...

1. work with some simple bash scripting from the command line (for loops) to generate multiple fastqc reports simultaneously and look at 200+ 272 plasmid samples.
2. work with MultiQC to make decisions about read preprocessing.
3. identify outlier files that are clearly different from the group as a whole and determine how to deal with these files.

Get some data and load fastqc

Copy the plasmid sequencing files found in the BioITeam directory gva_course/plasmid_qc/ to a new directory named GVA_multiqc. There are 2 main ways to do this particularlly since there are so many files (544 total). 

cp -r $BI/gva_course/plasmid_qc/ $SCRATCH/GVA_multiqc

mkdir $SCRATCH/GVA_multiqc 
cp $BI/gva_course/plasmid+qc/* $SCRATCH/GVA_multiqc

module load fastqc

Use a bash for loop on the command line to generate a fastQC command for all plasmid samples

We are going to construct a single commands file with 544 lines that will launch all commands without having to know the name of any single file.  To do so we will use the bash 'for' command.

For loops on the command line have 3 parts:

1. A list of something to deal with 1 at a time. Followed by a ';'
   1. for f in *.gz; in the following example
2. Something to do with each item in the list. this must start with the word 'do'
   1. do echo "fastqc -o fastqc_output $f &"; in the following example
3. The word "done" so bash knows to stop looking for more commands.
   1. done in the following example, but we add a final redirect (>) so rather than printing to the screen the output goes to a file (fastqc_commands in this case)

for f in *.gz; do echo "fastqc -o fastqc_output $f &" ;done  > fastqc_commands

Use the linux commands head and wc -l to see what the output is.

Next we need to make the output directory for all the fastqc reports to go and make the fastqc_commands file executable.

mkdir fastqc_output
chmod +

Run MultiQC tool on all fastQC output

...