Data for: Detection and diversity of Phytophthora species from declining Quercus suber stands using both DNA metabarcoding and soil baiting techniques

<p>This dataset on Zenodo accompanies the manuscript Salvatore <em>et al.</em> (2024), Detection and diversity of <em>Phytophthora</em> species from declining <em>Quercus suber</em> stands using both DNA metabarcoding and soil baiting techniques.</p> <p>There are two files here on Zenodo:</p> <ul> <li><code>metadata.tsv</code>&nbsp;- plain text table as tab-separated variables</li> <li><code>raw_data.tar.gz</code> - compressed archive of 56 paired raw FASTQ files</li> </ul> <p>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.</p> <p>To repeat the analysis described in the paper, first install THAPBI PICT. See <a href="https://github.com/peterjc/thapbi-pict/">https://github.com/peterjc/thapbi-pict/ </a>for instructions. At the time of the paper, v1.0.16 was the current release.</p> <p>Next, decompress the raw data into a folder of paired gzipped FASTQ files. There is no need to decompress those:</p> <pre><code> $ tar -zxvf raw_data.tar.gz</code><br><code> $ ls -1 raw_data/</code></pre> <p>If you wish, verify the checksums to confirm the data integrity:</p> <pre><code> $ cd raw_data/ &nbsp; $ md5sum -c MD5SUM.txt</code><br><code> $ cd ..</code></pre> <p>Setup output directories:</p> <pre><code> &nbsp; $ mkdir -p intermediate/ summary/</code></pre> <p>Run the THAPBI PICT pipeline:</p> <pre><code> &nbsp; $ thapbi_pict pipeline -m 1s3g -f 0 -a 15 -i raw_data/ \</code><br><code> -s intermediate/ -o summary/sardinia \</code><br><code> -t metadata.tsv -u -x 8 -c 4,5,3,2,7,6</code></pre> <p>The options here are as follows:</p> <ul> <li><code>-m</code> - use the 1s3g classifier (see methods)</li> <li><code>-f</code> - set to zero to disable the fractional abundance threshold</li> <li><code>-a</code> - set a lower absolute abundance threshold</li> <li><code>-i</code> - location of the input raw data</li> <li><code>-s</code> - optional location to store intermediate files</li> <li><code>-o</code> - output stem for reports</li> <li><code>-t</code> - filename for tab-separated-variable metadata</li> <li><code>-u</code> - show unsequenced samples defined in the metadata</li> <li><code>-x</code> - which metadata column contains Illumina FASTQ filename stems</li> <li><code>-c</code> - which metadata columns to include in the report.</li> </ul> <p>This leaves the <code>-d</code> 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 <em>Phytophthora</em> reads.</p> <p>That takes under a minute to run, and classifies most of the samples.</p> <p>Opening the output file <code>summary/sardinia_20240912_v1.0.16.ITS1.samples.1s3g.xlsx</code> 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.</p> <p>All the <em>Phytophthora</em> classifications were to species level except a single ASV from E5BTB-DILUTE-Wet-P6_S17_L001 (S3 &nbsp;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:</p> <pre><code>&gt;bad82a53fff502146e7ea00cbf2d9d3e Phytophthora<br>TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCTAAAACTTTCCACGTGAACCGTTTCAAACCAAATAGTTGGGGGTCTTGTCTGGTGGCGGCTGCTGGCTTTATTGTTGGCGGCTGCTGCTGGGTGAGCCCTATCATGGCGAGCGTTTGGGCTTCGGCCTGAGCTAGTAGCATTTCTTTTAAACCCATTCCTTAATACTGATTATACT</code></pre> <p>There are 9 samples left simply as "Unknown" (all six from S2 Buddus&ograve;, 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.</p> <p>The unknown ASV from S1BTC-DILUTE-Wet-P6_S9_L001 (S3 Bultei) looks to be a novel <em>Phytophthora</em> if real, similar to&nbsp;<em>P. quercina</em>:</p> <pre><code>&gt;3cd110db0a0b8fa8b971ea6b61c53970 Phytophthora?<br>TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCTAAAAAACTTTCCACGTGAACCGTTTCAACCAATATTTTGGGGGTCTCGTCTGGCGTGCGGCTGTTGCTGTAAAAGGCGGCGGCTGTTGCTGGGTGAGCCCTATCATGGCAAACGTTTGGGCTTCGGTCTGAACAAGTAGCTCTTTTTTAAACCATTACTTATTACTGATTATACT</code></pre> <p>The unknown ASV from S4OC-DILUTE-Wet-P6_S2_L001 (S4 Nuoro) looks to be a <em>Pythium</em> (a perfect match to partial sequences MN269744 and KY822489 from uncultured clones):</p> <pre><code>&gt;eac8c1931b4f8c57803e6ad9ffb4eb56 Pythium?<br>TTTCCGTAGGTGAACCTGCGGAAGGATCATTACCACACCAAAAAACTATCCACGTGAACCGTTAAGCAAAAGTCTAGTTGGCTTGTGTTGTTCGGGAGTGTGTTGGGAAGAGCTTGGAGATGTCTTCGGATATTTCGATGCCTAGTACTGGACATCCTGGCGAGCGAGTCGGCTAGCAACGAAGGTCGGGAGTTCGCTTGCGGACTGATGTGCGCTTGTCGCATGTCGGTCGAAAGGCTTGAGCAAACGGCTGATCTATTACTTTTAAACCATACCATAACTACTGATGATACT</code></pre> <p>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:</p> <pre><code>&gt;74933d826f6077cd5f3dd036f894429d Artefact?<br>TAGCCGTAGGGGAACCTGCGGCTGGATCACCTCCTTTCTGGATTCGGAAGGCAGGGATCAGTGATCAGTTATCAGAACCGATTGCTGCTCCTCTTCCGAGCATCCACAACGCCAGCTCTGGCAGGAATTCTGATATCTGATATCTGGTTTCTGCGATCTGGCAACGGCGCCGCCGTCTGCGCATCCCTTCTGCCGCGTTATTCCAGAGGGCAGTGATCAGTCATCAGAACCGATGGCGGATCCGCCCCCGGCTTCACGGCGAGCGCCCGAGCCTG</code></pre> <p>The remaining unknown ASVs were likely <em>Plasmopara</em>.</p> <p>&nbsp;</p>