Comparison of primers for the detection of Phytophthora
Many oomycetes are important plant pathogens that cause devastating diseases in agricultural fields, orchards, urban areas, and natural ecosystems. Limitations and difficulties associated with isolating these pathogens have led to strong uptake of DNA metabarcoding and mass parallel sequencing. At least 20 primers combinations have been designed to amplify oomycetes or, more specifically, Phytophthora species from environmental samples. We used the Illumina sequencing platform to compare 13 primer combinations on mock communities and environmental samples. The primer combinations tested significantly varied in their ability to amplify Phytophthora species in a mock community and from environmental samples; this was due to either low sensitivity (unable to detect species present in low concentrations) or a lack of specificity (an inability to amplify some species even if they were present in high concentrations). Primers designed for oomycetes underestimated the Phytophthora community compared to Phytophthora-specific primers. We recommend using technical replicates, primer combinations, internal controls, and a phylogenetic approach for assigning a species identity to OTUs or ASVs. Particular care must be taken if sampling substrates where hybrid species could be expected. Overall, the choice of primers should depend upon the hypothesis being tested. The data files presented here contain the raw data for four separate metabarcoding runs analysed for this study in a fasta format
Steps to reproduce
Paired-end reads were merged using USEARCH v10 with a minimum overlap length of 50 bp with no gaps allowed in the merged alignments. Only forward reads were used for Primer sets P1 and P5. Sequence deconvolution, such as quality control and clustering, was also carried out using USEARCH v10. Specifically, sequences less than 200 bp and low mean quality (<20) were removed. Sequences that passed quality control were clustered into operational taxonomic units (OTUs) with a similarity threshold of 99%.