Literature DB >> 34398515

Towards reproducible metabarcoding data: Lessons from an international cross-laboratory experiment.

Anastasija Zaiko1,2, Paul Greenfield3,4, Cathryn Abbott5, Ulla von Ammon1, Jaret Bilewitch6, Michael Bunce7, Melania E Cristescu8, Anthony Chariton4, Eddy Dowle9, Jonathan Geller10, Alba Ardura Gutierrez11, Mehrdad Hajibabaei12, Emmet Haggard10, Graeme J Inglis13, Shane D Lavery2,14, Aurelija Samuiloviene15, Tiffany Simpson16, Michael Stat17, Sarah Stephenson3, Judy Sutherland6, Vibha Thakur14, Kristen Westfall5, Susanna A Wood1, Michael Wright12, Guang Zhang8, Xavier Pochon1,2.   

Abstract

Advances in high-throughput sequencing (HTS) are revolutionizing monitoring in marine environments by enabling rapid, accurate and holistic detection of species within complex biological samples. Research institutions worldwide increasingly employ HTS methods for biodiversity assessments. However, variance in laboratory procedures, analytical workflows and bioinformatic pipelines impede the transferability and comparability of results across research groups. An international experiment was conducted to assess the consistency of metabarcoding results derived from identical samples and primer sets using varying laboratory procedures. Homogenized biofouling samples collected from four coastal locations (Australia, Canada, New Zealand and the USA) were distributed to 12 independent laboratories. Participants were asked to follow one of two HTS library preparation workflows. While DNA extraction, primers and bioinformatic analyses were purposefully standardized to allow comparison, many other technical variables were allowed to vary among laboratories (amplification protocols, type of instrument used, etc.). Despite substantial variation observed in raw results, the primary signal in the data was consistent, with the samples grouping strongly by geographical origin for all data sets. Simple post hoc data clean-up by removing low-quality samples gave the best improvement in sample classification for nuclear 18S rRNA gene data, with an overall 92.81% correct group attribution. For mitochondrial COI gene data, the best classification result (95.58%) was achieved after correction for contamination errors. The identified critical methodological factors that introduced the greatest variability (preservation buffer, sample defrosting, template concentration, DNA polymerase, PCR enhancer) should be of great assistance in standardizing future biodiversity studies using metabarcoding.
© 2021 John Wiley & Sons Ltd.

Entities:  

Keywords:  18S ribosomal rRNA (18S rRNA); high-throughput sequencing; metabarcoding; mitochondrial cytochrome c oxidase subunit 1 (COI); reproducibility; standardization

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Year:  2021        PMID: 34398515     DOI: 10.1111/1755-0998.13485

Source DB:  PubMed          Journal:  Mol Ecol Resour        ISSN: 1755-098X            Impact factor:   7.090


  2 in total

1.  Toward global integration of biodiversity big data: a harmonized metabarcode data generation module for terrestrial arthropods.

Authors:  Paula Arribas; Carmelo Andújar; Kristine Bohmann; Jeremy R deWaard; Evan P Economo; Vasco Elbrecht; Stefan Geisen; Marta Goberna; Henrik Krehenwinkel; Vojtech Novotny; Lucie Zinger; Thomas J Creedy; Emmanouil Meramveliotakis; Víctor Noguerales; Isaac Overcast; Hélène Morlon; Anna Papadopoulou; Alfried P Vogler; Brent C Emerson
Journal:  Gigascience       Date:  2022-07-19       Impact factor: 7.658

2.  Temporal and Spatial Variation of the Skin-Associated Bacteria from Healthy Participants and Atopic Dermatitis Patients.

Authors:  Christopher J Barnes; Maja-Lisa Clausen; Anders Johannes Hansen; Tove Agner; Maria Asplund; Linett Rasmussen; Caroline Meyer Olesen; Yasemin Topal Yüsel; Paal Skytt Andersen; Thomas Litman
Journal:  mSphere       Date:  2022-02-23       Impact factor: 4.389

  2 in total

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