Overview:

Throughout the course we've gone over how errors can pop up in your data and how they can effect confidence in variant calls and knowledge of what variants are actually real. Here we provide you with real data to show the difference between a non-corrected library and a error corrected library.

Learning Objectives:

1. Run breseq on fastq files corresponding to error corrected, and non-error corrected data
2. Gain an understanding of just how powerful error correction can be

Get some data:

The reference files 1400flanking.gff3, and REL606.maksed.gff3  should be copied from the $BI/gva_course/mixed_population directory into a new folder GVA_breseq_Error_Correction while DED110_SSCS.fastq, and DED110_all.trimmed.fastq should be copied from your GVA_Error_Correction folder. 

mkdir $SCRATCH/GVA_breseq_Error_Correction
cp $BI/gva_course/mixed_population/*.gff3 $SCRATCH/GVA_breseq_Error_Correction
cd $SCRATCH/GVA_breseq_Error_Correction

cp $SCRATCH/GVA_Error_Correction/DED110_SSCS.fastq .
cp $SCRATCH/GVA_Error_Correction/DED110_all.trimmed.fastq .

Generate a commands file

Like our other breseq tutorials these commands should be run on the job queue system.

In this case we are going to be running breseq in both polymorphism mode (-p), and targeted sequencing mode (-t). By default breseq creates a lot of html files as output which ordinarily are very useful to visualize what the characteristics of any mutation are (in a better way than can be done with IGV). For normal runs this is extremely useful. For targeted deep sequencing runs (especially ones where we have not done error corrections), this can be incredibly time consuming. Since we are ultimately only interested in comparing the difference in number of mutations detected and their frequencies rather than the specific characteristics of those mutations, we also include the --brief-html-output option. We will also be using 2 different reference files, 1400flanking.gff3 should be used as a standard reference while REL606.masked.gff3 should be used as a junction only reference. 

mkdir Logs SSCS_output
breseq -j 34 -p -t -o SSCS_output/trimmed -r 1400flanking.gff3 -s REL606.masked.gff3 --brief-html-output DED110_all.trimmed.fastq >& Logs/trimmed.log.txt
breseq -j 34 -p -t -o SSCS_output/SSCS -r 1400flanking.gff3 -s REL606.masked.gff3 --brief-html-output DED110_SSCS.fastq >& Logs/SSCS.log.txt

Generate a slurm file

cp /corral-repl/utexas/BioITeam/gva_course/GVA2022.launcher.slurm breseq.slurm
nano breseq.slurm

The changes to lines 22 and 23 are optional but will give you an idea of what types of email you could expect from TACC if you choose to use these options. Just be sure to pay attention to these 2 lines starting with a single # symbol after editing them.

Again use ctl-o and ctl-x to save the file and exit.

Submit a job

conda activate GVA-breseq
sbatch breseq.slurm

Generate comparison table for the 2 types of samples

One the job is complete, Just like the previous tutorial we now want to create a comparison file using "gdtools compare" so we can see the difference of having the trimmed reads vs the SSCS reads. This time you want to create a file "trimmed_vs_SSCS.html".

gdtools compare -h

gdtools compare -o trimmed_vs_SSCS.html -r reference/REL606.masked.gff3 -r reference/1400flanking.gff3 SSCS_output/SSCS/output/output.gd SSCS_output/trimmed/output/output.gd

The comparison table as well as the output for the trimmed and SSCS reads can now be exported to your local computer for viewing.

Evaluating the effect of error correction:

Once you have transferred the trimmed_vs_SSCS.html file back to your computer, open it and scroll around to see what the effects of our SSCS error correction were.

Return to GVA2022