DOI: 10.5281/zenodo.13753737
title: Data for: Detection and diversity of Phytophthora species from declining Quercus suber stands using both DNA metabarcoding and soil baiting techniques

description: This dataset on Zenodo accompanies the manuscript Salvatore et al. (2024), Detection and diversity of Phytophthora species from declining Quercus suber stands using both DNA metabarcoding and soil baiting techniques.

There are two files here on Zenodo:



metadata.tsv - plain text table as tab-separated variables

raw_data.tar.gz - compressed archive of 56 paired raw FASTQ files


This represents a subset of one complete Illumina Nano MiSeq plate run at the James Hutton Institute also containing a small number of unrelated samples using the same protocol.

To repeat the analysis described in the paper, first install THAPBI PICT. See https://github.com/peterjc/thapbi-pict/ for instructions. At the time of the paper, v1.0.16 was the current release.

Next, decompress the raw data into a folder of paired gzipped FASTQ files. There is no need to decompress those:

    $ tar -zxvf raw_data.tar.gz    $ ls -1 raw_data/

If you wish, verify the checksums to confirm the data integrity:

    $ cd raw_data/
    $ md5sum -c MD5SUM.txt    $ cd ..

Setup output directories:

   $ mkdir -p intermediate/ summary/

Run the THAPBI PICT pipeline:

   $ thapbi_pict pipeline -m 1s3g -f 0 -a 15 -i raw_data/ \      -s intermediate/ -o summary/sardinia \      -t metadata.tsv -u -x 8 -c 4,5,3,2,7,6

The options here are as follows:



-m - use the 1s3g classifier (see methods)

-f - set to zero to disable the fractional abundance threshold

-a - set a lower absolute abundance threshold

-i - location of the input raw data

-s - optional location to store intermediate files

-o - output stem for reports

-t - filename for tab-separated-variable metadata

-u - show unsequenced samples defined in the metadata

-x - which metadata column contains Illumina FASTQ filename stems

-c - which metadata columns to include in the report.


This leaves the -d option with the default provided ITS1 database. We are NOT taking advantage of the negative controls to automatically set a blanket minimum abundance as Control-Plate-3-Mix-3-Dry-P3-c_S56_L001 sadly has over 3000 Phytophthora reads.

That takes under a minute to run, and classifies most of the samples.

Opening the output file summary/sardinia_20240912_v1.0.16.ITS1.samples.1s3g.xlsx in Excel or similar should show you a table resembling Table 3 in the paper, without the baiting results, but with one row per sequencing sample, and additional columns with per-sample per-species read counts etc. The similarly named reads file as one row per unique amplicon sequence variant (ASV), and columns for each sequencing sample.

All the Phytophthora classifications were to species level except a single ASV from E5BTB-DILUTE-Wet-P6_S17_L001 (S3  Bultei) with 15 reads, a perfect match to partial sequences MH588088.1 and MH593844.1, isolates Y1 and Y2, which is in the THAPBI PICT database but only at genus level:

>bad82a53fff502146e7ea00cbf2d9d3e PhytophthoraTTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCTAAAACTTTCCACGTGAACCGTTTCAAACCAAATAGTTGGGGGTCTTGTCTGGTGGCGGCTGCTGGCTTTATTGTTGGCGGCTGCTGCTGGGTGAGCCCTATCATGGCGAGCGTTTGGGCTTCGGCCTGAGCTAGTAGCATTTCTTTTAAACCCATTCCTTAATACTGATTATACT

There are 9 samples left simply as "Unknown" (all six from S2 Buddusò, one each from S3 Bultei, S4 Nuoro, and S6 Tempio). A further two samples contained low levels of an unknown sequence in addition to knowns.

The unknown ASV from S1BTC-DILUTE-Wet-P6_S9_L001 (S3 Bultei) looks to be a novel Phytophthora if real, similar to P. quercina:

>3cd110db0a0b8fa8b971ea6b61c53970 Phytophthora?TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCTAAAAAACTTTCCACGTGAACCGTTTCAACCAATATTTTGGGGGTCTCGTCTGGCGTGCGGCTGTTGCTGTAAAAGGCGGCGGCTGTTGCTGGGTGAGCCCTATCATGGCAAACGTTTGGGCTTCGGTCTGAACAAGTAGCTCTTTTTTAAACCATTACTTATTACTGATTATACT

The unknown ASV from S4OC-DILUTE-Wet-P6_S2_L001 (S4 Nuoro) looks to be a Pythium (a perfect match to partial sequences MN269744 and KY822489 from uncultured clones):

>eac8c1931b4f8c57803e6ad9ffb4eb56 Pythium?TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCAAAAAACTATCCACGTGAACCGTTAAGCAAAAGTCTAGTTGGCTTGTGTTGTTCGGGAGTGTGTTGGGAAGAGCTTGGAGATGTCTTCGGATATTTCGATGCCTAGTACTGGACATCCTGGCGAGCGAGTCGGCTAGCAACGAAGGTCGGGAGTTCGCTTGCGGACTGATGTGCGCTTGTCGCATGTCGGTCGAAAGGCTTGAGCAAACGGCTGATCTATTACTTTTAAACCATACCATAACTACTGATGATACT

The unknown ASV from S9OC-Wet-P5_S12_L001 (S4 Nuoro) at only 38 reads seems to be an artefact of some kind, possibly chimeric:

>74933d826f6077cd5f3dd036f894429d Artefact?TAGCCGTAGGGGAACCTGCGGCTGGATCACCTCCTTTCTGGATTCGGAAGGCAGGGATCAGTGATCAGTTATCAGAACCGATTGCTGCTCCTCTTCCGAGCATCCACAACGCCAGCTCTGGCAGGAATTCTGATATCTGATATCTGGTTTCTGCGATCTGGCAACGGCGCCGCCGTCTGCGCATCCCTTCTGCCGCGTTATTCCAGAGGGCAGTGATCAGTCATCAGAACCGATGGCGGATCCGCCCCCGGCTTCACGGCGAGCGCCCGAGCCTG

The remaining unknown ASVs were likely Plasmopara.