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Dhivya Arasappan

Dhivya Arasappan

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Objectives

In this lab, you will explore a popular fast mapper called BWA. Simulated RNA-seq data will be provided to you; the data contains 75 bp paired-end reads that have been generated in silico to replicate real gene count data from Drosophila. The data simulates two biological groups with three biological replicates per group (6 samples total).  The objectives of this lab is mainly to:

 


  1. Learn how BWA works and how to use it.

...

Code Block
titleGet set up for the exercises
ls ../data
ls ../reference
 
#transcriptome
head ../reference/transcripts.fasta 
#see how many transcripts there are in the file
grep -c '^>' ../reference/transcripts.fasta
 
#genome
head ../reference/genome.fa
#see how many sequences there are in the file
grep -c '^>' ../reference/genome.fa
 
 
#annotation
head ../reference/genes.formatted.gtf
#see how many entries there are in this file
wc -l ../reference/genes.formatted.gtf

Load the module:Run BWA

Code Block
module load bwa

...

You can see the different commands available under the bwa package from the command line help:

Code Block
bwa

...


Part 1. Create a index of your reference
...
 Running alignment using the newest and greatest, BWA MEM to the transcriptome. 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 Block
titlePut this in your commands file
nano commands.mem
 
bwa mem ../reference/transcripts.fasta ../data/GSM794483_C1_R1_1.fq ../data/GSM794483_C1_R1_2.fq > C1_R1.mem.sam
bwa mem ../reference/transcripts.fasta ../data/GSM794484_C1_R2_1.fq ../data/GSM794484_C1_R2_2.fq > C1_R2.mem.sam 
bwa mem ../reference/transcripts.fasta ../data/GSM794485_C1_R3_1.fq ../data/GSM794485_C1_R3_2.fq > C1_R3.mem.sam 
bwa mem ../reference/transcripts.fasta ../data/GSM794486_C2_R1_1.fq ../data/GSM794486_C2_R1_2.fq > C2_R1.mem.sam 
bwa mem ../reference/transcripts.fasta ../data/GSM794487_C2_R2_1.fq ../data/GSM794487_C2_R2_2.fq > C2_R2.mem.sam 
bwa mem ../reference/transcripts.fasta ../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" -l bwa_mem_launcher.slurm
 Expand
titleUse sbatch to submit your job to the queue
sbatch --reservation=CCBB_5.23.17PM bwa_mem_launcher.slurm
Since this will take a while to run, you can look at already generated results at: bwa_mem_results_transcriptome
Alternatively, we can also use bwa to make to the genome (reference/genome.fa). Those already generated results are at: bwa_mem_results_genome
 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.