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:
- Learn how BWA works and how to use it.
Introduction
BWA (the Burrows-Wheeler Aligner) is a fast short read aligner. It's the successor to another aligner you might have used or heard of called MAQ (Mapping and Assembly with Quality). As the name suggests, it uses the burrows-wheeler transform to perform alignment in a time and memory efficient manner.
BWA Variants
BWA has three different algorithms:
- For reads upto 100 bp long:
- BWA-backtrack : BWA aln/samse/sampe
- For reads upto 1 Mbp long:
- BWA-SW
- BWA-MEM : Newer! Typically faster and more accurate.
Get your data
Six raw data files have been provided for all our further RNA-seq analysis:
- c1_r1, c1_r2, c1_r3 from the first biological condition
- c2_r1, c2_r2, and c2_r3 from the second biological condition
cds
cd my_rnaseq_course
cp -r /corral-repl/utexas/BioITeam/rnaseq_course_2015/bwa_exercise . &
cd bwa_exercise |
Run BWA
Load the module:
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.
module spider bwa
module list
bwa
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You can see the different commands available under the bwa package from the command line help:
Part 1. Create a index of your reference
NO NEED TO RUN THIS NOW- YOUR INDEX HAS ALREADY BEEN BUILT!
bwa index -a bwtsw reference/genome.fa
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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
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 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 |
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 |
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*.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.
Create a commands file and use launcher_creator.py followed by sbatch. 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 |
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 |
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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.
Create a commands file and use launcher_creator.py followed by sbatch. Put 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 |
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Since these 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!
Now that we are done mapping, lets look at how to assess mapping results.