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8 results for “chm13”
Notable genomic regions in T2T-CHM13 and GRCh38
<p>See 00README.md</p>
Joseph Lalli CHM13-T2T Pangenie reference vcf - unofficial
<p>hprc-jun1-mc-chm13.pangenie_panel.bcf.gz: Draft pangenome (Jun1-mc version) in CHM13-T2T coordinates</p> <p>hprc-jun1-mc-chm13.fasta: CHM13 reference paths from Jun1-mc draft pangenome.</p> <p>hprc-jun1-mc-chm13.fasta.fai: Reference index.</p> <p>pangenie.def and pangenie.sif: Singularity container to run Pangenie 2.1</p> <p>run_pangenie.sh: Bash script to run pangenie on HTCondor servers. Specifically, the script downloads a specified 1KGP unaligned cram file, extracts the reads locally, creates a checkpoint .jf kmer count table, and then runs Pangenie on the .jf file. The resulting vcf file is compressed to a bcf.gz file, and is saved in the staging directory.</p> <p>HG02030.pangenie.bcf.gz: An example of the output from the 1KGP sample HG02030.</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>
De novo genome assembly of human cell line CHM13 nanopore ultra-long reads using Shasta
Open the record for dataset details and reuse information.
chm13_asm_ctgs_2021_0104 / Jason Chin
<p>Genome assembly of the CHM13 cell line. Done with a new assembler based on the SHIMMER indexing method. The run time is 1.6 hours with 64 core aarch CPUs. The wall clock time is about 1.6 hours. </p> <p>2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] Total size: 3055717214<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] Longest size: 140284871<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] N50: 82577644<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] N90: 17313701<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] Number of contigs: 357<br> 2021-01-04 08:47:07,980 INFO [pg_asm::utils::layout] Number of Contigs > 100kb: 207</p> <p>2021-01-04 08:48:22,512 INFO [pg_asm] pg_asm run end<br> 2021-01-04 08:48:22,512 INFO [pg_asm] total user cpu time: 272568 seconds = 75.71333 hours<br> 2021-01-04 08:48:22,512 INFO [pg_asm] total system cpu time: 920 seconds = 0.25555554 hours<br> 2021-01-04 08:48:22,512 INFO [pg_asm] total elapse time: 5773.4155 seconds = 1.6037266 hours</p> <p>Here is the result of the CHM13 BAC coverage with the assembly</p> <p><br> Checking attempted with maximum distance from contig end of 1000 bp..Done<br> ******************* BAC SUMMARY ******************<br> TOTAL : 341<br> BP : 51532183<br> ************** Statistics for: _test9.fa ****************<br> BACs closed: 330 (96.7742); BACs attempted: 341 %good = 96.7742; BASES 49874385 (96.783)<br> Median: 99.99135<br> MedianQV: 40.62984<br> Mean: 99.94396<br> MeanQV: 32.51511<br> ***** STATS IGNORING INDELS ********************<br> Median: 100<br> MedianQV: Inf<br> Mean: 99.99418<br> MeanQV: 42.34828<br> **********************************************</p>
Sequencing reads simulated with ART from Homo sapiens CHM13 chr 21
<p>Sequencing reads simulated with ART from Homo sapiens CHM13 chr 21</p>
BLEND: A Fast, Memory-Efficient, and Accurate Mechanism to Find Fuzzy Seed Matches in Genome Analysis (chm13-ont-pbsim2)
<p>Simulated 30X CHM13 ONT reads. PBSIM2 with R95 model is used to generate the reads.</p>
ChIP-Seq of SA1, SA2, and Rad21 in RPE-1 and CHM13 cells
GEO Series GSE240957. Homo sapiens. 20 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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