WSL2 is a Windows feature that allows near-native execution of ELF binaries like Unix bash and Gnome apps. With GPU acceleration support, it turns a gaming PC into a powerful bioinformatics workstation without needing to dual-boot.
This guide details how to install WSL2 on a fresh Windows 11 (25H2) system and use Pixi, a high-performance package manager, to install SpliceAI and its GPU dependencies.
Windows Key + R, type cmd, and press Enter to open Terminal.Build an Azure stack to operate NP-series VMs on Azure with Dragen's pay-as-you-go (PAYG) license.
Microsoft.Network, Microsoft.Storage, and Microsoft.Compute.Status is set to Enable for the Azure subscription you intend to use. T
Prototype Illumina's Nirvana on Dragen Pay-As-You-Go (PAYG) VMs on Azure.
Microsoft.Network, Microsoft.Storage, and Microsoft.Compute. Strictly speaking, you don't need Microsoft.Storage for the steps in this guide. But in a production environment, it is recommended to use blob storage for inputs/outputs. You should also upload Nirvana annotation source files into your own blob storage account so that you can quickly deploy them into multiple ephemeral VMs that run Nirvana in parallel on each sample.
Reproduce the double free or corruption (fasttop) error from Dragen 4.3.6 on their CentOS 7.9 image on Azure NP10 VMs.
This error was linked to Dragen's code for streaming FASTQs from Azure blob storage. As a workaround, we now use azcopy to copy FASTQs onto VM local storage before alignment, which significantly reduces runtime too. The errors also disappear in AlmaLinux 8, which all newer Dragen VM images are based on.
A proof-of-concept high-performance server for primary and secondary NGS analyses with reasonable cost and TAT.
Acquired a Dell Precision 5820 tower workstation in mid 2018 with the following specs. Minimally, you want fast single-thread performance, at least 64GB RAM preferably ECC, and very speedy disks. A GPU with at least 16GB VRAM allows you to run Nvidia's Parabricks v4.4 or at least 12GB VRAM for Parabricks v3.8.
| # GOAL: Prototype a bioinformatics pipeline to calculate Polygenic Risk Scores (PRS) using WGS gVCFs. | |
| # Let's use pgscalc (https://github.com/pgscatalog/pgsc_calc) a nextflow pipeline to calculate PRS, given PR weights and | |
| # a multi-sample VCF. Variant allele weights can be specified either as PGS Catalog IDs (--pgs_id) and/or as custom scoring | |
| # files (--scorefile). pgscalc can also perform liftover if scoring files use a different reference genome build than the | |
| # input VCFs (--liftover --target_build). | |
| # Steps below were tested on Ubuntu 24.04. But should work fine with any Linux server using bash. | |
| # ----- # |
On a Linux VM or Workstation with docker installed, fetch the GRCh38 FASTA, its index, and a pair of FASTQs:
wget -P /hot/ref https://storage.googleapis.com/genomics-public-data/references/GRCh38_Verily/GRCh38_Verily_v1.genome.fa{,.fai}
wget -P /hot/reads/test https://storage.googleapis.com/data.cyri.ac/test_L001_R{1,2}_001.fastq.gzIf on a Slurm cluster, here is an example of wrapping a docker run command in an sbatch request:
sbatch --chdir=/hot --output=ref/std.out --error=ref/std.err --nodes=1 --ntasks-per-node=1 --cpus-per-task=8 --mem=30G --time=4:00:00 --wrap="docker run --help"
Ensembl's VEP (Variant Effect Predictor) is popular for how it picks a single effect per gene as detailed here, its CLIA-compliant HGVS variant format, and Sequence Ontology nomenclature for variant effects.
The official instructions to install VEP have never worked well from the United States because of the flaky network connection to their FTP servers in the UK. So, we will instead use conda to install VEP and its dependencies and then manually download VEP caches and reference genomes using rsync.
If you don't already have conda, download and install it into $HOME/miniconda3:
curl -sL https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o miniconda.sh
bash miniconda.sh -bup $HOME/minic
Ensembl's VEP (Variant Effect Predictor) is popular for how it picks a single effect per gene as detailed here, its CLIA-compliant HGVS variant format, and Sequence Ontology nomenclature for variant effects.
Instead of the official instructions, we will use conda to install VEP and its dependencies. If you don't already have conda, install it into $HOME/miniconda3 as follows:
curl -sL https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh -o /tmp/miniconda.sh
sh /tmp/miniconda.sh -bfp $HOME/miniconda3Add the conda bin folder into your $PATH so that all installed tools are accessible via command-line. You can also add this to your ~/.bashrc
| # Fetch the WGS gnomAD 3.1.2 per-chrom VCFs (the large size is mostly due to INFO fields): | |
| mkdir gnomad | |
| gsutil -m cp gs://gcp-public-data--gnomad/release/3.1.2/vcf/genomes/gnomad.genomes.v3.1.2.sites.chr*.vcf.bgz gnomad | |
| gsutil -m cp gs://gcp-public-data--gnomad/release/3.1.2/vcf/genomes/gnomad.genomes.v3.1.2.sites.chr*.vcf.bgz.tbi gnomad | |
| # Shortlist INFO fields we want to keep when merging these into a single VCF of reduced file size: | |
| bcftools view -h gnomad/gnomad.genomes.v3.1.2.sites.chr21.vcf.bgz | grep ^##INFO | cut -f3- -d= | grep -Ev "controls|non_cancer|non_neuro|non_topmed|non_v2|vep" | sort | less -S | |
| cadd_phred | |
| cadd_raw_score |