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bwa index -a bwtsw reference/genome.fa


Part 2a. Align the samples to reference using bwa aln/samse/sampe

We will be using this set of commands (with options that you should try to figure out) in this order, on each sample:

    
    bwa aln
    bwa samse or sampe

Let's submit the bwa aln job

...

titleSubmit to the TACC queue or run in an idev shell

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

...

nano commands.bwa

 

Put this in your commands file:

bwa aln -f GSM794483_C1_R1_1.sai reference/genome.fa data/GSM794483_C1_R1_1.fq

bwa aln -f GSM794483_C1_R1_2.sai reference/genome.fa data/GSM794483_C1_R1_2.fq

bwa aln -f GSM794484_C1_R2_1.sai reference/genome.fa data/GSM794484_C1_R2_1.fq

bwa aln -f GSM794484_C1_R2_2.sai reference/genome.fa data/GSM794484_C1_R2_2.fq

bwa aln -f GSM794485_C1_R3_1.sai reference/genome.fa data/GSM794485_C1_R3_1.fq

...

2.

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bwa aln -f GSM794486_C2_R1_1.sai reference/genome.fa data/GSM794486_C2_R1_1.fq

bwa aln -f GSM794486_C2_R1_2.sai reference/genome.fa data/GSM794486_C2_R1_2.fq

bwa aln -f GSM794487_C2_R2_1.sai reference/genome.fa data/GSM794487_C2_R2_1.fq

bwa aln -f GSM794487_C2_R2_2.sai reference/genome.fa data/GSM794487_C2_R2_2.fq

bwa aln -f GSM794488_C2_R3_1.sai reference/genome.fa data/GSM794488_C2_R3_1.fq

bwa aln -f GSM794488_C2_R3_2.sai reference/genome.fa data/GSM794488_C2_R3_2.fq

 

Expand
titleUse this Launcher_creator command

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

 *.sai file is a file containing "alignment seeds" in a file format specific to BWA.  We still need to extend these seed matches into alignments of entire reads, choose the best matches, and convert the output to SAM format. Do we use sampe or samse?

Lets submit the bwa sampe job, but have it be on hold till previous job is finished.

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

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

Expand
titleI need some help figuring out the options...

nano commands.bwa.sampe

 

Put this in your commands file:

bwa sampe -f C1_R1.sam reference/genome.fa GSM794483_C1_R1_1.sai GSM794483_C1_R1_2.sai data/GSM794483_C1_R1_1.fq data/GSM794483_C1_R1_2.fq

bwa sampe -f C1_R2.sam reference/genome.fa GSM794484_C1_R2_1.sai GSM794484_C1_R2_2.sai data/GSM794484_C1_R2_1.fq data/GSM794484_C1_R2_2.fq

bwa sampe -f C1_R3.sam reference/genome.fa GSM794485_C1_R3_1.sai GSM794485_C1_R3_2.sai data/GSM794485_C1_R3_1.fq data/GSM794485_C1_R3_2.fq

bwa sampe -f C2_R1.sam reference/genome.fa GSM794486_C2_R1_1.sai GSM794486_C2_R1_2.sai data/GSM794486_C2_R1_1.fq data/GSM794486_C2_R1_2.fq

bwa sampe -f C2_R2.sam reference/genome.fa GSM794487_C2_R2_1.sai GSM794487_C2_R2_2.sai data/GSM794487_C2_R2_1.fq data/GSM794487_C2_R2_2.fq

bwa sampe -f C2_R3.sam reference/genome.fa GSM794488_C2_R3_1.sai GSM794488_C2_R3_2.sai data/GSM794488_C2_R3_1.fq data/GSM794488_C2_R3_2.fq

Expand
titleUse this Launcher_creator command

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

sbatch --dependency=afterok:<aln-job-ID> bwa_sampe_launcher.slurm

Part 2b. Align the samples to reference using bwa mem

Alternatively, lets also try running alignment using the newest and greatest, BWA MEM. Alignment is just one single step with bwa mem.

 

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
Expandcode
I need some help figuring out the options...
titlePut this in your commands file
nano commands.mem
 
bwa mem reference/genome.fa data/GSM794483_C1_R1_1.fq data/GSM794483_C1_R1_2.fq > C1_R1.mem.sam
bwa mem reference/genome.fa data/GSM794484_C1_R2_1.fq data/GSM794484_C1_R2_2.fq > C1_R2.mem.sam
bwa mem reference/genome.fa data/GSM794485_C1_R3_1.fq data/GSM794485_C1_R3_2.fq > C1_R3.mem.sam
bwa mem reference/genome.fa data/GSM794486_C2_R1_1.fq data/GSM794486_C2_R1_2.fq > C2_R1.mem.sam
bwa mem reference/genome.fa data/GSM794487_C2_R2_1.fq data/GSM794487_C2_R2_2.fq > C2_R2.mem.sam
bwa mem reference/genome.fa 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 these this will take a while to run, you can look at already generated results at:  /corral-repl/utexas/BioITeam/rnaseq_course_2015/ 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.