Format

Send to

Choose Destination
Sci Rep. 2017 Dec 15;7(1):17668. doi: 10.1038/s41598-017-17333-x.

Estimating and mitigating amplification bias in qualitative and quantitative arthropod metabarcoding.

Author information

1
Department of Environmental Sciences, Policy and Management University of California Berkeley Mulford Hall, Berkeley, California, USA. Krehenwinkel@berkeley.edu.
2
Center for Comparative Genomics California Academy of Sciences Music Concourse Drive, San Francisco, California, USA. Krehenwinkel@berkeley.edu.
3
Department of Environmental Sciences, Policy and Management University of California Berkeley Mulford Hall, Berkeley, California, USA.
4
Center for Comparative Genomics California Academy of Sciences Music Concourse Drive, San Francisco, California, USA.

Abstract

Amplicon based metabarcoding promises rapid and cost-efficient analyses of species composition. However, it is disputed whether abundance estimates can be derived from metabarcoding due to taxon specific PCR amplification biases. PCR-free approaches have been suggested to mitigate this problem, but come with considerable increases in workload and cost. Here, we analyze multilocus datasets of diverse arthropod communities, to evaluate whether amplification bias can be countered by (1) targeting loci with highly degenerate primers or conserved priming sites, (2) increasing PCR template concentration, (3) reducing PCR cycle number or (4) avoiding locus specific amplification by directly sequencing genomic DNA. Amplification bias is reduced considerably by degenerate primers or targeting amplicons with conserved priming sites. Surprisingly, a reduction of PCR cycles did not have a strong effect on amplification bias. The association of taxon abundance and read count was actually less predictable with fewer cycles. Even a complete exclusion of locus specific amplification did not exclude bias. Copy number variation of the target loci may be another explanation for read abundance differences between taxa, which would affect amplicon based and PCR free methods alike. As read abundance biases are taxon specific and predictable, the application of correction factors allows abundance estimates.

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center