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

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 amongst 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-one amplicon processing pipelines with the goal of helping users to select a pipeline for their metabarcoding projects.

Is the metabarcoding ripe enough to be applied to the authentication of foodstuff of animal origin? A systematic review

“…As reported in Section 2.2.3, the pipelines used in the SPs were classified according to Hakimzadeh et al. (2023) in Pip‐1 and Pip‐2. Most of the SPs (17/23; 73.9%) used Pip‐1, including bioinformatic packages (FASTA package, BWA, DADA2, BBDuk, FLASH), as well as expandable web‐based platforms for NGS data analysis (QIIME, Galaxy, Mothur CD‐HIT‐EST, USEARCH).…”

“…For each SP, the used bioinformatic pipeline was analyzed. Typically, a metabarcoding pipeline is articulated in the following phases: (Hakimzadeh et al, 2023). The first phase is often directly performed by the sequencing software, with demultiplexed and adapter/primer-removed data that are directly provided to the users, whereas the other phases are executed by the users.…”

Community‐scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

“…Amplicon sequences are clustered into operational taxonomic units (OTUs) or into amplicon sequencing variants (ASVs) that provide 'species-like' delineation of organisms and estimations of relative abundance. Bioinformatic pipelines for amplicon sequencing analysis are reviewed in Hakimzadeh et al (2023). While the functions present in a microbiome cannot be directly identified by amplicon sequencing (Almeida et al, 2021), approximations may be indirectly inferred using phylogenetic placement of markers (Bowman & Ducklow, 2015;Douglas et al, 2020), alignment (Narayan et al, 2020;Wemheuer et al, 2020) or incorporation of biological knowledge in the form of taxonomic and functional profiles (Jing et al, 2021;Mallick et al, 2019).…”

Molecular ecology of microbiomes in the wild: Common pitfalls, methodological advances and future directions

“…The sequencing of negative controls including DNA extraction blanks and PCR negative controls (and potentially also positive controls and technical replicates) alongside experimental samples is important for quantifying errors and artefacts (Davis et al., 2018), and can improve data curation procedures through tuned, experiment‐specific criteria, including for samples with low microbial biomass. While there is more than one way to implement such efforts, a thorough description of the controls, a rationale for including them, and the ways they are integrated into data analysis, are essential practices of good microbiome science (Hakimzadeh et al., 2023).…”

“…As a consequence, the most abundant taxa will be detected in many samples, including the negative controls (Esling et al., 2015; Minich et al., 2019; Taberlet et al., 2018), preventing simple removal of all taxa occurring in controls as a fix for field and laboratory contamination. Alleviating the problem of tag‐switches can be achieved with modified library preparation protocols (Carøe & Bohmann, 2020), appropriate sample labelling strategies and a posteriori using the information contained in both samples and negative controls (Bohmann et al., 2022; Hakimzadeh et al., 2023).…”