Start up a Jetstream m1.medium as per Jetstream startup instructions.

You should now be logged into your Jetstream computer! You should see something like this

titus@js-17-71:~$ Installing some software¶ Run: sudo apt-get -y update && \ sudo apt-get -y install trimmomatic fastqc python-pip \ samtools zlib1g-dev ncurses-dev python-dev pip install multiqc  apt-get install doesn’t work properly for fastqc. So we will update the default fastqc version using the following commands cd ~/ wget https://launchpad.net/ubuntu/+archive/primary/+files/fastqc_0.11.5+dfsg-3_all.deb && \ sudo dpkg -i fastqc_0.11.5+dfsg-3_all.deb && \ sudo apt-get install -f  Data source¶ We will be using mRNAseq reads from a small subset of data from Nematostella vectensis (Tulin et al., 2013). Original RNAseq workflow protocol here, more updated protocol here. Load your data into /mnt/work/data. You may need to make the /mnt/ directory writeable by doing sudo chmod a+rwxt /mnt  First, and then creating the subdirectories cd /mnt mkdir -p work work/data cd /mnt/work/data  Download subset of data: cd /mnt/work curl -O https://s3.amazonaws.com/public.ged.msu.edu/mrnaseq-subset.tar cd data tar xvf ../mrnaseq-subset.tar  Define your$PROJECT variable to be the location of your work directory; in this case, it will be /mnt/work:

export PROJECT=/mnt/work


Check:

ls $PROJECT/data  If you see all the files you think you should, good! Otherwise, debug. These are FASTQ files – let’s take a look at them: less 0Hour_ATCACG_L002_R1_001.extract.fastq.gz  (use the spacebar to scroll down, and type ‘q’ to exit ‘less’) Question: • where does the filename come from? • why are there 1 and 2 in the file names? Links: Quality trimming and light quality filtering¶ Make sure you’ve got the PROJECT location defined, and your data is there: set -u printf "\nMy raw data is in$PROJECT/data/, and consists of $(ls -1${PROJECT}/data/*.fastq.gz | wc -l) files\n\n"
set +u


Important: If you get an error above or the count of files is wrong...STOP!! Revisit the installation instructions!

FastQC¶

We’re going to use FastQC summarize the data. We already installed ‘fastqc’ on our computer for you.

Now, run FastQC on two files:

fastqc *.fastq.gz


After this finishes running (has to run on each file so might take a while), type ‘ls’:

ls -d *fastqc.zip*


to list the files, and you should see a number of files with the extensions .fastqc.zip.

Inside each of the fatqc directories you will find reports from the fastqc. You can download these files using your RStudio Server console, if you like. To install and run an RStudio Server, go here.

Questions:

• What should you pay attention to in the FastQC report?
• Which is “better”, file 1 or file 2? And why?

There are several caveats about FastQC - the main one is that it only calculates certain statistics (like duplicated sequences) for subsets of the data (e.g. duplicate sequences are only analyzed for the first 100,000 sequences in each file.

As an alternative to viewing the files on the Rstudio server, we can secure copy (scp) these files to our own laptops, and view them from there.

mkdir ~/Desktop/nema_fastqc  # make a directory for these files


where the first argument after scp is your login and path for the files we want to copy (from the jetstream instance), and the second argument is the path to place the files on our own computer.

You’ll need to know which Illumina sequencing adapters were used for your library in order to trim them off. Below, we will use the TruSeq3-PE.fa adapters:

wget https://anonscm.debian.org/cgit/debian-med/trimmomatic.git/plain/adapters/TruSeq3-PE.fa


Note: If running this on your own data, make sure these are the right adapters for your data. If they are the right adapters, you should see that some of the reads are trimmed; if they’re not, you won’t see anything get trimmed.

Adapter trim each pair of files¶

See excellent paper on trimming from MacManes 2014.

Run:

for filename in *_R1_*.fastq.gz
do
# first, make the base by removing fastq.gz
base=$(basename$filename .fastq.gz)
echo $base # now, construct the R2 filename by replacing R1 with R2 baseR2=${base/_R1_/_R2_}
echo $baseR2 # finally, run Trimmomatic TrimmomaticPE${base}.fastq.gz ${baseR2}.fastq.gz \${base}.qc.fq.gz s1_se \
${baseR2}.qc.fq.gz s2_se \ ILLUMINACLIP:TruSeq3-PE.fa:2:40:15 \ LEADING:2 TRAILING:2 \ SLIDINGWINDOW:4:2 \ MINLEN:25 # save the orphans gzip -9c s1_se s2_se >> orphans.qc.fq.gz rm -f s1_se s2_se done  The paired sequences output by this set of commands will be in the files ending in .qc.fq.gz, with any orphaned sequences all together in orphans.qc.fq.gz. Make these trimmed reads read-only and keep them, as we will reuse them later. chmod u-w${PROJECT}/quality/*.qc.fq.gz


Questions:

• How do you figure out what the parameters mean?
• How do you figure out what parameters to use?
• What adapters do you use?
• What version of Trimmomatic are we using here? (And FastQC?)
• Do you think parameters are different for RNAseq and genomic data sets?
• What’s with these annoyingly long and complicated filenames?
• why are we running R1 and R2 together?

For a discussion of optimal trimming strategies, see MacManes, 2014 – it’s about RNAseq but similar arguments should apply to metagenome assembly.

MultiQc¶

MultiQC aggregates results across many samples into a single report for easy comparison.

Run Mulitqc on both the untrimmed and trimmed files

multiqc .


And now you should see output that looks like this:

[INFO   ]         multiqc : This is MultiQC v1.0
[INFO   ]         multiqc : Template    : default
[INFO   ]         multiqc : Searching '.'
Searching 15 files..  [####################################]  100%
[INFO   ]          fastqc : Found 4 reports
[INFO   ]         multiqc : Compressing plot data
[INFO   ]         multiqc : Report      : multiqc_report.html
[INFO   ]         multiqc : Data        : multiqc_data
[INFO   ]         multiqc : MultiQC complete


You can view output html file multiqc_report.html

Questions:

• is the quality trimmed data “better” than before?
• Does it matter that you still have adapters!?