Disentangling bias for non-destructive insect metabarcoding

PeerJ

article

Francesco Martoni¹  Alexander M. Piper¹  Brendan C. Rodoni¹  Mark J. Blacket¹ 

[1] Agriculture Victoria Research, AgriBio Centre for AgriBioscience;School of Applied Systems Biology, La Trobe University

关键词: High throughput sequencing;    Barcoding;    Biodiversity;    Primer bias;    Entomology;    Biosecurity;   

DOI  :  10.7717/peerj.12981

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

A fast and reliable method for obtaining a species-level identification is a fundamental requirement for a wide range of activities, from plant protection and invasive species management to biodiversity assessments and ecological studies. For insects, novel molecular techniques such as DNA metabarcoding have emerged as a rapid alternative to traditional morphological identification, reducing the dependence on limited taxonomic experts. Until recently, molecular techniques have required a destructive DNA extraction, precluding the possibility of preserving voucher specimens for future studies, or species descriptions. Here we paired insect metabarcoding with two recent non-destructive DNA extraction protocols, to obtain a rapid and high-throughput taxonomic identification of diverse insect taxa while retaining a physical voucher specimen. The aim of this work was to explore how non-destructive extraction protocols impact the semi-quantitative nature of metabarcoding, which alongside species presence/absence also provides a quantitative, but biased, representation of their relative abundances. By using a series of mock communities representing each stage of a typical metabarcoding workflow we were able to determine how different morphological (i.e., insect biomass and exoskeleton hardness) and molecular traits (i.e., primer mismatch and amplicon GC%), interact with different protocol steps to introduce quantitative bias into non-destructive metabarcoding results. We discuss the relevance of taxonomic bias to metabarcoding identification of insects and potential approaches to account for it.

【 授权许可】

CC BY