informative in other eukaryotic groups (e.g. flowering plants, mites, springtails; Anslan & Tedersoo, 2015; Banchi et al., 2020; Ben-David et al., 2007). The ITS region is highly variable in length among eukaryotic groups, complicating the bioinformatics analysis steps that rely on aligning (such as, e.g., mothur OTU clustering) or require uniform sequence length (such as, e.g., deblur). Pipelines such as NextITS, PIPITS (Gweon et al., 2015), and DANIEL are developed explicitly for ITS amplicon analyses. Those pipelines implement the extraction of ITS sub-regions (ITS1/ITS2, or full ITS) to exclude flanking conservative regions (18S/5.8S/28S), which is optimal for taxonomic assignment accuracy (Bengtsson-Palme et al., 2013; Vu et al., 2022). SCATA is also optimized for the ITS region, and for other amplicon sequences which cannot be easily aligned. However, a few other universal pipelines, such as LotuS2, SEED 2, nf-core/ampliseq, PipeCraft2, MetaWorks, dadasnake, FROGS (all using ITSx; Bengtsson-Palme et al., 2013), and QIIME 2 (using the ITSxpress plugin; Rivers et al., 2018) incorporate the step for extracting the ITS sub-regions for optimal processing of ITS amplicon data. Because the ITS-subregions of some fungal groups may not sufficiently overlap during the paired-end data assembly
process, FROGS, PipeCraft2, Dadaist2 and Cascabel (latter two without ITSx) implement settings to also include nonassembled reads to ensure that taxa with longer ITS regions are not excluded (Bernard et al., 2021).

Although AMPtk, DADA2, eDNAflow, and gDAT were validated using ITS reads, these pipelines lack a step to clip the flanking regions from ITS reads. While ITS extraction tools may eliminate some fungal strains from the data, many false-positive molecular units are generated when this extraction process is excluded (Pauvert et al., 2019). To mitigate the detection of false-negatives, the exclusion of the ITS extraction may be more appropriate if the aim is to find specific target taxa, whereas the ITS extraction operation should be included in community ecology studies (Pauvert et al., 2019).

Tested on technical replicates from soil samples (i.e. DNA from the same sample sequenced twice), compositional matrices of ITS data from QIIME 2 and LotuS2 were more reproducible than native DADA2, where the latter did not incorporate an ITS extraction step (Özkurt et al., 2022). Differences in the ITS amplicon data analyses among various software (PipeCraft1, QIIME 2, PIPITS, LotuS1, and custom pipeline compiled on Galaxy platform) were evident also in the study by Anslan et al. (2018) where QIIME 2 and Galaxy-based pipelines did not include the ITS extraction step (because it was not yet implemented). Although the inclusion of ITS region extraction step lowers the amount of nontarget features, the latter study concluded that none of the tested workflows were able to fully filter out the erroneous sequences, which contributed to the demonstrated differences between pipelines.

## 7.3 | COI

Found in the mitochondria, the cytochrome oxidase subunit I (COI/CO1/cox1) is a standard animal barcode (Hajibabaei et al., 2011; Hebert et al., 2003). Compared with other suitable markers (e.g. mt 16S, ITS, 28S) for most metazoan groups, the reference database of the COI is vast (Porter & Hajibabaei, 2018) and COI fragments are extensively used in metabarcoding studies.

Metabarcoding of metazoan communities is increasingly employed in ecology, but the strategies for analysing the sequencing data vary largely across studies. Generally, the metabarcoding studies utilizing protein-coding genes (such as COI) have largely followed the bioinformatic workflows designed to characterize microbial diversity without adapting the workflows to the characteristics of protein-coding markers (Creedy et al., 2022). When processing protein-coding markers, the noise of nuclear mitochondrial pseudogenes (NUMTs) may inflate the richness estimates and thus introduce biases in biodiversity research using metabarcoding (Porter & Hajibabaei, 2021). Thus, the amino acid translation, but also the length of the read should be used to identify erroneous sequences (Creedy et al., 2022). Of the pipelines reviewed here, MetaWorks and VTAM implement a step of removing putative NUMTs, which alleviates the burden of manual curation of the features to produce more accurate richness estimates. The multimarker amplicon

processing platform PipeCraft2 has also wrapped MetaWorks strategy of the pseudogene removal step. Apart from the full pipelines, the multisample features matrix may be processed with metaMATE (Andújar et al., 2021) to remove putative NUMTs and other erroneous sequences (based on, e.g., length and relative read abundance). Additionally, DARN (Zafeiropoulos et al., 2021), which makes use of the phylogenetic tree, aids in filtering out nontarget features and upon denoising, the characteristics of protein coding genes are also accounted for in the DnoisE (Antich et al., 2022). We will most likely see the latter module integrated into the already established pipelines in the near future.

## 7.4 | Other markers and multimarker pipelines

Besides the above-mentioned markers, other popular markers used for metabarcoding are mt 16S rRNA for Metazoa, mt 12S rRNA for fish (Miya et al., 2020), 18S rRNA for protists and other eukaryotes, 28S rRNA for nematodes and eukaryotes in general, rbcL for diatoms (Rimet et al., 2019), rbcL + matK and trnL for plants (CBOL Plant Working Group 1 et al., 2009; Taberlet et al., 2007), and 23S rRNA for photosynthetic microbes (Djemiel, Dequiedt, et al., 2020; Djemiel, Plassard, et al., 2020). A variety of pipelines have been applied for the analyses of the amplicon sequences from these markers. For example, MICCA, DADA2 for 18S rRNA (Harrison et al., 2021; Minerovic et al., 2020); DADA2, OBITools for mt 16S (Marquina et al., 2019; Thomsen & Sigsgaard, 2019); and custom built pipelines (using multiple third-party sequence data analysis tools) for other markers above (Anslan et al., 2021; Elbrecht et al., 2016; Liu & Zhang, 2021; Westfall et al., 2020). Benchmarked on mt 12S reads from both simulated and real eDNA data, the Barque pipeline demonstrated a small sensitivity improvement over QIIME 2 and OBITools (Mathon et al., 2021). Moreover, another VSEARCH-based custom pipeline found in the latter study, which was designed to match Barque's performance by adjusting the parameters and threshold, showed the same mean sensitivity as Barque, demonstrating that the careful choice of the tools for the required task provides accurate results.

Table 1 lists multimarker software that may be utilized for various markers. All of the developed application software contain the most crucial steps for basic metabarcoding data analyses, but the suitability of a software or workflow steps for a given marker should be assessed. For example, considering the length variability and alignment of the amplicon set is important when some pipeline steps (e.g. clustering) use alignment-based methods (such as in mothur) or require uniform read lengths (such as deblur denoising). When working, for example, rbcL amplicons (or amplicons from any other protein coding gene), validation is needed to ensure that the generated features do not represent potential pseudogenes (or off-target taxa) for biodiversity analyses. Some multimarker pipelines incorporate marker-specific steps, for example, extracting the ITS region, removing putative pseudogenes and off-target features (Appendix S1). Using a pipeline that is not restricted to a certain marker gene, but
where the above listed automated filtering processes are lacking, a
manual feature curation step is usually required to filter out bioin-
formatically unfiltered noise or to validate that most of the noise has
already been removed. Depending on the study context, different
analytical pipelines may yield highly compatible results (e.g. Baltrušis
et al., 2022; Kang et al., 2021), but the outcome and interpretation
may also vary considerably (Anslan et al., 2018; Bailet et al., 2020;
Pauvert et al., 2019; Straub et al., 2020) without the validation of the
software suitability for a given marker.

8 | CONCLUDING REMARKS

The development of a wide range of metabarcoding data analysis
pipelines illustrates the need for not only 'easy-to-use' software but
also of specific customized workflows depending on the underlying
sequencing data set. Although most of the precompiled pipelines
largely mirror the functionalities of several software suites by in-
corporating steps from algorithms providing software suites, they
offer easily executable automated alternatives for users with less
bioinformatics experience. Additionally, many precompiled pipe-
lines are supplemented with several possibilities for downstream
analyses by wrapping various third-party tools. Applying different
workflows on the same data will always demonstrate a certain level
of variation among pipelines. These variations are usually most ob-
vious in terms of the reported number of features. This generally
derives from variations in filtering out spurious and low-abundant
sequences (e.g. Edgar, 2017; Prodan et al., 2020). Therefore, one
pipeline may produce a higher number of features per sample and
the other much less, but the correlations between sample-wise
richness from one to another result are in most cases very high
(Baltrušis et al., 2022; Kang et al., 2021). However, depending on
the analysed data set, this correlation pattern may be the opposite
(Nearing et al., 2018) and pipeline settings should be carefully con-
sidered, especially when identifying rare taxa is imperative. Thus,
although the automated pipelines have made the analyses easier
and more reproducible, expertise is still required to validate the ac-
curacy of the biological results. It is noteworthy that a pipeline's
performance measured on mock community samples with relatively
few species may vary when applied to a complex data set originat-
ing from environmental samples. Nevertheless, including a mock
community control sample(s) in a study will certainly aid in identify-
ing false positives and false negatives. A robust sense of the com-
munity patterns may be obtained by applying 'default' parameter
values but fine-tuning of the parameters may be required to find an
appropriate compromise between false positive removal and reten-
tion of true detections.

Table 1 and Figure 2 are aiming to provide assistance in narrow-
ing down the desirable pipelines for the task. Once the potential
target workhorses have been selected, one would naturally need
to explore the respective user guides for more detailed information
about the underlying procedures.

ACKNOWLEDGEMENTS

This work was supported by the European Regional Development
Fund and the programme Mobilitas Plus (MOBTP198). MH and
TMP received funding from Genome Canada and Ontario Genomics
through the Sequencing the Rivers for Environmental Assessment
and Monitoring (STREAM) project. DS acknowledges funding by
the Deutsche Forschungsgemeinschaft (DFG) under Germany's
Excellence Strategy, cluster of Excellence EXC2124 "Controlling mi-
crobes to fight infection" (CMFI), project ID 390838134. We wish to
offer our heartfelt thanks to Sébastien Terrat, the leading developer
of BIOCOM-PIPE (including ReClustOR) for the support extended to
certain points in this tool. TV was supported by the Czech Science
Foundation (21-17749S). We thank other FROGS'members Vincent
Darbot, Lucas Auer and Olivier Rué, and Frédéric Mahé (swarm
software author) for their fruitful exchanges on the ASV/OTU/
cluster terminology. EC received funding through an Institutional
Development Award (IDeA) from the National Institute of General
Medical Sciences of the National Institutes of Health under grant
number P20GM103449.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

DATA AVAILABILITY STATEMENT

Not applicable.

ORCID

Ali Hakimzadeh https://orcid.org/0000-0003-1336-7445

Maria Bernard https://orcid.org/0000-0001-9005-5563

Dominik Buchner https://orcid.org/0000-0002-8499-5863

Christophe Djemiel https://orcid.org/0000-0002-5659-7876

Mikael Brandström Durling https://orcid.

org/0000-0001-6485-197X

Vasco Elbrecht https://orcid.org/0000-0003-4672-7099

Mehrdad Hajibabaei https://orcid.org/0000-0002-8859-7977

Géraldine Pascal https://orcid.org/0000-0002-5250-6594

Martti Vasar https://orcid.org/0000-0002-4674-932X

Sten Anslan https://orcid.org/0000-0002-2299-454X

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## SUPPORTING INFORMATION

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**How to cite this article:** Hakimzadeh, A., Abdala Asbun, A., Albanese, D., Bernard, M., Buchner, D., Callahan, B., Caporaso, J. G., Curd, E., Djemiel, C., Brandström Durling, M., Elbrecht, V., Gold, Z., Gweon, H. S., Hajibabaei, M., Hildebrand, F., Mikryukov, V., Normandeau, E., Özkurt, E., M. Palmer, J. ... Anslan, S. (2023). A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses. *Molecular Ecology Resources*, 00, 1-17. https://doi.org/10.1111/1755-0998.13847