1. Quality Trimming and Filtering Your Sequences¶
Boot up an m1.xlarge machine from Amazon Web Services running Ubuntu 12.04 LTS (ami-59a4a230); this has about 15 GB of RAM, and 2 CPUs, and will be enough to complete the assembly of the example data set.
On the new machine, run the following commands to update the base software and install new necessary software
sudo apt-get update && sudo apt-get -y install screen git curl gcc make \
g++ python-dev unzip default-jre pkg-config libncurses5-dev \
r-base-core r-cran-gplots python-matplotlib sysstat \
python-virtualenv fastqc trimmomatic
Note
Some of these commands may take a very long time. Please see Using ‘screen’.
You may also want to make sure that the default working directory on Amazon is writeable:
sudo chmod a+rwxt /mnt
Install software¶
Install khmer:
cd ~/
python2.7 -m virtualenv work
source work/bin/activate
pip install -U setuptools
git clone --branch v2.0-rc4 https://github.com/dib-lab/khmer.git
cd khmer
make install
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 run:
source ~/work/bin/activate
Create Your Work Directory and Link Your Data¶
Put your data in /mnt/data/.
Then, link your data into a working directory.
Link your data into a working directory¶
Rather than copying the files into the working directory, let’s just link them in – this creates a reference so that UNIX knows where to find them but doesn’t need to actually move them around. :
cd /mnt
mkdir -p work
cd work
ln -fs /mnt/data/*.fastq.gz .
(The ln command does the linking.)
Now, do an ls to list the files. If you see only one entry,
*.fastq.gz, then the ln command above didn’t work properly. One
possibility is that your files aren’t in /mnt/data; another is that
their names don’t end with .fastq.gz.
Note
This protocol takes many hours (days!) to run, so you might not want to run it on all the data the first time. If you’re using the example data, you can work with a subset of it by running this command instead of the ln -fs command above:
cd /mnt/data
mkdir -p extract
for file in *.fastq.gz
do
gunzip -c ${file} | head -400000 | gzip \
> extract/${file%%.fastq.gz}.extract.fastq.gz
done
This will pull out the first 100,000 reads of each file (4 lines per record)
and put them in the new /mnt/data/extract directory. Then, do:
rm -fr /mnt/work
mkdir /mnt/work
cd /mnt/work
ln -fs /mnt/data/extract/*.fastq.gz /mnt/work
to work with the subset data.
Run FastQC on all your files¶
We can use FastQC to look at the quality of your sequences:
fastqc *.fastq.gz
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 /mnt/work
wget https://sources.debian.net/data/main/t/trimmomatic/0.33+dfsg-1/adapters/TruSeq3-PE.fa
Note
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.
Trim Your Data¶
(From this point on, you may want to be running things inside of screen, so that you can leave it running while you go do something else; see Using ‘screen’ for more information.)
Run
rm -f orphans.fq.gz
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 \
${baseR2}.qc.fq.gz s2_se \
ILLUMINACLIP:TruSeq3-PE.fa:2:40:15 \
LEADING:2 TRAILING:2 \
SLIDINGWINDOW:4:2 \
MINLEN:25
# save the orphans
gzip -9c s1_se s2_se >> orphans.fq.gz
rm -f s1_se s2_se
done
Each file with an R1 in its name should have a matching file with an R2 – these are the paired ends.
The paired sequences output by this set of commands will be in the
files ending in qc.fq.gz, with any orphaned sequences all together
in orphans.fq.gz.
Interleave the sequences¶
Next, we need to take these R1 and R2 sequences and convert them into interleaved form, for the next step. To do this, we’ll use scripts from the khmer package, which we installed above.
Now let’s use a for loop again - you might notice this is only a minor modification of the previous for loop...
for filename in *_R1_*.qc.fq.gz
do
# first, make the base by removing .extract.fastq.gz
base=$(basename $filename .qc.fq.gz)
echo $base
# now, construct the R2 filename by replacing R1 with R2
baseR2=${base/_R1_/_R2_}
echo $baseR2
# construct the output filename
output=${base/_R1_/}.pe.qc.fq.gz
(interleave-reads.py ${base}.qc.fq.gz ${baseR2}.qc.fq.gz | \
gzip > $output) && rm ${base}.qc.fq.gz ${baseR2}.qc.fq.gz
done
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.fq.gz that
contains orphaned sequences.
Finishing up¶
Make the end product files read-only:
chmod u-w *.pe.qc.fq.gz orphans.fq.gz
to make sure you don’t accidentally delete them.
If you linked your original data files into /mnt/work, you can now do
rm *.fastq.gz
to remove them from this location; you don’t need them any more.
Things to think about¶
Note that the filenames, while ugly, are conveniently structured with the history of what you’ve done to them. This is a good strategy to keep in mind.
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:
fastqc *.pe.qc.fq.gz
LICENSE: This documentation and all textual/graphic site content is licensed under the Creative Commons - 0 License (CC0) -- fork @ github.
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