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Code Block
titleGet set up for the exercises
cds
cd my_rnaseq_course
cp -r /corral-repl/utexas/BioITeam/rnaseq_course_2015/bwa_exercise . &
cd bwa_exercise

...

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.

...

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

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

Expand

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

bwa aln -f GSM794485_C1_R3_2.sai reference/genome.fa data/GSM794485_C1_R3_2.fq

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 CCBB -m "module load bwa/0.7.7" -l bwa_launcher.sgeslurm

 *.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?

...

 

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

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

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

Put this in your commands file:

Code Block
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

Since these will take a while to run, you can look at already generated results at: /corral-repl/utexas/BioITeam/rnaseq_course_2015/bwa_exercise/results/bwa 

 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.