#commonly used

juR96QR6

#Linux

  1. truncate -s 0 file # empty a file with file name kept to save disk memory

#HPC-SCC

  1. frequently used commands
sbatch submit.sbatch # submit a sbatch job
squeue -u $USER # check the submitted jobs 
scancel <JOBID> #  kill a job
ps -u $USER # show your processes
pkill -u $USER # kill all your processes on this node (be careful!)
  1. download data

0_get_data.sh

kingfisher get --run-identifiers-list srr_list.txt -m aws-http prefetch aws-cp gcp-cp ena-ftp -t 16 -f fastq.gz --check-md5sums

srr_list

SRR8259677
SRR8259678
SRR31052172
SRR31690640

sbatch 0_submit.sbatch

0_submit.sbatch

#!/bin/bash
#SBATCH --job-name=fetch_data
#SBATCH --partition=medium
#SBATCH --cpus-per-task=16
#SBATCH --mem=32G
#SBATCH --time=48:00:00
#SBATCH --chdir=./
#SBATCH --output=%x-%j.out            
#SBATCH --error=%x-%j.err             

set -euo pipefail

# --- load conda & env ---
set +u                                  
source ~/.bashrc
conda activate tools_kingfisher
set -u                                  

# --- log / runtime setup ---
echo "JobID=$SLURM_JOB_ID Node(s)=$SLURM_JOB_NODELIST"
echo "Partition=$SLURM_JOB_PARTITION  CPUs/Task=${SLURM_CPUS_PER_TASK:-NA}  Mem=${SLURM_MEM_PER_NODE:-NA}"
echo "NTASKS=${SLURM_NTASKS:-1}  THREADS(from cpus-per-task)=${SLURM_CPUS_PER_TASK:-?}"   
date

# match threads to allocation (exported for your scripts to pick up)
export THREADS="${SLURM_CPUS_PER_TASK:-8}"
export PYTHONUNBUFFERED=1
export OMP_NUM_THREADS="$THREADS"
export OPENBLAS_NUM_THREADS="$THREADS"
export MKL_NUM_THREADS="$THREADS"
export NUMEXPR_NUM_THREADS="$THREADS"

# --- run your pipeline ---
bash 0_get_data.sh

echo "Done."
date