Objectives
In this lab, you will explore a popular fast mapper called Bowtie2BWA. 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:
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- 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 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
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title | Get set up for the exercises |
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cds
cd my_rnaseq_course
cp -r /corral-repl/utexas/BioITeam/rnaseq_course_2015/bwa_exercise . &
cd bwa_exercise |
Run BWA
Load the module:
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module load bwa/0.7.7
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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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title | Here are some commands that could help... |
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module spider bwa
module list
bwa
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Create a fresh output directory. Be sure you are back in your main intro_to_mapping
directory. Then:
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!
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bwa index -a bwtsw reference/genome.fa
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Part 2a. Align the samples to reference using bwa aln/samse/sampe
You will need to run We will be using this set of commands (with options that you should try to figure out) in this order, on each sample:
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bwa aln
bwa samse or sampe
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WhatLet's going on at each step?Remember to use the option that enables multithreading, if there is one, for each BWA command.submit the bwa aln job
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title | Submit to the TACC queue or run in an idev shell |
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Create a commands file and use launcher_creator.py followed by qsub. Expand |
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title | I need some help figuring out the options...nano commands.bwa Put this in your commands file: codebwa aln -f GSM794483_C1_R1_1.sai reference/genome.fa data/GSM794483_C1_R1_1.fq bwa aln -t 6 -f bwa/SRR030257_1.sai bwa/NC_012967.1.fasta SRR030257_1.fastq
bwa aln -t 6 -f bwa/SRR030257_2.sai bwa/NC_012967.1.fasta SRR030257_2.fastq
Why did we use -t 6 instead of -t 12 for multithreading? Both of our commands are going to go to a single node on Lonestar, so they should share the 12 available cores. |
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-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 |
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title | Use this Launcher_creator command |
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| 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.sge |
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*.sai file is a file containing "alignment seeds" in a file format specific to BWA. Many programs produce this kind of "intermediate" file in their own format and then at the end have tools for converting things to a "community" format shared by many downstream programs. 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
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Lets submit the bwa sampe job, but have it be on hold till previous job is finished.
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title | Submit to the TACC queue or run in an idev shell |
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Create a commands file and use launcher_creator.py followed by qsub. Expand |
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title | I need some help figuring out the options... |
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| nano commands.bwa.sampe Put this in your commands file: Code Block |
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bwa sampe -f bwa/SRR030257.sam bwa/NC_012967.1.fasta bwa/SRR030257_1.sai bwa/SRR030257_2.sai SRR030257_1.fastq SRR030257_2.fastq
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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 |
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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 sep step with bwa mem.
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title | Submit to the TACC queue or run in an idev shell |
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Create a commands file and use launcher_creator.py followed by qsub. Expand |
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title | I need some help figuring out the options... |
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| Put this in your commands file: Code Block |
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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_exercise/results/bwa
Help! I have a lots of reads and a large number of reads. Make BWA go faster!
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- 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.