Metabarcoding can be broadly defined as the identification of multiple species via the high-throughput amplicon sequencing of environmental samples. This approach has been shown to be particularly powerful in studies examining complex assemblages of small and often cryptic organisms (e.g. microbes), as well as scenarios where samples may be degraded, with their constituents difficult to identify by optical means (e.g. faeces and gut-contents). While still a relatively new field of research, metabarcoding has not only transformed the way we obtain ecological data, but more importantly, our fundamental understanding of the distributions of taxa and their interactions with the environment. However, metabarcoding data is inherently noisy, with artefacts being produced in each step from sample collection to data processing. Collectively, these have the potential to substantially alter the data which underpins a study’s findings. Here, I present a meta-analysis of the metabarcoding literature focusing on a number of QA/QC protocols pivotal for ensuring the reliability and transparency of data. The resulting evidence demonstrates a lack of adequate QA/QC implementation and/or reporting across the field, indicating that the findings of many studies may have been derived from data of unknown quality. While it is undoubtedly easy to pass judgement on a field that is still in its infancy, here I argue that in order for metabarcoding research to progress and to ensure it produces robust and reproducible findings, there is a critical need for a step-change in the way we monitor and report the quality of data.
Metabarcoding can be broadly defined as the identification of multiple species via the high-throughput amplicon sequencing of environmental samples. This approach has been shown to be particularly powerful in studies examining complex assemblages of small and often cryptic organisms (e.g. microbes), as well as scenarios where samples may be degraded, with their constituents difficult to identify by optical means (e.g. faeces and gut-contents). While still a relatively new field of research, metabarcoding has not only transformed the way we obtain ecological data, but more importantly, our fundamental understanding of the distributions of taxa and their interactions with the environment. However, metabarcoding data is inherently noisy, with artefacts being produced in each step from sample collection to data processing. Collectively, these have the potential to substantially alter the data which underpins a study’s findings. Here, I present a meta-analysis of the metabarcoding literature focusing on a number of QA/QC protocols pivotal for ensuring the reliability and transparency of data. The resulting evidence demonstrates a lack of adequate QA/QC implementation and/or reporting across the field, indicating that the findings of many studies may have been derived from data of unknown quality. While it is undoubtedly easy to pass judgement on a field that is still in its infancy, here I argue that in order for metabarcoding research to progress and to ensure it produces robust and reproducible findings, there is a critical need for a step-change in the way we monitor and report the quality of data.
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