Towards reproducible metabarcoding data: Lessons from an international cross‐laboratory experiment. (31st August 2021)

• Record Type: Journal Article
• Authors: Zaiko, Anastasija; Greenfield, Paul; Abbott, Cathryn; von Ammon, Ulla; Bilewitch, Jaret; Bunce, Michael; Cristescu, Melania E.; Chariton, Anthony; Dowle, Eddy; Geller, Jonathan; Ardura Gutierrez, Alba; Hajibabaei, Mehrdad; Haggard, Emmet; Inglis, Graeme J.; Lavery, Shane D.; Samuiloviene, Aurelija; Simpson, Tiffany; Stat, Michael; Stephenson, Sarah; Sutherland, Judy; Thakur, Vibha; Westfall, Kristen; Wood, Susanna A.; Wright, Michael; Zhang, Guang; Pochon, Xavier
• Is Part Of: Molecular ecology resources. Volume 22:Number 2(2022)
• Page Start: 519
• Page End: 538
• Publication Date: 2021-08-31
• Subjects: 18S ribosomal rRNA (18S rRNA) -- high‐throughput sequencing -- metabarcoding -- mitochondrial cytochrome c oxidase subunit 1 (COI) -- reproducibility -- standardization; Molecular ecology -- Periodicals; 572.8
• DOI: 10.1111/1755-0998.13485
• Languages: English
• ISSNs: 1755-098X

Abstract

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.