Evaluating the quality of your short reads, and trimming them

As useful as BLAST is, we really want to get into sequencing data, right? One of the first steps you must do with your data is evaluate its quality and throw away bad sequences.

Before you can do that, though, you need to install a bunch o’ software.

Logging in

Log in and type:

sudo bash

to change into superuser mode.

Packages to install

Install khmer:

cd /usr/local/share
git clone https://github.com/ged-lab/khmer.git
cd khmer
git checkout v1.1
make install

Install Trimmomatic:

cd /root
curl -O http://www.usadellab.org/cms/uploads/supplementary/Trimmomatic/Trimmomatic-0.32.zip
unzip Trimmomatic-0.32.zip
cp Trimmomatic-0.32/trimmomatic-0.32.jar /usr/local/bin

Install FastQC:

cd /usr/local/share
curl -O http://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.11.2.zip
unzip fastqc_v0.11.2.zip
chmod +x FastQC/fastqc

Install libgtextutils and fastx:

cd /root
curl -O http://hannonlab.cshl.edu/fastx_toolkit/libgtextutils-0.6.1.tar.bz2
tar xjf libgtextutils-0.6.1.tar.bz2
cd libgtextutils-0.6.1/
./configure && make && make install

cd /root
curl -O http://hannonlab.cshl.edu/fastx_toolkit/fastx_toolkit-
tar xjf fastx_toolkit-
cd fastx_toolkit-
./configure && make && make install

In each of these cases, we’re downloading the software – you can use google to figure out what each package is and does if we don’t discuss it below. We’re then unpacking it, sometimes compiling it (which we can discuss later), and then installing it for general use.

Getting some data

Start at your EC2 prompt, then type

cd /mnt

Now, grab the 5m E. coli reads from our data storage (originally from Chitsaz et al.):

curl -O https://s3.amazonaws.com/public.ged.msu.edu/ecoli_ref-5m.fastq.gz

You can take a look at the file contents by doing:

gunzip -c ecoli_ref-5m.fastq.gz | less

(use ‘q’ to quit the viewer). This is what raw FASTQ looks like!

Note that in this case we’ve given you the data interleaved, which means that paired ends appear next to each other in the file. Most of the time sequencing facilities will give you data that is split out into s1 and s2 files. We’ll need to split it out into these files for some of the trimming steps, so let’s do that –

split-paired-reads.py ecoli_ref-5m.fastq.gz
mv ecoli_ref-5m.fastq.gz.1 ecoli_ref-5m_s1.fq
mv ecoli_ref-5m.fastq.gz.2 ecoli_ref-5m_s2.fq

This uses the khmer script ‘split-paired-reads’ (see documentation) to break the reads into left (/1) and right (/2). (This takes a long time! 5m reads is a lot of data...)

We’ll also need to get some Illumina adapter information – here:

curl -O https://s3.amazonaws.com/public.ged.msu.edu/illuminaClipping.fa

These sequences are (or were) “trade secrets” so it’s hard to find ‘em. Don’t ask me how I got ‘em.

Trimming and quality evaluation of your sequences

Start at the EC2 login prompt. Then,

cd /mnt

Make a directory to store all your trimmed data in, and go there:

mkdir trim
cd trim

Now, run Trimmomatic to eliminate Illumina adapters from your sequences –

java -jar /usr/local/bin/trimmomatic-0.32.jar PE ../ecoli_ref-5m_s1.fq ../ecoli_ref-5m_s2.fq s1_pe s1_se s2_pe s2_se ILLUMINACLIP:../illuminaClipping.fa:2:30:10

Next, let’s take a look at data quality using FastQC

mkdir /root/Dropbox/fastqc
/usr/local/share/FastQC/fastqc s1_* s2_* --outdir=/root/Dropbox/fastqc

This will dump the FastQC output into your Dropbox folder, under the folder ‘fastqc’. Go check it out on your local computer in Dropbox – you’re looking for folders named <filename>_fastqc, for example ‘s1_pe_fastqc’; then double click on ‘fastqc_report.html’. (You can’t look at these on the dropbox.com Web site – it won’t interpret the HTML for you.)

It looks like a lot of bad data is present after base 70, so let’s just trim all the sequences that way. Before we do that, we want to interleave the reads again (don’t ask) –

interleave-reads.py s1_pe s2_pe > combined.fq

(interleave-reads is another khmer scripts)

Now, let’s use the FASTX toolkit to trim off bases over 70, and eliminate low-quality sequences. We need to do this both for our combined/paired files and our remaining single-ended files:

fastx_trimmer -Q33 -l 70 -i combined.fq | fastq_quality_filter -Q33 -q 30 -p 50 > combined-trim.fq

fastx_trimmer -Q33 -l 70 -i s1_se | fastq_quality_filter -Q33 -q 30 -p 50 > s1_se.filt

Let’s take a look at what we have –

ls -la

You should see:

drwxr-xr-x 2 root root       4096 2013-04-08 03:33 .
drwxr-xr-x 4 root root       4096 2013-04-08 03:21 ..
-rw-r--r-- 1 root root  802243778 2013-04-08 03:33 combined-trim.fq
-rw-r--r-- 1 root root 1140219324 2013-04-08 03:26 combined.fq
-rw-r--r-- 1 root root  570109662 2013-04-08 03:23 s1_pe
-rw-r--r-- 1 root root     407275 2013-04-08 03:23 s1_se
-rw-r--r-- 1 root root     319878 2013-04-08 03:33 s1_se.filt
-rw-r--r-- 1 root root  570109662 2013-04-08 03:23 s2_pe
-rw-r--r-- 1 root root          0 2013-04-08 03:22 s2_se

Let’s run FastQC on things again, too:

mkdir /root/Dropbox/fastqc.filt
/usr/local/share/FastQC/fastqc combined-trim.fq s1_se.filt --outdir=/root/Dropbox/fastqc.filt

Now go look in your Dropbox folder under ‘fastqc.filt’, folder ‘combined-trim.fq_fastqc’ – looks a lot better, eh?

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