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Dataset results
7 results for “HPRC”
Minigraph pangenome graphs for HPRC samples
<p>Minigraph pangenome graphs and derived results for HPRC samples. Glenn Hickey generated the graphs. Heng Li produced the per-assembly variant calls and annotated the alleles. See 00README.txt for file description.</p>
HLA and KIR allele genotyping for HPRC-frz2 haplotype assemblies
<p>HLA/KIR annotation for available long reads assemblies, constructed by the pipeline : https://github.com/YingZhou001/Immuannot</p> <p>IPD-KIR version: V2.13.0, IPD-IMGT/HLA version: V3.59.0</p> <p>472 haploid assemblies included</p>
Epigenomic analysis on the HPRC genome graphs
<p>Processed data, analysis and figure code for the epigenomic analysis on the HPRC genome graph.</p>
HPRC PanGenie results
<p><strong>HPRC PanGenie results</strong></p> <p>Input VCF used for PanGenie for the HPRC experiments as well as genotyping results, statistics and filters computed. All experiments are based on the Minigraph-Cactus (MC) graph.</p> <p>Experiments were run at Heinrich-Heine University Düsseldorf by Jana Ebler (ebler@hhu.de). Pipelines used to produce these results are here: <a href="http://bitbucket.org/jana_ebler/hprc-experiments/src/master/genotyping-experiments/">http://bitbucket.org/jana_ebler/hprc-experiments/src/master/genotyping-experiments/</a></p> <p> </p> <p><strong>How to run PanGenie on the MC variants</strong></p> <p>We ran PanGenie using the file "cactus_filtered_ids.vcf.gz" as input (contained in this repository). It was produced from the file "hprc-v1.0-mc-grch38.vcf.gz" generated from the MC graph using vg decompose. The output VCF generated by PanGenie can be converted into a bi-allelic VCF containing a single record for each (nested) variant allele, i.e. after decomposing large bubbles into their nested variants using the script: <a href="https://bitbucket.org/jana_ebler/hprc-experiments/src/master/genotyping-experiments/workflow/scripts/convert-to-biallelic.py">https://bitbucket.org/jana_ebler/hprc-experiments/src/master/genotyping-experiments/workflow/scripts/convert-to-biallelic.py</a></p> <pre><code># run PanGenie, produces genotyped VCF "pangenie_genotyping.vcf" PanGenie -i <input-reads> -v cactus_filtered_ids.vcf -r <reference-genome> -o pangenie -j 24 -t 24 # decompose bubbles and produce a bi-allelic VCF with genotypes for each (nested) allele cat pangenie_genotyping.vcf | python3 convert-to-biallelic.py cactus_filtered_ids_biallelic.vcf > pangenie_genotyping_biallelic.vcf</code></pre> <p> </p> <p> </p>
HPRC-CHM13 PanGenie results
<p><strong>HPRC-CHM13 PanGenie results</strong></p> <p>Input VCF used for PanGenie for the HPRC experiments based on CHM13, as well as genotyping results, statistics and filters computed. All experiments are based on the Minigraph-Cactus (MC) graph. PanGenie v2.1.1 was used.</p> <p><strong>Note:</strong> PanGenie results for CHM13 provided here are <strong>not</strong> directly comparable to the GRCh38-based ones provided here: <a href="http://doi.org/10.5281/zenodo.6797328">https://doi.org/10.5281/zenodo.6797328</a>. While v1.0.0 was used to produce GRCh38-based results, a newer, more accurate version (v2.1.1) was used to produce the CHM13-based results provided here.</p> <p>Experiments were run at Heinrich-Heine University Düsseldorf by Jana Ebler (ebler@hhu.de). Pipelines used to produce these results are here: <a href="/github.com/eblerjana/genotyping-pipelines/tree/main/benchmarking-pipeline">https://github.com/eblerjana/genotyping-pipelines/tree/main/benchmarking-pipeline</a></p> <p> </p> <p><strong>How to run PanGenie on the MC variants</strong></p> <p>We ran PanGenie using the file "chm13_cactus_filtered_ids.vcf.gz" as input (contained in this repository). It was produced from the file "hprc-v1.0-mc-chm13.vcf.gz" generated from the MC graph using vg decompose. The output VCF generated by PanGenie can be converted into a bi-allelic VCF containing a single record for each (nested) variant allele, i.e. after decomposing large bubbles into their nested variants using the script: <a href="https://bitbucket.org/jana_ebler/hprc-experiments/src/master/genotyping-experiments/workflow/scripts/convert-to-biallelic.py">https://bitbucket.org/jana_ebler/hprc-experiments/src/master/genotyping-experiments/workflow/scripts/convert-to-biallelic.py</a></p> <pre><code># run PanGenie, produces genotyped VCF "pangenie_genotyping.vcf" PanGenie -i <input-reads> -v chm13_cactus_filtered_ids.vcf -r <reference-genome> -o pangenie -j 24 -t 24 # decompose bubbles and produce a bi-allelic VCF with genotypes for each (nested) allele cat pangenie_genotyping.vcf | python3 convert-to-biallelic.py chm13_cactus_filtered_ids_biallelic.vcf > pangenie_genotyping_biallelic.vcf</code></pre> <p> </p> <p> </p>
High-resolution KIR allele annotations for HPRC 47 assemblies and other reference genomes using SKIRT
<p><strong>Leveraging the high-quality genome assemblies from the Human Pangenome Reference Consortium (HPRC), we present a novel bioinformatic tool, the Structural KIR annoTator (SKIRT), to annotate, identify, and discover hundreds of novel alleles, more than a dozen structural variations among KIR genes, and all haplotypes carrying novel alleles, confirming KIR genetic diversity. The use of HPRC data needs to comply with the rules announced by HPRC.</strong></p>
Instructions on how to run PanGenie on HGSVC3 + HPRC data
<h2>Input Data</h2> <h3>Reference genome</h3> <p>The reference genome used (CHM13) can be obtained from: <a href="https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/analysis_set/chm13v2.0.fa.gz">https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/analysis_set/chm13v2.0.fa.gz</a></p> <h3>Reads</h3> <p>We used the raw 1kg Illumina reads (FASTA/FASTQ) obtained from: <a href="http://ftp.sra.ebi.ac.uk/vol1/fastq/">http://ftp.sra.ebi.ac.uk/vol1/fastq/</a>.<br>We created a single FASTQ file per sample by concatenating individual files, however, PanGenie can also be run with multiple files per sample (see instructions below on how this is done).</p> <h3>Input panel VCF</h3> <p>The input VCF was derived from Minigraph-Cactus VCFs produced from 65 HGSVC3 and 42 HPRC assemblies. It is available here in this repository: <code> MC_hgsvc3-hprc_chm13_filtered_bubbles.vcf.gz </code><br>There is an additional VCF containing decomposed variant alleles, which will be needed to postprocess the PanGenie output (in order to translate bubble genotypes to genotypes for all nested variants). This VCF is also available in this repository and is called: <code>MC_hgsvc3-hprc_chm13_filtered_decomposed.vcf.gz</code></p> <h2>Running PanGenie</h2> <p>We used PanGenie version v3.1.0 and ran it via singularity as described in the README (<a href="https://github.com/eblerjana/pangenie/tree/master">https://github.com/eblerjana/pangenie/tree/master</a>). The singularity image used is available in this repository: <code>eblerjana_eblerjana_pangenie-v3.1.0.sif</code></p> <h3>Commands used for genotyping</h3> <p><strong>(1) Indexing (run only once):</strong></p> <p> <code> PanGenie-index -v MC_hgsvc3-hprc_chm13_filtered_bubbles.vcf -r chm13v2.0.fa -o index -t 24 </code></p> <ul> <li>Note: the input panel VCF (<code>-v</code>) as well as the reference genome (<code>-r</code>) need to be uncompressed.</li> </ul> <p><strong>(2) Genotyping (run for each sample):</strong></p> <p><code> PanGenie -a 108 -f index -i <(zcat <sample>.R1.fq.gz <sample>.R2.fq.gz) -o pangenie_<sample> -j 24 -t 24 -s <sample> </code></p> <ul> <li><code>-j</code> and <code>-t</code> specify the number of threads to be used for internal k-mer counting and genotyping steps (here 24 cores are used for both).</li> <li>The <code>-i</code> parameter is used to provide input reads (in FASTA/FASTQ format). If you have more than one file per sample (e.g. for paired-end reads) use the syntax shown in the command above to avoid having to concatenate the files beforehand.</li> </ul> <p><br><strong>(3) Decompose bubbles (run for each sample)</strong></p> <p><code>cat pangenie_<sample>_genotyping.vcf | python3 convert-to-biallelic.py MC_hgsvc3-hprc_chm13_filtered_decomposed.vcf > pangenie_<sample>_genotyping_biallelic.vcf</code></p> <ul> <li>Note: the <code>convert-to-biallelic.py</code> script is provided in this repository.</li> </ul>
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