Minnesota Supercomputing Institute
0.7.17_gcc-7.2.0_haswell
0.7.12_gcc-4.9.2_haswell, 0.7.17_gcc-7.2.0_haswell, 0.7.17.CentOS7, 0.5.9, 0.6.2, 0.7.10, 0.7.12, 0.7.15, 0.7.17, 0.7.4
Tuesday, August 29, 2023
0.7.17.CentOS7, 0.5.9, 0.6.2, 0.7.10, 0.7.12, 0.7.15, 0.7.17, 0.7.4
BWA is a software package for mapping low-divergent sequences against a large reference genome, such as the human genome. It consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM. The first algorithm is designed for Illumina sequence reads up to 100bp, while the rest two for longer sequences ranged from 70bp to 1Mbp. BWA-MEM and BWA-SW share similar features such as long-read support and split alignment, but BWA-MEM, which is the latest, is generally recommended for high-quality queries as it is faster and more accurate. BWA-MEM also has better performance than BWA-backtrack for 70-100bp Illumina reads.
module load bwa bwa
For each of the BWA algorithms, you must first index the genome with the following command:
bwa index [options] input.fasta
#PBS -l nodes=2:ppn=8,pmem=1000mb,walltime=8:00:00 #PBS -m abe #PBS -M sample_email@umn.edu module load bwa bwa index input.fasta bwa aln -t $PBS_NP input.fasta input.fq > output.sai
bwa aln bwa samse bwa sampe
bwa bwasw
bwa mem
A complete list of options for each of the algorithms is available at the BWA manual page.
#!/bin/bash #SBATCH --job-name="rfm_RunBWATest_job" #SBATCH --ntasks=1 #SBATCH --ntasks-per-node=1 #SBATCH --output=rfm_RunBWATest_job.out #SBATCH --error=rfm_RunBWATest_job.err #SBATCH --time=0:10:0 #SBATCH -p small,large,ram256g,ram1t module load bwa/0.7.17 wget https://public.s3.msi.umn.edu/reframe/sw/bwa/sample.fq wget https://public.s3.msi.umn.edu/reframe/sw/bwa/ref.fasta bwa index ref.fasta bwa aln -t 4 ref.fasta sample.fq > output.sai