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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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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 |
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What is a *.sai file ? It's 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?
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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| 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 |
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Part 2b. Align the samples to reference using 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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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/bwa_exercise/results/bwa
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.
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