NanoVarBench variant truthset files

<p>These tarballs contain the variant truthsets used for each sample in our <a href="https://doi.org/10.1101/2024.03.15.585313">paper</a> "<em>Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data</em>".</p> <p>Each directory contains the following files:</p> <ul> <li>&nbsp;<code>&lt;sample&gt;.bed</code> - A BED file of all regions in the genome</li> <li><code>&lt;sample&gt;.repetitive_regions.bed </code>- A BED file of all repetitive regions of the genome (see the paper for details of how these were identified).</li> <li><code>&lt;sample&gt;.unique_regions.bed</code> - Non-repetitive regions of the genome. This is the result of performing&nbsp;<code>bedtools complement -i &lt;repetitive BED&gt; -g &lt;faidx of mutref&gt;</code></li> <li><code>ani.tsv</code> - skani output from skani search for the sample's assembly against all of the downloaded genomes for that species. The last three columns are not from skani. They are completeness_percentile, completeness, and contamination metrics, all obtained from NBCI for each assembly accession.</li> <li><code>apply.vcf.gz</code> - the variants that were applied to the sample's reference assembly.</li> <li><code>apply.vcf.gz.csi</code> - VCF index for the above VCF</li> <li><code>dnadiff.vcf.gz</code> - Variants between the sample and donor genome from mummer4</li> <li><code>minimap2.vcf.gz</code> - Variants between the sample and donor genome from minimap2</li> <li><code>mutdonor.fna</code> - the FASTA file of the selected variant donor</li> <li><code>mutreference.fna</code> - the sample's reference assembly with the apply.vcf.gz applied to it. This is the genome that the sample's read are aligned to for calling variants</li> <li><code>mutreference.fna.fai</code> - the faidx of the above genome</li> <li><code>reference.fna </code>- the reference assembly of the sample. These are also available on GenBank, but are included here for interoperability</li> <li><code>truth.vcf.gz</code> - the truthset of variants. This is essentially apply.vcf.gz with the REF and ALT invert and the POS adjusted for the difference in position between the sample and donor assemblies. (See <a href="https://github.com/mbhall88/NanoVarBench/blob/31d9f07e9de04f0accdf30b666d69ae71069559a/workflow/scripts/create_mutref.py#L986-L1012" target="_blank" rel="noopener">this script</a>)</li> <li><code>vcfstats.txt</code> - VCF statistics produced by paftools.js vcfstat on the truth VCF</li> </ul> <p>For information about each sample, refer to the <a href="https://github.com/mbhall88/NanoVarBench/blob/31d9f07e9de04f0accdf30b666d69ae71069559a/config/pep/samples.csv" target="_blank" rel="noopener">samplesheet</a> and <a href="https://doi.org/10.1101/2024.03.15.585313" target="_blank" rel="noopener">paper</a>.</p>