Understanding Read Formats and Quality Controlling Data¶
Note: there are generic instructions for doing quality control at the khmer-protocols Web site. These should work for most Illumina data sets, even those consisting of multiple files. Below, we’ve done a bit of a shorthand because we only have a small data set to filter.
The fastq Format¶
After spending weeks, nay, months of time on designing your study and planning your bioinformatics goals (right?), you finally get the email from you sequencing center: they have your data! You get a link to an ftp server and some login information, and are presented with a list of files. But what are these formats?
Most commonly, you’ll get your data in fastq format. fastq is a really simple way of representing sequence in plain text which is understood by pretty much every piece of bioinformatics software. A fastq file can contain anywhere from one to billions of sequences, and is usually used for reads before they have been assembled. A faux example of the format is:
@read1
+
ATCGTAGCTAGCTAGCT
+
DH<F4CFDFH@FHIBBE
The first line is the name of the sequence; there is no set format for this, though most of the big centers like the NCBI have set standards. This is followed by a ‘+’, a line break, and then the sequence itself, which can be either nucleotide or protein. Then we have another line break, a ‘+’, and a line break, followed by the quality line.
The quality line is the part of the format which is not immediately obvious. This line follows what is known as the phred format. Each ASCII character corresponds to a base, and its integer mapping is used in the equation \(P = 10^{-Q / 10}\), where \(Q\) is the phred score and \(P\) is the probability that the base is incorrectly called.
The counterpart to fastq is fasta, which is essentially fastq without the quality score and a minor formatting change:
>read1
ATCGTAGGTAGGATATA
fasta is usually output by assembly programs, and can be used if data has already been quality controlled and needs to be a more manageable size. However, if you’re not sure what preprocessing steps your data has been through, but you have fasta instead of fastq, you’d be well-off to make sure of what those steps were.
Getting the Data¶
Now that you know a little about the format, let’s get some data. The data set we’ll be using is from everyone’s favorite bacterium, ecoli. We’ll use the command line tool curl to download it to our Amazon machines:
cd /mnt
mkdir ecoli
cd ecoli
curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR390/SRR390202/SRR390202_1.fastq.gz
curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR390/SRR390202/SRR390202_2.fastq.gz
The data came from this study, if you’re interested.
To take a quick look at the files, use less:
less SRR390202_1.fastq.gz
Hit ‘q’ to quit less.
These reads are compressed with gzip to save some space, and are in two files,
because they are paired – the first read in SRR390202_1.fastq.gz
is paired with the first read in SRR390202_2.fastq.gz and so on. Some programs
prefer paired reads to be interleaved, that is, in the same file alternating between
the first and second read pair. Many programs also require the name field in
the fasta/q to explicitly state which part of a pair a read is with a /1 or /2; for example,
in an interleaved file, you might have:
@SRR390202.1 M10_0139:1:2:18915:1321/1
ATCAAGAAAGATTTTAACAGCATTGAC
+
ECCFFFDDHGHFDHJJJJIGIDIJJJJ
@SRR390202.1 M10_0139:1:2:18915:1321/2
GTTCATAGTGACAAGGTAATATTTGTC
+
FDFFFFHHGGIJIF?CIGJJGI@FEFH
Naturally, because this is a standard, almost every program has a different way of doing it. So, be sure to double check the pairing format in your data!
Getting the Dependencies¶
Before we can work with our data, we need to grab a few more dependencies. We’ll download screed, which is a simple python module for parsing fasta files developed by our lab at MSU:
pip install screed
And then khmer, which is a Python interface to a really fast (and awesome) piece of software for counting k-mers, also developed by our lab (more on that later):
cd /usr/local/share
git clone https://github.com/ged-lab/khmer.git
cd khmer
git checkout 2013-caltech-cemi
make
Now that we’ve downloaded and built khmer, we’ll add it to the system’s python PATH so that our scripts know where to find it:
echo 'export PYTHONPATH=/usr/local/share/khmer/python' >> ~/.bashrc
source ~/.bashrc
Get Trimmomatic, which is used for adapter removal and quality filtering:
curl -O http://www.usadellab.org/cms/uploads/supplementary/Trimmomatic/Trimmomatic-0.30.zip
unzip Trimmomatic-0.30.zip
cd Trimmomatic-0.30/
cp trimmomatic-0.30.jar /usr/local/bin
cp -r adapters /usr/local/share/adapters
And then fastx, and its dependencies, which is another tool for quality filtering:
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-0.0.13.2.tar.bz2
tar xjf fastx_toolkit-0.0.13.2.tar.bz2
cd fastx_toolkit-0.0.13.2/
./configure && make && make install
And finally, FastQC, which is a program for assessing quality, finding contaminants, and generally producing nice plots:
apt-get -y install lighttpd
Now, configure:
cd /etc/lighttpd/conf-enabled
ln -fs ../conf-available/10-cgi.conf ./
echo 'cgi.assign = ( ".cgi" => "" )' >> 10-cgi.conf
echo 'index-file.names += ( "index.cgi" ) ' >> 10-cgi.conf
/etc/init.d/lighttpd restart
And install FastQC:
cd /usr/local/share
curl -O http://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.10.1.zip
unzip fastqc_v0.10.1.zip
chmod +x FastQC/fastqc
Assessing your Data with FastQC¶
First we’re going to separate out a subset of the reads for demonstrative purposes; otherwise, things take way too long:
cd /mnt/ecoli
gunzip -c SRR390202_1.fastq.gz | head -n 400000 > SRR390202_1.head.fastq
gunzip -c SRR390202_2.fastq.gz | head -n 400000 > SRR390202_2.head.fastq
Before you go wildly charging at your data with trimmers and filters, it’s always a good idea to know what your data looks like ahead of time. The program we will use for this is FastQC, which parses the quality information from all the reads and produces handy charts and statistics:
mkdir /var/www/ecoli_fastqc
/usr/local/share/FastQC/fastqc SRR390202_1.head.fastq SRR390202_2.head.fastq -o /var/www/ecoli_fastqc
In the previous step, we actually also installed a very lightweight http server. This allows you to host things publicly on your instance and view it through a browser, which in some cases avoids having to download the data to your computer. In the last command, we put our output in the web server’s folder, so let’s go ahead and access it. In a new browser tab, go to:
http://ec2-???????????.compute-1.amazonaws.com/ecoli_fastqc/
replacing the question marks with your EC2 URL. You should be presented with
something like this.
There is a folder for each of your sequence files, each of which contains a file
called fastqc_report.html. Clicking on that file will render the report
in your browser.
Trimming Your Data¶
Based on the FastQC report for the reads, we should probably quality trim them. Although there aren’t any flagged overrepresented sequences, it’s good practice to filter for adapters as well, which can confound assemblers. Trimmomatic can both filter adapters and quality trim, though we’ll only use it for adapter removal here:
mkdir trim
cd trim
java -jar /usr/local/bin/trimmomatic-0.30.jar PE ../SRR390202_1.head.fastq ../SRR390202_2.head.fastq s1_pe s1_se s2_pe s2_se ILLUMINACLIP:/usr/local/share/adapters/TruSeq3-PE.fa:2:30:10
fastx is an alternative which performs many of the same functions as Trimmomatic. We’ll use it for quality filtering; the following flags direct its fastq_quality_filter module to keep reads if 50% of the bases have a quality score over 30:
/usr/local/share/khmer/scripts/interleave-reads.py s1_pe s2_pe > combined.fq
fastq_quality_filter -Q33 -q 30 -p 50 -i combined.fq > combined-trim.fq
fastq_quality_filter -Q33 -q 30 -p 50 -i s1_pe > s1_se.filt
We also interleaved the reads in the previous block, as fastx requires it. We’ll now separate out the reads which had their pair thrown out into their own file, combine them with the output of Trimmomatic, and compress the results:
/usr/local/share/khmer/scripts/extract-paired-reads.py combined-trim.fq
cat combined-trim.fq.se s1_se.filt | gzip -9c > ../SRR390202.head.se.qc.fq.gz
gzip -9c combined-trim.fq.pe > ../SRR390202.head.pe.qc.fq.gz
Finally, move up a directory and get rid of all the unneeded intermediate files:
cd ../
rm -fr trim
Reassess Data Quality¶
Once the reads have been quality-controlled, we should check to make sure that our measures were actually helpful:
mkdir /var/www/ecoli_qc_fastqc
/usr/local/share/FastQC/fastqc SRR390202.head.pe.qc.fq.gz SRR390202.head.se.qc.fq.gz -o /var/www/ecoli_qc_fastqc
Check the output the same way as above.
The Trimmed Data¶
We only quality-controlled a subset of the reads, but we’ll want all of them later on. To that end, we’ve run the programs on the full data, which you can download with:
curl -O https://s3.amazonaws.com/public.ged.msu.edu/SRR390202.pe.qc.fq.gz
curl -O https://s3.amazonaws.com/public.ged.msu.edu/SRR390202.se.qc.fq.gz