1. Quality Trimming and Filtering Your Sequences

Login to boqueron, and let’s set up some software.

Install software

Install khmer. We can use Python’s pip command

cd ${HOME}
module load python2
pip install --user virtualenv
python2.7 -m virtualenv venv
source ${HOME}/venv/bin/activate
pip install khmer

The use of virtualenv allows us to install Python software without having root access. If you come back to this protocol in a different terminal session you will need to rerun source ${HOME}/venv/bin/activate again.

Find your data

Load in your own data (as in 0. Downloading and Saving Your Initial Data) (again, this is the data from Tulin et al., 2013).


ls ${WORK}/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’)

Evaluate the quality of your files with FastQC

We can use FastQC to look at the quality of your sequences. To use fastqc on boqueron, you can use a version installed by the staff as a software module:

module load fastqc
fastqc *.fastq.gz

The output will be placed in the ‘${WORK}/quality’ folder on boqueron. You can transfer the html files to your local computer; look for the fastqc_report.html files, and double click on them to load them into your browser.

Find the right Illumina adapters

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:

cd ${WORK}/quality
wget https://sources.debian.net/data/main/t/trimmomatic/0.32+dfsg-2/adapters/TruSeq3-PE.fa


You’ll need to 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

(From this point on, you may want to be running things inside of screen, so that you detach and log out while it’s running; see Using ‘screen’ for more information.)

If you’re following along using the Nematostella data, you should have a bunch of files that look like this (use ‘ls’ to show them):


Each file with an R1 in its name should have a matching file with an R2 – these are the paired ends.

See excellent paper on trimming from MacManes 2014.

We will use the copy of Trimmomatic, read trimming tool (homepage). Since Trimmomatic is a java program, we load a module for java.


# Load a newer java version
module load jdk

# Delete any old orphan reads
rm -f orphans.qc.fq.gz

for filename in *_R1_*.fastq.gz
  # 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
  echo $baseR2

 # finally, run Trimmomatic
  java -jar /work/eelpondworkshop/Trimmomatic-0.36/trimmomatic-0.36.jar PE ${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 \

  # save the orphans
  gzip -9c s1_se s2_se >> orphans.qc.fq.gz
  rm -f s1_se s2_se

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

Interleave the sequences

Next, we need to take these R1 and R2 sequences and convert them into interleaved form, for the next step. To do this, we’ll use scripts from the khmer package, which we installed above.

Now let’s use a for loop again - you might notice this is only a minor modification of the previous for loop...

for filename in *_R1_*.qc.fq.gz
     # first, make the base by removing .extract.fastq.gz
     base=$(basename $filename .qc.fq.gz)
     echo $base

     # now, construct the R2 filename by replacing R1 with R2
     echo $baseR2

     # construct the output filename

     (interleave-reads.py ${base}.qc.fq.gz ${baseR2}.qc.fq.gz | \
         gzip > $output)

The final product of this is now a set of files named *.pe.qc.fq.gz that are paired-end / interleaved and quality filtered sequences, together with the file orphans.qc.fq.gz that contains orphaned sequences.

Finishing up

Make the end product files read-only

chmod u-w *.pe.qc.fq.gz orphans.qc.fq.gz

to make sure you don’t accidentally delete them.

Since you linked your original data files into the quality directory, you can now do:

rm *.fastq.gz

to remove them from this location; you don’t need them for any future steps.

Things to think about

Note that the filenames, while ugly, are conveniently structured with the history of what you’ve done. This is a good idea.

OPTIONAL: Evaluate the quality of your files with FastQC again

We can once again use FastQC to look at the quality of your newly-trimmed sequences:

cd ${WORK}/quality
fastqc *.qc.fq.gz

Saving the files

At this point, you should save these files, which will be used in two ways: first, for assembly; and second, for mapping, to do quantitation and ultimately comparative expression analysis. You can save them by transferring the files to your local computer.

Next stop: 2. Applying Digital Normalization.

LICENSE: This documentation and all textual/graphic site content is licensed under the Creative Commons - 0 License (CC0) -- fork @ github.
comments powered by Disqus