期刊
MOLECULAR ECOLOGY RESOURCES
卷 22, 期 2, 页码 519-538出版社
WILEY
DOI: 10.1111/1755-0998.13485
关键词
18S ribosomal rRNA (18S rRNA); high-throughput sequencing; metabarcoding; mitochondrial cytochrome c oxidase subunit 1 (COI); reproducibility; standardization
资金
- New Zealand Ministry of Business, Innovation and Employment [C01X1527]
- New Zealand Ministry of Business, Innovation and Employment funding [CAWX1904-A]
- NZ Government's Strategic Science Investment Fund (SSIF) through NIWA Coasts & Oceans Programme [6 - 2019/20 SCI]
- New Zealand Ministry of Business, Innovation & Employment (MBIE) [C01X1527] Funding Source: New Zealand Ministry of Business, Innovation & Employment (MBIE)
Advances in high-throughput sequencing technology are changing marine monitoring by enabling rapid and accurate detection of species in complex samples. An international experiment showed that while there was variation in results from different laboratories, the primary signal in the data was consistent, with samples grouping by geographical origin. Post hoc data clean-up, such as removing low-quality samples, improved sample classification accuracy significantly.
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.
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