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Apptainer def files, env input yamls for build.
README instructions for build and run.
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# Build Container | ||
apptainer build deeprvat_preprocessing.sif apptainer_deeprvat_preprocessing.def | ||
apptainer build deeprvat.sif apptainer_deeprvat.def | ||
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NOTE: deeprvat image ~= 5 GB, deeprvat_preprocessing image ~= 1 GB | ||
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# Verify Working Image | ||
apptainer exec deeprvat_preprocessing.sif pip list | grep "deeprvat" | ||
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# Run Preprocessing | ||
cd ./my_exp_dir | ||
. | ||
├── data | ||
│ └── vcf | ||
│ ├── test_vcf_data_c21_b1.vcf.gz | ||
│ └── test_vcf_data_c22_b1.vcf.gz | ||
├── deeprvat_preprocess_config.yaml | ||
├── deeprvat_preprocessing.sif | ||
└── vcf_files_list.txt | ||
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[~/my_exp_dir]$ apptainer run deeprvat_preprocessing.sif snakemake -j 1 --snakefile /opt/deeprvat/pipelines/preprocess_no_qc.snakefile --configfile deeprvat_preprocess_config.yaml --ri -n | ||
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# Run Training + Association Testing | ||
cd ./my_exp_dir | ||
. | ||
├── annotations.parquet | ||
├── baseline_results | ||
│ ├── Apolipoprotein_A | ||
│ │ ├── missense | ||
│ │ │ ├── burden | ||
│ │ │ │ └── eval | ||
│ │ │ │ └── burden_associations.parquet | ||
│ │ │ └── skat | ||
│ │ │ └── eval | ||
│ │ │ └── burden_associations.parquet | ||
│ │ └── plof | ||
│ │ ├── burden | ||
│ │ │ └── eval | ||
│ │ │ └── burden_associations.parquet | ||
│ │ └── skat | ||
│ │ └── eval | ||
│ │ └── burden_associations.parquet | ||
│ ├── Calcium | ||
│ │ ├── missense | ||
│ │ │ ├── burden | ||
│ │ │ │ └── eval | ||
│ │ │ │ └── burden_associations.parquet | ||
│ │ │ └── skat | ||
│ │ │ └── eval | ||
│ │ │ └── burden_associations.parquet | ||
│ │ └── plof | ||
│ │ ├── burden | ||
│ │ │ └── eval | ||
│ │ │ └── burden_associations.parquet | ||
│ │ └── skat | ||
│ │ └── eval | ||
│ │ └── burden_associations.parquet | ||
│ ├── Cholesterol | ||
│ │ ├── missense | ||
│ │ │ ├── burden | ||
│ │ │ │ └── eval | ||
│ │ │ │ └── burden_associations.parquet | ||
│ │ │ └── skat | ||
│ │ │ └── eval | ||
│ │ │ └── burden_associations.parquet | ||
│ │ └── plof | ||
│ │ ├── burden | ||
│ │ │ └── eval | ||
│ │ │ └── burden_associations.parquet | ||
│ │ └── skat | ||
│ │ └── eval | ||
│ │ └── burden_associations.parquet | ||
│ ├── Platelet_count | ||
│ │ ├── missense | ||
│ │ │ ├── burden | ||
│ │ │ │ └── eval | ||
│ │ │ │ └── burden_associations.parquet | ||
│ │ │ └── skat | ||
│ │ │ └── eval | ||
│ │ │ └── burden_associations.parquet | ||
│ │ └── plof | ||
│ │ ├── burden | ||
│ │ │ └── eval | ||
│ │ │ └── burden_associations.parquet | ||
│ │ └── skat | ||
│ │ └── eval | ||
│ │ └── burden_associations.parquet | ||
├── deeprvat_config.yaml | ||
├── deeprvat.sif | ||
├── gencode.v38.basic.annotation.gtf.gz | ||
├── genotypes.h5 | ||
├── phenotypes.parquet | ||
├── protein_coding_genes.parquet | ||
└── variants.parquet | ||
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[~/my_exp_dir]$ apptainer run deeprvat.sif snakemake -j 1 --snakefile /opt/deeprvat/pipelines/training_association_testing.snakefile --configfile deeprvat_config.yaml --ri -n |
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Bootstrap: docker | ||
From: mambaorg/micromamba:2.0-ubuntu22.04 | ||
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%files | ||
deeprvat_env.yaml /opt/deeprvat_env.yaml | ||
%environment | ||
export MAMBA_ROOT_PREFIX=/opt/conda | ||
export PATH="$MAMBA_ROOT_PREFIX/bin:$PATH" | ||
%post | ||
apt-get -y update && apt-get -y install cargo g++ | ||
micromamba install -q -y -n base -f /opt/deeprvat_env.yaml | ||
micromamba clean --all --yes | ||
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micromamba run -n base git clone https://github.com/PMBio/deeprvat /opt/deeprvat | ||
micromamba run -n base pip install -e /opt/deeprvat | ||
%runscript | ||
exec micromamba run -n base "$@" | ||
%labels | ||
OWNER: https://github.com/PMBio/deeprvat | ||
EnvironmentFile: /opt/deeprvat_env.yaml | ||
%help | ||
This container containes the deepRVAT repository and the relevant | ||
Python environment created from an environment file. | ||
To run Python from the environment: | ||
apptainer run my_environment.sif python | ||
To see the environment file used to create the environment: | ||
apptainer run my_environment.sif cat /opt/deeprvat_env.yaml | ||
To see what packags are in the environment: | ||
apptainer run my_environment.sif micromamba list |
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Bootstrap: docker | ||
From: mambaorg/micromamba:2.0-ubuntu22.04 | ||
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%files | ||
deeprvat_preprocessing_env.yml /opt/deeprvat_preprocessing_env.yml | ||
%environment | ||
export MAMBA_ROOT_PREFIX=/opt/conda | ||
export PATH="$MAMBA_ROOT_PREFIX/bin:$PATH" | ||
%post | ||
micromamba install -q -y -n base -f /opt/deeprvat_preprocessing_env.yml | ||
micromamba clean --all --yes | ||
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micromamba run -n base git clone https://github.com/PMBio/deeprvat /opt/deeprvat | ||
micromamba run -n base pip install -e /opt/deeprvat | ||
%runscript | ||
exec micromamba run -n base "$@" | ||
%labels | ||
OWNER: https://github.com/PMBio/deeprvat | ||
EnvironmentFile: /opt/deeprvat_preprocessing_env.yml | ||
%help | ||
This container containes the deepRVAT repository and the relevant | ||
Python environment created from an environment file. | ||
To run Python from the environment: | ||
apptainer run my_environment.sif python | ||
To see the environment file used to create the environment: | ||
apptainer run my_environment.sif cat /opt/deeprvat_preprocessing_env.yml | ||
To see what packags are in the environment: | ||
apptainer run my_environment.sif micromamba list |
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name: deeprvat | ||
channels: | ||
- pytorch | ||
- nvidia | ||
- conda-forge | ||
- bioconda | ||
dependencies: | ||
- click=8.1 | ||
- cudatoolkit=11.8 | ||
- dask=2023.5 | ||
- fastparquet=0.5 | ||
- git | ||
- h5py=3.1 | ||
- mkl==2022.1.0 | ||
- numcodecs=0.11 | ||
- numpy=1.21 | ||
- optuna=2.10 | ||
- pandas=1.5 | ||
- pyarrow=11.0 | ||
- pyranges=0.0.129 | ||
- python=3.8 | ||
- pytorch=1.13 | ||
- pytorch-cuda=11 | ||
- pytorch-lightning=1.5 | ||
- pyyaml=5.4 | ||
- regenie=3.4.1 | ||
- scikit-learn=1.1 | ||
- scipy=1.10 | ||
- setuptools=59.5 | ||
- snakemake=7.17 | ||
- sqlalchemy=1.4 | ||
- statsmodels=0.13 | ||
- tqdm=4.59 | ||
- zarr=2.13 | ||
- Cython=0.29 | ||
- parallel=20230922 | ||
- pip=23.0.1 | ||
- plotnine=0.10.1 | ||
- pip: | ||
- git+https://github.com/HealthML/seak@v0.4.3 | ||
- bgen==1.6.3 |
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name: deeprvat_preprocess | ||
channels: | ||
- conda-forge | ||
- bioconda | ||
dependencies: | ||
- python=3.8 | ||
- click=8.1.3 | ||
- h5py=3.8.0 | ||
- numpy=1.21.2 | ||
- pandas=1.5.1 | ||
- tqdm=4.59.0 | ||
- pytest=8.2.0 | ||
- pyarrow=11.0.0 | ||
- snakemake=7.17.1 | ||
- bcftools=1.17 | ||
- htslib=1.19.1 | ||
- samtools=1.17 | ||
- mamba=1.4.2 | ||
- git |