sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d6304fa24f0189d624d2a6bb3a991529fc2c6e6cf7d25ce088bd9dbb890fc888 | Shell | 178 | 9 | #!/bin/sh
#SBATCH -J boruta
#SBATCH -o boruta.out
#SBATCH -e boruta.err
#SBATCH --time=24:00:00
#SBATCH --mem-per-cpu=1G
#SBATCH --cpus-per-task=30
python run_boruta.py
|
3066e5d0c4da630e3fe32e0489c345e5d3dd317676b1524a83946d03ac55eff2 | Shell | 179 | 3 | #!/usr/bin/env bash
wget -O nips2017_pretrained.zip https://www.dropbox.com/sh/cp8zpi08umfiyha/AAAS6HJSsDQPjpKlxnBHtHvga?dl=1 --no-check-certificate
unzip nips2017_pretrained.zip
|
574de73d1d64522bcf45fa6890271372def34987e87697df7e1001a1ddcf6f67 | Shell | 181 | 7 | #!/bin/bash
# Builds the Krews workflow and runs it on google with the given configuration
set -e
./gradlew clean shadowJar
for JAR in build/*.jar; do :; done;
java -jar ${JAR} $@
|
6b3b230577601b79ea61ad296614035c9a154756de87a1e97f20d3ff5da5103a | Shell | 182 | 7 | #!/bin/bash -l
# algorithm=('MLDG' 'ERM' 'DANN' 'RSC' 'Mixup' 'MMD' 'CORAL' 'VREx')
FOLDS=(0 1 2 3 4)
for FOLD in ${FOLDS[@]}; do
qsub -N $1_${FOLD} scripts/run.sh $2 $FOLD
done |
9d673a4c916674b52a6b27d23c1fb838f3588d32a5752f0f87b7ae88867444cc | Shell | 182 | 10 | #!/bin/bash
set -e
# cd to project root directory
cd "$(dirname "$(dirname "$0")")"
cd src
# test
python3 -m unittest test.test_utilities
python3 -m unittest test.test_annotations
|
6820a025b95c4a26d707fee4c8d105be27f21bd9bd59d4f1a12fce2899452916 | Shell | 187 | 6 | #!/bin/bash
source parse_yaml.sh
eval $(parse_yaml ../config.yaml CONF_)
podman build --shm-size=4096m -t $CONF_project_docker - < $CONF_project_root_host/$CONF_project_name.Dockerfile
|
c71624913943caf8f92ebc1dc719c270e9561e86ca1b140ad9b3c0e0733fcbf9 | Shell | 188 | 9 | # gpt base
python -m train \
experiment=hg38/enhancer_mel \
wandb.mode=online \
wandb.group=Enhancer_Mel \
wandb.name=gpt \
hydra.run.dir=./outputs/enhancers_mel/gpt
|
4104b582f8bd4889addd6ad741507aeee3a3988baa66d94048eeb0c99c96c4af | Shell | 190 | 2 | FOLDER=datasets/consortium/00_download/
xargs -a $FOLDER/SRR_Acc_List.txt -I {} -n 1 -P 8 fastq-dump --split-3 --disable-multithreading --skip-technical -O $FOLDER/fastq $FOLDER/prefetch/{}
|
43640839d1a0e9b72a7aa9ba24bd9833cf0e77bc6af1f461fcbc764afb72dbfd | Shell | 190 | 5 | #!/bin/sh
awk '{ print 0,$4,$5,0,$1,$2,$3 }' $1 \
| awk -v wx=$2 -v wy=$3 -f $BBMAKE_ROOT/structural/hexassign.awk - normal.txt hexgrid.txt \
| awk '{ print $5,$6,$7,$2,$3,$8,$9 }'
|
49c5bb20fa0072ee38b4915d6b8c8f9d2734382a662f19e5953145abb6654327 | Shell | 191 | 9 | # gpt base
python -m train \
experiment=hg38/enhancer_flybrain \
wandb.mode=online \
wandb.group=Enhancer_Fb \
wandb.name=gpt \
hydra.run.dir=./outputs/enhancers_fb/gpt
|
6e2ce0353e5c8e050c51163cc2a16220026939c0d2e588b8ed6335bf0052cc64 | Shell | 192 | 2 | set -ex
python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --netG unet_256 --direction BtoA --lambda_L1 100 --dataset_mode aligned --norm batch --pool_size 0
|
78970988bbb6712dc4c8442766906d2ea1d279581c424f1ba77177a2c80020a9 | Shell | 192 | 7 | conda create -n covampnet python=3.10
conda activate covampnet
conda config --add channels conda-forge
conda install pyemma
pip install tensorflow==2.11.0
conda install h5py
conda install yaml |
9a909f608c6392abeb1ef71891f4a43624a84d8a0015d017437c4614b0f3510d | Shell | 192 | 4 | conda env create --force -f no_provenance.yaml
conda env create --force -f provenance.yaml
conda env create --force -f provenance_mpi.yaml
conda env create --force -f provenance_snakemake.yaml |
d7d09e931f58f3ca276415ad10743baacb1e8594440dfe33061eab6be69ce2ee | Shell | 193 | 1 | docker run -it -v $(pwd)/build:/usr/local/src/coinstac/packages/coinstac-desktop-app/build/apps --rm coinstacteam/coinstac /bin/bash -c "cd packages/coinstac-desktop-app && npm run build-${1}"
|
104a8311548ce116cedafc70bf2ea696da4b0cfad4166ef94a6a2c45259e1d65 | Shell | 195 | 4 | #!/bin/bash
docker build --no-cache -t "ghcr.io/imsb-uke/maxomod_pipeline-r-python:latest" .
docker build --no-cache -t "ghcr.io/imsb-uke/maxomod_pipeline-mofa:latest" --file Dockerfile_mofa .
|
b639d3e8db1ea46aa73f9e2e167084d8d8dd82d58e9a4c13c705163aeae5057f | Shell | 195 | 9 | # gpt base
python -m train \
experiment=hg38/promoter \
wandb.mode=online \
wandb.group=Promoter \
wandb.name=train_inf_gpt \
hydra.run.dir=./outputs/promoter/train_inf_gpt
|
ed4ef183fa0b173227cf6f29a985ae7ec833c489c8644992a6160e78aa823b87 | Shell | 196 | 8 | #!/bin/bash
python process_crossdock.py <path>/crossdock2020/ \
--outdir <path>/processed_crossdock_noH_ca_only_temp_smiles_reorder_cutoff_15/ \
--ca_only \
--no_H \
--reorder \
--dist_cutoff 15 |
10cf2620d0fb25ee60d118d35bd7c8da3f75eb8aabb3d317b36ffbe75a6ebaee | Shell | 199 | 10 | #!/bin/bash
#SBATCH --time=24:00:00
#SBATCH --mem=20G
#SBATCH --partition=rondror
#SBATCH --constraint=GPU_MEM:12GB
#SBATCH --qos=high_p
python ../scripts/get_tutorial_datasets.py -d "./mor-data"
|
4ae61cffc9906e29f60a4898e0d5c55484c670465335b3b7886c70fdf6bf4b41 | Shell | 201 | 8 | #!/bin/bash
K8S_PROJECT_ID=devenv-215523
K8S_CLUSTER_NAME=api
COMPUTE_ZONE=us-east1-d
KUBE_DEPLOYMENT_NAME=psychscreen-app-deployment
KUBE_DEPLOYMENT_CONTAINER_NAME=psychscreen-app
echo $COMPUTE_ZONE
|
ddc7798d393c48d0331d93f40f702ca2d82465ed7ef7464dff59b6852fc1b183 | Shell | 202 | 8 | #!/bin/sh
mkdir -p "database/miRDB"
export ftp_proxy=${http_proxy}
wget \
"http://mirdb.org/download/miRDB_v6.0_prediction_result.txt.gz" \
-O "database/miRDB/miRDB_v6.0_prediction_result.txt.gz"
|
73a814f8954ab42f596a49723ba5a120b98f3cc4981ccb4b6813eff766e74814 | Shell | 203 | 8 | #!/bin/bash
K8S_PROJECT_ID=devenv-215523
K8S_CLUSTER_NAME=api
COMPUTE_ZONE=us-east1-d
KUBE_DEPLOYMENT_NAME=psychscreen-beta-deployment
KUBE_DEPLOYMENT_CONTAINER_NAME=psychscreen-beta
echo $COMPUTE_ZONE
|
9c9eb79147118d133a01d40e7bf4d7c8ce612c9282fb925b054ba89b5b73f95d | Shell | 203 | 3 | #!/usr/bin/env bash
wget -O nips2017_pretrained_movingmnist.zip https://www.dropbox.com/sh/n7gxfdd1pdasoio/AAC8uC4yto5Uam_7f3BEl-3La?dl=1 --no-check-certificate
unzip nips2017_pretrained_movingmnist.zip
|
a7a81621677ae4a6958c750f381ce1727d54a0f634d8a005063fba57bfe3ccc4 | Shell | 206 | 14 | ERROR=false
./fastqTest > results/fastqTest.txt
diff results/fastqTest.txt expectedResults/ExpectedResultsFastqTestResults.txt
if [ $? -ne 0 ]
then
ERROR=true
fi
if($ERROR == true)
then
exit 1
fi
|
b84e10550d8cdc10b4dac2bb78c33ca43b494de5c22e1065db52e4047194b7b4 | Shell | 206 | 6 | #!/bin/bash
for i in {1..9}
do
echo "Decoding file: $i ..."
./decode_raster_file --duration 130 --sep ';' --read-file "../exp$i/raster_loopback.csv" --save-file "../exp$i/raster_loopback_decoded.csv"
done |
2f20c25575024983d3627671a54dd7df5bde6fb1dd8e995b109e29a2c72dc261 | Shell | 209 | 13 | #!/bin/bash
log_path="/scratch/m/mchakrav/gjoanes/multirat/test/export/log/confound/"
find $log_path/*.log | while read line
do
RESULT=`grep 'Error' $line`
if [ -n "$RESULT" ]; then
echo $line
fi
done
exit
|
30136e967828d0f40816d109c8341fc62ad6143339d76bc8c0598fb8bdaf5921 | Shell | 211 | 6 | #!/bin/bash
cd /home/xuesheng3/liuyuchen/Projects/nnSegnext_segnext/
CUDA_VISIBLE_DEVICES=3 /home/xuesheng3/.condaoenvs/pytorch/bin/python /home/xuesheng3/liuyuchen/Projects/nnSegnext_segnext/run_training.py
|
805c4d231fcba0fcfd998c91f1cb8e212f7af0e92273392c240096f6a9a20632 | Shell | 211 | 8 | cd recon-all-clinical/
for subj in $(cut -f1 subjectsfile.txt); do
echo "--------------${subj}--------------------"
cd $subj
freeview -v mri/T1.mgz mri/aparc+aseg.mgz:colormap=lut:opacity=0.2
cd ..
done |
4bac6d9b0acda8d4ae74194901d3180361dc58cbfa776863aab42548d7342a69 | Shell | 212 | 4 | #!/bin/bash
mkdir -p ../../data/gffread
gffread -w ../../data/gffread/isoform_sequences.fa -g ../../data/gencode_annotations/GRCm39.primary_assembly.genome.fa ../../data/nextflow/bambu/extended_annotations.gtf
|
e1d8f13eaf447b9c9556e584d9c95e12c57d2a3365a1e26df5992844569a70ad | Shell | 214 | 5 | #!/bin/bash
vcf-concat $(echo vcf/cayo_brain_chr{1..20}_raw.vcf) | gzip -c > vcf/cayo_brain_all_raw.vcf.gz
vcf-concat $(echo vcf/cayo_brain_chr{1..20}_filtered.vcf) | gzip -c > vcf/cayo_brain_all_filtered.vcf.gz
|
3d9bdbb89c64e6ee1789b4bbd93bc4919bc099eaf9eef74dbf9de27b13f18f3c | Shell | 215 | 3 | wget https://www.dropbox.com/s/2g2jagq2jn1fd0i/blur_jpg_prob0.5.pth?dl=0 -O ./weights/blur_jpg_prob0.5.pth
wget https://www.dropbox.com/s/h7tkpcgiwuftb6g/blur_jpg_prob0.1.pth?dl=0 -O ./weights/blur_jpg_prob0.1.pth
|
8466b3e16b76c0657a78e1c7ca81f3e1a7cc7f4dcbaa98faa4d07acef0a07b21 | Shell | 215 | 5 | #!/bin/bash
vcftools --gzvcf vcf/cayo_brain_all_raw.vcf.gz \
--remove-filtered-all --thin 100000 --remove-indels --maf 0.3 \
--recode --recode-INFO-all --stdout --max-missing 0.9 > vcf/cayo_brain_all_thinned.vcf
|
b5b204ffcb29c4abdb643bffa3774c5fb1f066a6a295a05e6ae7ef50592a47ee | Shell | 216 | 8 | #!/bin/sh
cp /usr/share/nginx/config/config.template.json /var/www/config.json
for var in $(printenv); do
a=${var%%=*}
b=${var##*=}
sed -i "s|${a}|${b}|g" /var/www/config.json
done
nginx -g "daemon off;"
|
f871f4a57c85c8d2501876857f90506a6487bee557a533de85b99889202f6f56 | Shell | 216 | 16 | #!/bin/bash
./1-preprocessing-gpcrmd.sh
./2-comparison-of-feature-distributions.sh
./3-principal-component-analysis.sh
./4-clustering.sh
./5-density-hbond-featurization.sh
./6-state-specific-information.sh
-
|
09ffda2aeff32944da9c6b61240fa0d7be288cf0e33f51e55841a2d9346b985a | Shell | 217 | 7 | #!/bin/bash
python convert_token_frag.py \
--base_folder processed_crossdock_noH_ca_only_temp_smiles_reorder_cutoff_15 \
--save_folder ../seq/two_digit_smiles_reorder_cutoff_15_ligand_only_frag/ \
--process_lig_only |
1d356afe15e5c58efe819ac564bf01b094e1dbffe2216e2d696962acbad755eb | Shell | 217 | 11 | #!/bin/bash
# Load Local Variables
source ./SET_VARIABLES.sh
# Convert Bruker Data to Nifti
${SOFTWARE}/bru2/Bru2 -a -p -z -v ${BRUKER_RAW_DIR}/subject
mv ${BRUKER_RAW_DIR}/*.nii.gz ${NII_RAW_DIR}
echo $0 " Done" |
63708ecf710e96bb0f73a96bf4d12bf2517a6c8d4fbc95a28799aaf8831461c8 | Shell | 217 | 3 | URL=http://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/fcn-8s-cityscapes/fcn-8s-cityscapes.caffemodel
OUTPUT_FILE=./scripts/eval_cityscapes/caffemodel/fcn-8s-cityscapes.caffemodel
wget -N $URL -O $OUTPUT_FILE
|
8d3175d5f23918c6dd00ed8358a1f3c7a5408702ea5adaac424d825e3b1a0e60 | Shell | 219 | 7 | #!/bin/bash
CURRENT_DIR="$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
export XP_SCRIPT_DIR=${CURRENT_DIR}
export XP_TARGET_DIR=${CURRENT_DIR}/..
make --makefile=${XP_SCRIPT_DIR}/Makefile "$@"
|
9aea766eee12a617d9e85a80ee57a96571f171b14f5a28c71a3c2b0295949f6c | Shell | 222 | 11 | #!/bin/bash
#SBATCH -J extra_trees
#SBATCH -o extra_trees_out.txt
#SBATCH -e extra_trees_err.txt
#SBATCH --partition=gpu
#SBATCH --time=24:00:00
#SBATCH --mem=100000
#SBATCH --gres=gpu:volta:4
python pu_learning.py
|
ad9a7012ee745900ac3ab7b24515cc33ec9e55aa4cfa3ff8e37641f5d3a66e2d | Shell | 222 | 10 | #!/bin/sh
set -o errexit -o pipefail
BFILE="/PATH/TO/GENOTYPES"
Rscript ../_h/extract_shared_snps.R
plink2 --bfile $BFILE/LIBD_Brain_TopMed \
--extract shared_snps.tsv \
--make-bed --out LIBD_Brain_TopMed
|
fd150a9a05d2b0f3dc10f05c398d44f152a660036188c7bc85c5ab3b2c20b5dc | Shell | 224 | 11 | #!/bin/bash
wo sim \
-c ./configs/phase_2d.yml \
-o ./phase_2d_data.zarr
wo rec \
-i ./phase_2d_data.zarr \
-c ./configs/phase_2d.yml \
-o ./phase_2d_recon.zarr
wo view ./phase_2d_data.zarr ./phase_2d_recon.zarr
|
d1ff09b63ae376d939ac30e62bb3c95c0f48064cf26d81f0428c9ba545bb8af4 | Shell | 226 | 8 | #!/usr/bin/env bash
# Input python command to be submitted as a job
#SBATCH -p all
#SBATCH --time 01:00:00
#SBATCH --output slurm_outputs/mean_0.out
#SBATCH --job-name img_avg_0
python -u save_mean_volumes.py encoding_onerep |
9af64a8228229b1ed7860113715a6a0b50933dc9a344f490fdee14cf76fc371c | Shell | 229 | 10 | #!/bin/bash
set -euxo pipefail
workflows=$(gh workflow list --limit 300 | grep -v "^#" | awk '{print $1}' | grep "test-tool")
for workflow in $workflows; do
echo "Running workflow $workflow"
gh workflow run $workflow
done
|
5f298031efdb4cefab4387c57ce0e372646e7fe6023b49ea685353f18850c38b | Shell | 231 | 4 | #!/bin/bash
for i in $(cat /data/bioinformatics/projects/hassan2022/scr/SampleSheet_FC007379_mod.csv | awk -F "," '(NR>1){print $2}'); do
bsub -q big -e ${i}_count.log -u tzhao7@bwh.harvard.edu "sh cellranger_count.sh $i"
done
|
ad1cd1b0e1837ace89e032ac86743c633b34b9f0ee8a632dde0c4946bf198488 | Shell | 231 | 5 | #!/usr/bin/env bash
#CUDA_VISIBLE_DEVICES=1 python train.py --name=1210-gen --temp=1.0 --ring_num=4 --ring_width=1 --lr=1e-3
CUDA_VISIBLE_DEVICES=1 python train.py --name=1211-gen --temp=1.0 --ring_num=4 --ring_width=1 --lr=1e-3
|
667717056f3be9f597759cd5aad8f2b40964d8f1d7159abe9dcdce3c17e314e1 | Shell | 234 | 8 | #!/bin/zsh
export PYTHONPATH="$PWD:$PYTHONPATH"
export OMP_NUM_THREADS="1"
export OPENBLAS_NUM_THREADS="1"
export MKL_NUM_THREADS="1"
export VECLIB_MAXIMUM_THREADS="1"
export NUMEXPR_NUM_THREADS="1"
echo "set environmental variables"
|
3db2213e8d0e9040d73bec66e3663abdb10a93b683cfb477f6a7cc5654e0ccf2 | Shell | 237 | 9 | #!/bin/bash
mvn deploy:deploy-file \
-Durl=file://$(pwd)/local_mvn_repo/ \
-Dfile=lib/encryption-impl-1.0.0-SNAPSHOT.jar \
-DgroupId=com.arosbio \
-DartifactId=encryption-impl \
-Dpackaging=jar \
-Dversion=1.0.0
|
4ce4d8343b66df8e67e69801715630e6001e214b75317f8b6545f779d6f31475 | Shell | 237 | 10 | #!/bin/bash
# git clone -b gh-pages --single-branch git@github.com:Open-Minds-Lab/mrQA.git .
cd docs
make clean && make html
cp -r build/html/* ../../mrQA-gh-pages/
cd ../../mrQA-gh-pages/
git add .
git commit -m "Update docs"
git push
|
1208ed4dc9a2d8e81c683341351564275dc414e3224c6dcd545d2bf766ccbd3b | Shell | 238 | 4 | (nosetests -v tests/test_commandline.py &> test_commandline.out &)
(nosetests -v tests/test_style.py &> test_style.out &)
(nosetests -v tests/test_scripts.py &> test_scripts.out &)
(nosetests -v tests/test_import.py &> test_import.out &)
|
ef32f33150d4a1ea4ab30bf13d3b6c814b8fbb4b45402f743699211ec7d56c0c | Shell | 240 | 8 | #!/usr/bin/env bash
# Input python command to be submitted as a job
#SBATCH -p all
#SBATCH --time 01:00:00
#SBATCH --output slurm_outputs/p_value_0.out
#SBATCH --job-name bootstrap_0
python -u calculate_bootstrap_pvalue.py encoding_onerep |
f7f1330a2dc08f5bb1b0d223c79046fb57ed517bec2652d0efadf11327726161 | Shell | 241 | 14 | #!/bin/bash
export MODEL_FILE=$(pwd)"/src/test/resources/test-model.cpsign"
mvn verify -DskipUnitTests
exit_status=$?
unset MODEL_FILE
if [ $exit_status -ne 0 ]; then
echo "Error: IT Test for Venn-ABERS server failed."
exit 1
fi |
712cffda4fe9e1546cad3da517f3166a093f6348a9371753d94616394c55e410 | Shell | 244 | 13 | #!/bin/sh
mkdir -p ./NEED_to_PREPARE/cm
bash ./tools/pdb_to_cm/pdb_to_cm.sh
mkdir -p ./NEED_to_PREPARE/pkl
python ./tools/feature_extract/feature_extra.py
python ./tools/Predict.py
rm -rf ./NEED_to_PREPARE/cm&&rm -rf ./NEED_to_PREPARE/pkl |
9d4521a5f6d49a27fd7cfb1eaccf52b6f9684e8c8db9ae8442f0c6119ea42fcd | Shell | 244 | 7 | #!/bin/sh
GPU=3,4,5,6
CUDA_VISIBLE_DEVICES=${GPU} python ../diffusion/main_protein.py --dp=True --batch_size=2048 --n_epochs=16000 --exp_name=latent_diffusion \
--latent_dataname=<set this variable> --test_epochs=20 \
--diffusion_steps=1000 \ |
a73fd89a35f2973b3ac10a6591d97e9e6786951d732c5e2d97408adbf0a42f20 | Shell | 244 | 14 | #!/bin/bash
export MODEL_FILE=$(pwd)"/src/test/resources/test-model.cpsign"
mvn verify -DskipUnitTests
exit_status=$?
unset MODEL_FILE
if [ $exit_status -ne 0 ]; then
echo "Error: IT Test for CP Regression server failed."
exit 1
fi |
8bb23ef3b13fc95e9a30c14cffb78ef0cfa4f21ea3acccef59bde36a1bb269ad | Shell | 246 | 10 | echo "Plot all Figures and print Table.."
python plot_Figure2.py
python plot_Figure3.py
python plot_Figure4.py
python plot_Figure5.py
python plot_Figure6.py
python plot_Figure7.py
python plot_Figure8.py
python print_Table3.py
echo "Figures done." |
46e7ce85768cc0e989599855e96c0f5e069a427c52126504759ee404f784eb01 | Shell | 248 | 14 | #!/bin/bash
export MODEL_FILE=$(pwd)"/src/test/resources/test-model.cpsign"
mvn verify -DskipUnitTests
exit_status=$?
unset MODEL_FILE
if [ $exit_status -ne 0 ]; then
echo "Error: IT Test for CP Classification server failed."
exit 1
fi |
6a4b28e7ed00db1e3a40bcd4d0543063364be07a6518ec092b74d0893ebd7666 | Shell | 249 | 7 | #!/bin/bash
echo "[BioemuS] Setup environment variables and scripts permissions"
DIR="$( cd "$( dirname -- "$0" )" && pwd )"
export BIOEMUS_PATH="$DIR";
chmod +x $BIOEMUS_PATH/*.sh
chmod +x $BIOEMUS_PATH/app/*.sh
chmod +x $BIOEMUS_PATH/firmware/*.sh |
c6aae2f1af8a0785fb85707b558d81e11541a62638173dd7abea65740a359c9e | Shell | 249 | 11 | #!/bin/bash
#SBATCH -J dnn_gridsearch
#SBATCH -o out_dnn_gridsearch.txt
#SBATCH -e err_dnn_gridsearch.txt
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=10
#SBATCH --mem-per-cpu=4G
config_file=$1
python -u dnn_grid_search_cv.py $config_file
|
2419355b7b450e195e654072aab3f4413ea6b2101b6b4ecf25746825b6a5c834 | Shell | 250 | 9 | #!/bin/bash
index=genomes/Mmul_10.cdna_noPAR.idx
mkdir -p kallisto
sample=`sed -n ${SLURM_ARRAY_TASK_ID}p male_ids.txt`
kallisto quant -i $index -t $SLURM_CPUS_ON_NODE -o kallisto/${sample} fastq/${sample}.R1.fastq.gz fastq/${sample}.R2.fastq.gz
|
2fd4f1cac9827466f10acf86d11717727e2c2820c296e0bc5a0b311dd5da6b28 | Shell | 250 | 14 | #!/bin/bash
# compile PALM output into a single file
model=ppi_domain
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
526fd00387678d5963d4a4e6e133ecfdad35265cc9bc70a9cf19c41696b5b0cb | Shell | 250 | 14 | #!/bin/bash
# compile PALM output into a single file
model=act_domain
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
d8662f2ed702b7ce66ac58dc1c5cce6c19c1c9795c02775e70ee61ea272d84c9 | Shell | 250 | 9 | #!/bin/bash
index=genomes/Mmul_10.cdna_noY.idx
mkdir -p kallisto
sample=`sed -n ${SLURM_ARRAY_TASK_ID}p female_ids.txt`
kallisto quant -i $index -t $SLURM_CPUS_ON_NODE -o kallisto/${sample} fastq/${sample}.R1.fastq.gz fastq/${sample}.R2.fastq.gz
|
278a3b812490e8d60d1ed6253683f6fd02d1ef96add6acd7c50c8a548d2525b0 | Shell | 251 | 14 | #!/bin/bash
# compile PALM output into a single file
model=ppi_outcome
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
eb71521c03e01e6b1281a76318d119109862e853eccaecfcd0d2b784702c4554 | Shell | 251 | 14 | #!/bin/bash
# compile PALM output into a single file
model=act_outcome
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
dfaed585b78b0d922cbebe924ddafb950982ef625eaca86b926d2f1fb0911945 | Shell | 252 | 11 | #!/bin/sh
set -eux
IN1=$1
IN2=$2
SRR=`echo ${IN1} | grep -o ".RR[0-9]*"`
OUT1=${SRR}_1.fq.gz
OUT2=${SRR}_2.fq.gz
gzip -cd ${IN1} | perl /workdir/for_trinity.pl 1 | gzip -c > ${OUT1}
gzip -cd ${IN2} | perl /workdir/for_trinity.pl 2 | gzip -c > ${OUT2} |
05d3427157512e44fbff27a05aab1c089258e0aecf88ff393eab8a734446fef5 | Shell | 253 | 14 | #!/bin/bash
# compile PALM output into a single file
model=ppi_domain_ee
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
821d9b1e67a518e3f582f26c66fbd5c7effcaba2fc3885ad5a4a3bee674496fc | Shell | 253 | 14 | #!/bin/bash
# compile PALM output into a single file
model=act_domain_ee
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
97a67f551920dfbcd9facc2b13ca4eef058829c3172ef380e8021384df1f2acc | Shell | 254 | 14 | #!/bin/bash
# compile PALM output into a single file
model=ppi_outcome_ee
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
faa01db9da640135c9debc179fcbb2dcf5447bc0c9f18503154064a0e39161fb | Shell | 254 | 14 | #!/bin/bash
# compile PALM output into a single file
model=act_outcome_ee
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
5f08fdb8da16dc29147dc6a2173f77cf36d389ea8a422aa3740ae8ef815bb5fe | Shell | 255 | 12 | #!/bin/bash
#SBATCH -J benchmark
#SBATCH -o out_benchmark.txt
#SBATCH -e err_benchmark.txt
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=10
#SBATCH --mem-per-cpu=8G
config_file=$1
iterations=$2
python benchmark_all_classifiers.py $config_file $2
|
49b48c076994bee866f6c852d2c6617798fdd790bf088f885fab4bcc3318a20b | Shell | 256 | 14 | #!/bin/bash
# compile PALM output into a single file
model=act_outcome_none
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
3553717fba50053eee5cebc3b6dfe9e8326ab17f8af2c7d2dcc0330e84c150e8 | Shell | 257 | 7 | #!/bin/sh
set -eux
# SraRunTable.txtの５項目目のサンプル名と12項目目にあるコンディション情報を抜き出す
echo "sample\tcondition\tpath" > sample.txt
tail -n+2 $1 | awk 'BEGIN{FS="\t";OFS="\t"}{print $7,$14,$7}' >> sample.txt
|
dfff3051b585ab3fbbefad1b915b9f982f59fc5311476828dceb0bf03d429058 | Shell | 259 | 17 | #!/bin/bash
while getopts o: flag
do
case "${flag}" in
o) organism=${OPTARG};;
esac
done
cat $organism/sample_ids.txt | while read line;
do
echo $line
sed -i 's/TruSeq3-PE-2.fa/NexteraPE-PE.fa/' $organism/scripts/trimmomatic$line.sh
done
|
48565e715233a581e169afc50848a264d12a47b91993eabb54d9b445ee89f11c | Shell | 260 | 14 | #!/bin/bash
# compile PALM output into a single file
model=ppi_domain_x_outcome
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
d2085140b4a5f2ef8a4c9bf16b376a76f775733ac8957f59f17ff1a2efb244d1 | Shell | 260 | 14 | #!/bin/bash
# compile PALM output into a single file
model=act_domain_x_outcome
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
d662c3ee695cd939dab9ffc371501121fef36181397798f3251cff8d184b71e9 | Shell | 260 | 14 | #!/bin/bash
export MODEL_FILE=$(pwd)"/src/test/resources/test-model-tcp.cpsign"
mvn verify -DskipUnitTests
exit_status=$?
unset MODEL_FILE
if [ $exit_status -ne 0 ]; then
echo "Error: IT Test for - TCP - CP Classification server failed."
exit 1
fi |
4895381ed1af99aa07bb9a245a1f75bc76ccdbf713150883653c492958ff23f8 | Shell | 263 | 14 | #!/bin/bash
# compile PALM output into a single file
model=act_domain_x_outcome_ee
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
7bcb2648bdabdae1d1f9277f4382cc806feebbc7fe256edafa057fefa8840745 | Shell | 263 | 14 | #!/bin/bash
# compile PALM output into a single file
model=ppi_domain_x_outcome_ee
for c in 1 2 3 4 5 6 7 8 9 10 11 12; do
for s in tstat tstat_uncp tstat_fwep tstat_cfwep; do
echo ${model}_dat_${s}_c${c}.csv
cat ${model}_dat_${s}_c${c}.csv
done
done
|
03774abab3099c08093f91bd98f50ec092097994df8da29876a0a00dc530de5c | Shell | 264 | 11 | #!/bin/bash
wo sim \
-c ./configs/fluorescence_3d.yml \
-o ./fluorescence_data.zarr
wo rec \
-i ./fluorescence_data.zarr \
-c ./configs/fluorescence_3d.yml \
-o ./fluorescence_3d_recon.zarr
wo view ./fluorescence_data.zarr ./fluorescence_3d_recon.zarr
|
ce7a1e30c804a4b647d1b26d5fe1ab77243810b2c594c631788b45b2434511ed | Shell | 265 | 11 | #!/bin/bash
mkdir -p lcmlkin
lcmlkin -i vcf/cayo_brain_all_thinned.vcf \
-o lcmlkin/cayo_brain_all_thinned.txt \
-l phred -g all -t ${SLURM_CPUS_ON_NODE}
# Copy results to data/ folder
cp lcmlkin/cayo_brain_all_thinned.txt data/cayo_brain_lcmlkin_results.txt
|
b71d35c0e9e034094a5ab0a83cffd8cbe1f03f3236babecbdc08cb2cc8942ccd | Shell | 268 | 7 | conda create -y -n QHNet python=3.8
source activate QHNet
CUDA="cu102"
conda install -y pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch
pip install scipy
conda install pyg==2.2.0 -c pyg
pip install tqdm hydra-core pyscf rdkit transformers torch_ema e3nn
|
21d181209fa862462c2ed524135d44bddce9ebe5ddcdc96d59a7dbe74aaa558b | Shell | 271 | 11 | #!/bin/bash
wo sim \
-c ./configs/birefringence_3d.yml \
-o ./birefringence_data.zarr
wo rec \
-i ./birefringence_data.zarr \
-c ./configs/birefringence_3d.yml \
-o ./birefringence_3d_recon.zarr
wo view ./birefringence_data.zarr ./birefringence_3d_recon.zarr
|
f6e9e3bae374f239f39d908991f05df19bc0dabbff4f3c427b15ccc827ad8ea9 | Shell | 272 | 8 | #!/bin/sh
## Preprocessing data of Chen et al., Nature (2023)
Rscript constraintZ_to_bed.R
cat constraint_z_genome_1kb.qc.bed | sort -k1,1V -k2,2n -k3,3n -t$'\t' | bgzip -c > sorted.constraint_z_genome_1kb.qc.bed.gz
tabix -p bed sorted.constraint_z_genome_1kb.qc.bed.gz |
26002b703d018d5abec07f762b97c68bb6774be392058ace3fb26c5ff25c65ab | Shell | 273 | 16 | #!/bin/bash
# get read counts for FASTQ files
indir=$1
prefix=$2
total=0
for file in `ls ${indir} | grep "$prefix" | grep -E "_R1_|_R2_"`; do
count=`zcat ${indir}/${file} | wc -l`
tmp=$((total + count))
total=${tmp}
done
y=4
count=$((total / y))
echo $prefix $count
|
bb52bbcf0a1145e68f0275b53ffe654580a5fdc213827a3b68327f95db12439e | Shell | 273 | 11 | #!/bin/bash
wo sim \
-c ./configs/fluorescence_2d.yml \
-o ./fluorescence_2d_data.zarr
wo rec \
-i ./fluorescence_2d_data.zarr \
-c ./configs/fluorescence_2d.yml \
-o ./fluorescence_2d_recon.zarr
wo view ./fluorescence_2d_data.zarr ./fluorescence_2d_recon.zarr
|
cbd51dfb642647cd9f48e5f50b868a338f1d3a7e8c55bbf157d1998bdb3063d9 | Shell | 281 | 10 | #!/bin/bash
set -ex
TAG=sgospm12
docker build --network=host --tag "10.202.67.207:5000/${USER}:${TAG}" -f ./Dockerfile.preprocessing . \
--build-arg USER_ID=$(id -u) \
--build-arg GROUP_ID=$(id -g) \
--build-arg USER=${USER}
docker push "10.202.67.207:5000/${USER}:${TAG}" |
93448dedb5ac8956fb37dc9ab011a36dbaee57eff72ea2259c34848fbb54e6aa | Shell | 282 | 12 | #!/bin/bash
# get read counts in raw FASTQ files
fastq_read_count=/path/to/fastq_read_count.sh
indir=/path/to/FASTQ
for prefix in `ls $indir | grep "_R1_001.fastq.gz" | grep -v "Undetermined" | sed "s/_L00.*//" | uniq`; do
bash ${fastq_read_count} ${indir} ${prefix} &
done
|
0692de5c4b6b9d2941bdac1b5854298cc5c4da3687efdc39405301d0d4fb74e9 | Shell | 287 | 6 | # usage: source setup.sh
conda create -n GFNO python=3.8 -y
conda activate GFNO
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia -y
conda install scipy numpy h5py conda gfortran tensorboard gpustat -c anaconda -c conda-forge -y
pip install zarr xarray
|
d4715a811bd7943c19d4a3b180bf6c96a3e9f99fe89b161df08e7386f5a5a820 | Shell | 294 | 9 | #!/bin/sh
#module load openmpi-x86_64
module load openmpi-1.10-x86_64
# Run MPI program through Ethernet eth0
mpirun -np 28 /localdisk/software/hyphy/hyphy-2.2.4/HYPHYMPI USEPATH=/dev/null BASEPATH=/localdisk/software/hyphy/lib/hyphy /localdisk/home/PATH_TO_FOLDER_/SCUEALfullG.bf
|
865c14b3c27166e8a12a4c7e91b8f9e4e37486a338b69997dcfecefdf1e62836 | Shell | 296 | 10 | #!/bin/bash
if [ -z "$1" ]; then
exit
fi
parallel -C' ' -kj1 "\
m4 -P -D _num={1} -D _len={2} h5config.m4 > /tmp/config.tmp; \
gunzip -c streamlines/streamline_{1}.txt.gz > /tmp/data.tmp; \
h5import /tmp/data.tmp -c /tmp/config.tmp -o streamlines.h5" :::: streamlines/index.txt
|
8047d5896cf5229a7b04fb8fcf45a5bc01647072277b0e538817df4634c9a02f | Shell | 299 | 6 | conda create -y -n QHBench python=3.8
conda activate QHBench
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
pip install scipy
conda install pyg==2.1.0 -c pyg
pip install tqdm hydra-core>=1.2.0 pyscf rdkit transformers torch_ema e3nn lmdb apsw gdown
|
16430f2bc572f0628ee00475049e16a5518403b65fcd3dc3a10e16392db47d80 | Shell | 300 | 4 | gsutil cp -r gs://rna-seq_araja/PASS1A/Stanford/batch1/set*/rnaseq_pipeline/*/call-qc_report/shard*/*_qc_info.csv .
gsutil cp -r gs://rna-seq_araja/PASS1A/Stanford/batch1/redo/rnaseq_pipeline/*/call-qc_report/shard*/*_qc_info.csv .
#below yields the report for total number of samples
ls *.csv|wc -l
|
21f396aa1c14d20f75bde36300f58566e9ca5d45c03b2acedb4c1b30b94668d9 | Shell | 300 | 4 | docker run -v `pwd`:`pwd` -w `pwd` -it gcr.io/motrpac-portal/motrpac_rrbs:araja_06_17_2019 bismark2summary *.bam
docker run -v `pwd`:`pwd` -w `pwd` -it gcr.io/motrpac-portal-dev/bismark:0.20.0 deduplicate_bismark -p --barcode --bam Rat_Muscle_R1_bismark_bt2_pe.bam Rat_Muscle_R1_bismark_bt2_pe.bam
|
cbf3196fd64fddb4421f1a16506abca7cf2e717ce812ff6c871aacdcdda20911 | Shell | 301 | 9 | #!/usr/bin/env bash
set -xeuo pipefail
old_ligand_idx=0
for new_ligand_idx in $(seq 1 10)
do
perses-cli --yaml template.yaml --override old_ligand_index:"$old_ligand_idx" --override new_ligand_index:"$new_ligand_idx" --override trajectory_directory:lig"$old_ligand_idx"to"$new_ligand_idx"
done
|
ee958c79b2bd118ad2f1a260266c547b9ee365c3a2073e37c4fe61eabc667584 | Shell | 305 | 4 | #!/bin/bash
awk '/^>/{if (l!="") print l; print; l=0; next}{l+=length($0)}END{print l}' ../../../data/bernardo/processed/05.mitochondrial_novel_transcripts_probing/novel_mt_transcripts.fa \
> ../../../data/bernardo/processed/05.mitochondrial_novel_transcripts_probing/novel_mt_transcript_lengths.txt
|
8c409d6b293b2a0bdf2bf79bca4ec534b259f0c50441710986b2d13d82f4049f | Shell | 309 | 5 | echo "running EVERYTHING - this will take ~ 5-6 days"
julia --optimize=0 --math-mode=ieee --check-bounds=yes bayesian_network_regression_imp/simulation/run_simulation_unrealistic.jl
julia --optimize=0 --math-mode=ieee --check-bounds=yes bayesian_network_regression_imp/simulation/run_simulation_realistic.jl |
2d575c208f3085b6553cedff40aa28b0f82249b546cf85189dad3db2dc3e1017 | Shell | 310 | 9 | #!/bin/sh
GPU=0
CUDA_VISIBLE_DEVICES=${GPU} python ../protein_autoencoder/main.py --dataname='AFPDB_data_128_complete' --data_path='../data/' \
--mode='train' --lr_init=1e-3 --layers=2 --epochs=200 --batch_size=100 --suffix='protein_autoencoder' \
--transpose --attn --edgeloss_weight=0.5 --kl_weight=1e-4
|
ad3e069cf6a555b0dbebac6c06299e1b0b197922fadce096dfb5a6b84f877828 | Shell | 311 | 5 | #!bin/sh
# change the file paths given for --file_1 & --file_2 and --output_dir to match your data location and desired corrected image location
# (python pyhysco.py --help) to see all options
python pyhysco.py --file_1 ../data/156334_v.nii.gz --file_2 ../data/156334_-v.nii.gz --ped 1 --output_dir ../results/ |
b4be012402c9667c2affe99b3f7a1f5efcf72da948184321418c25aa6bd81d13 | Shell | 314 | 10 | #!/bin/bash
# set up a nf-core conda env in your terminal
# conda create --name nf-core
# conda activate nf-core
# conda install nf-core
# conda activate nf-core
# from base dir run: bash bash/wes/00_download_sarek-2.7.2.sh > 00_download_sarek-2.7.2.log 2>&1
nf-core download sarek -r 2.7.2 -s -o ./sarek-2.7.2
|
8f9da83e3c8a551811be15890f5f7fc65b188d40c329918a978b788fdac4d5a4 | Shell | 315 | 3 | #!/bin/bash
python scripts/main.py -wf data/worflow-connection-subset-20-04.tsv -tu data/tool-popularity-20-04.tsv -om data/tool_recommendation_model.hdf5 -cd '2017-12-01' -pl 25 -ep 10 -oe 5 -me 20 -ts 0.2 -bs '32,256' -ut '32,512' -es '32,512' -dt '0.0,0.5' -sd '0.0,0.5' -rd '0.0,0.5' -lr '0.00001,0.1' -cpus 4
|
e7e833bc11d4b9a0256198198ef578b5915ce66336a978492375b7a243065e5a | Shell | 319 | 11 | #!/bin/bash
# Compile the source files and generate the executable
g++ -o trimmed_filter trimmed_filter_main.cpp trimmed_filter.cpp list.cpp
# Check if the compilation was successful
if [ $? -eq 0 ]; then
echo "Compilation successful. Executable 'trimmed_filter' generated."
else
echo "Compilation failed."
fi |
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