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Code Block
module load bwa/0.7.7

There are multiple versions of BWA on TACC, so you might want to check which one you have loaded for when you write up your awesome publication that was made possible by your analysis of next-gen sequencing data.

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Code Block
bwa index -a bwtsw reference/genometranscripts.fa


Part 2. Align the samples to reference using bwa mem

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Warning
titleSubmit to the TACC queue or run in an idev shell

Create a commands file and use launcher_creator.py followed by sbatch.

Code Block
titlePut this in your commands file
nano commands.mem
 
bwa mem reference/genometranscripts.fafasta data/GSM794483_C1_R1_1.fq data/GSM794483_C1_R1_2.fq > C1_R1.mem.sam
bwa mem reference/genometranscripts.fafasta data/GSM794484_C1_R2_1.fq data/GSM794484_C1_R2_2.fq > C1_R2.mem.sam 
bwa mem reference/genometranscripts.fafasta data/GSM794485_C1_R3_1.fq data/GSM794485_C1_R3_2.fq > C1_R3.mem.sam 
bwa mem reference/genometranscripts.fafasta data/GSM794486_C2_R1_1.fq data/GSM794486_C2_R1_2.fq > C2_R1.mem.sam 
bwa mem reference/genometranscripts.fafasta data/GSM794487_C2_R2_1.fq data/GSM794487_C2_R2_2.fq > C2_R2.mem.sam 
bwa mem reference/genometranscripts.fafasta data/GSM794488_C2_R3_1.fq data/GSM794488_C2_R3_2.fq > C2_R3.mem.sam
Expand
titleUse this Launcher_creator command

launcher_creator.py -n mem -t 04:00:00 -j commands.mem -q normal -a UT-2015-05-18 -m "module load bwa/0.7.7" -l bwa_mem_launcher.slurm

Since this will take a while to run, you can look at already generated results at: bwa_mem_results

 Help! I have a lots of reads and a large number of reads. Make BWA go faster!

  • Use threading option in the bwa command ( bwa -t <number of threads>)

  • Split one data file into smaller chunks and run multiple instances of bwa. Finally concatenate the output.
    • WAIT! We have a pipeline for that!
    • Look for runBWA.sh in $BI/bin  (it should be in your path)

Now that we are done mapping, lets look at how to assess mapping results.