Assessing Read Quality

Overview

Teaching: 30 min
Exercises: 20 min

Questions

How can I describe the quality of my data?

Objectives

Explain how a FASTQ file encodes per-base quality scores.

Interpret a FastQC plot summarizing per-base quality across all reads.

Use for loops to automate operations on multiple files.

This lesson has been adapted from the original Data Carpentry - Wrangling Genomics to be run using the NeSI infrastructure as part of the Otago Bioinformatics Spring School instead of AWS.

Bioinformatic workflows

When working with high-throughput sequencing data, the raw reads you get off of the sequencer will need to pass through a number of different tools in order to generate your final desired output. The execution of this set of tools in a specified order is commonly referred to as a workflow or a pipeline.

An example of the workflow we will be using for our variant calling analysis is provided below with a brief description of each step.

workflow

Quality control - Assessing quality using FastQC
Quality control - Trimming and/or filtering reads (if necessary)
Align reads to reference genome
Perform post-alignment clean-up
Variant calling

These workflows in bioinformatics adopt a plug-and-play approach in that the output of one tool can be easily used as input to another tool without any extensive configuration. Having standards for data formats is what makes this feasible. Standards ensure that data is stored in a way that is generally accepted and agreed upon within the community. The tools that are used to analyze data at different stages of the workflow are therefore built under the assumption that the data will be provided in a specific format.

Starting with Data

Often times, the first step in a bioinformatic workflow is getting the data you want to work with onto a computer where you can work with it. If you have outsourced sequencing of your data, the sequencing center will usually provide you with a link that you can use to download your data. Today we will be working with publicly available sequencing data.

We are studying a population of Escherichia coli (designated Ara-3), which were propagated for more than 50,000 generations in a glucose-limited minimal medium. We will be working with three samples from this experiment, one from 5,000 generations, one from 15,000 generations, and one from 50,000 generations. The population changed substantially during the course of the experiment, and we will be exploring how with our variant calling workflow.

The data are paired-end, so we will download two files for each sample. We will use the European Nucleotide Archive to get our data. The ENA “provides a comprehensive record of the world’s nucleotide sequencing information, covering raw sequencing data, sequence assembly information and functional annotation.” The ENA also provides sequencing data in the fastq format, an important format for sequencing reads that we will be learning about today.

To save time, the data has already been downloaded for you and placed in ~/obss_2022/genomic_dna/data/untrimmed_fastq.

Today we’re going to be working inside of ~/obss_2022/genomic_dna. The main directory structure for genomic_dna/ (inside of ~/obss_2022/) currently looks like this currently

genomic_dna/
    |-adapters/
    |-data/
        `-untrimmed_fastq/
    |-docs/
    `-results/    

Now lets navigate to where the untrimmed fastq data is stored.

cd ~/obss_2022/genomic_dna/data/untrimmed_fastq

Original way to obtain the data (Use if not on NeSI)

To download the data, run the commands below.

Here we are using the -p option for mkdir. This option allows mkdir to create the new directory, even if one of the parent directories doesn’t already exist. It also supresses errors if the directory already exists, without overwriting that directory.

It will take about 15 minutes to download the files.
mkdir -p ~/obss_2022/data/untrimmed_fastq/
cd ~/obss_2022/data/untrimmed_fastq

curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR258/004/SRR2589044/SRR2589044_1.fastq.gz
curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR258/004/SRR2589044/SRR2589044_2.fastq.gz
curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR258/003/SRR2584863/SRR2584863_1.fastq.gz
curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR258/003/SRR2584863/SRR2584863_2.fastq.gz
curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR258/006/SRR2584866/SRR2584866_1.fastq.gz
curl -O ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR258/006/SRR2584866/SRR2584866_2.fastq.gz

The data comes in a compressed format, which is why there is a .gz at the end of the file names. This makes it faster to transfer, and allows it to take up less space on our computer. Let’s unzip one of the files so that we can look at the fastq format.

$ gunzip SRR2584863_1.fastq.gz

Quality Control

We will now assess the quality of the sequence reads contained in our fastq files.

workflow_qc

Details on the FASTQ format

Although it looks complicated (and it is), we can understand the fastq format with a little decoding. Some rules about the format include…

Line	Description
1	Always begins with ‘@’ and then information about the read
2	The actual DNA sequence
3	Always begins with a ‘+’ and sometimes the same info in line 1
4	Has a string of characters which represent the quality scores; must have same number of characters as line 2

We can view the first complete read in one of the files our dataset by using head to look at the first four lines.

$ head -n 4 SRR2584863_1.fastq

@SRR2584863.1 HWI-ST957:244:H73TDADXX:1:1101:4712:2181/1
TTCACATCCTGACCATTCAGTTGAGCAAAATAGTTCTTCAGTGCCTGTTTAACCGAGTCACGCAGGGGTTTTTGGGTTACCTGATCCTGAGAGTTAACGGTAGAAACGGTCAGTACGTCAGAATTTACGCGTTGTTCGAACATAGTTCTG
+
CCCFFFFFGHHHHJIJJJJIJJJIIJJJJIIIJJGFIIIJEDDFEGGJIFHHJIJJDECCGGEGIIJFHFFFACD:BBBDDACCCCAA@@CA@C>C3>@5(8&>C:9?8+89<4(:83825C(:A#########################

Line 4 shows the quality for each nucleotide in the read. Quality is interpreted as the probability of an incorrect base call (e.g. 1 in 10) or, equivalently, the base call accuracy (e.g. 90%). To make it possible to line up each individual nucleotide with its quality score, the numerical score is converted into a code where each individual character represents the numerical quality score for an individual nucleotide. For example, in the line above, the quality score line is:

CCCFFFFFGHHHHJIJJJJIJJJIIJJJJIIIJJGFIIIJEDDFEGGJIFHHJIJJDECCGGEGIIJFHFFFACD:BBBDDACCCCAA@@CA@C>C3>@5(8&>C:9?8+89<4(:83825C(:A#########################

The numerical value assigned to each of these characters depends on the sequencing platform that generated the reads. The sequencing machine used to generate our data uses the standard Sanger quality PHRED score encoding, using Illumina version 1.8 onwards. Each character is assigned a quality score between 0 and 41 as shown in the chart below.

Quality encoding: !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJ
                   |         |         |         |         |
Quality score:    01........11........21........31........41                                

Each quality score represents the probability that the corresponding nucleotide call is incorrect. This quality score is logarithmically based, so a quality score of 10 reflects a base call accuracy of 90%, but a quality score of 20 reflects a base call accuracy of 99%. These probability values are the results from the base calling algorithm and depend on how much signal was captured for the base incorporation.

Looking back at our read:

@SRR2584863.1 HWI-ST957:244:H73TDADXX:1:1101:4712:2181/1
TTCACATCCTGACCATTCAGTTGAGCAAAATAGTTCTTCAGTGCCTGTTTAACCGAGTCACGCAGGGGTTTTTGGGTTACCTGATCCTGAGAGTTAACGGTAGAAACGGTCAGTACGTCAGAATTTACGCGTTGTTCGAACATAGTTCTG
+
CCCFFFFFGHHHHJIJJJJIJJJIIJJJJIIIJJGFIIIJEDDFEGGJIFHHJIJJDECCGGEGIIJFHFFFACD:BBBDDACCCCAA@@CA@C>C3>@5(8&>C:9?8+89<4(:83825C(:A#########################

we can now see that there is a range of quality scores, but that the end of the sequence is very poor (# = a quality score of 2).

Exercise

What is the last read in the SRR2584863_1.fastq file? How confident are you in this read?
Solution
$ tail -n 4 SRR2584863_1.fastq 
@SRR2584863.1553259 HWI-ST957:245:H73R4ADXX:2:2216:21048:100894/1
CTGCAATACCACGCTGATCTTTCACATGATGTAAGAAAAGTGGGATCAGCAAACCGGGTGCTGCTGTGGCTAGTTGCAGCAAACCATGCAGTGAACCCGCCTGTGCTTCGCTATAGCCGTGACTGATGAGGATCGCCGGAAGCCAGCCAA
+
CCCFFFFFHHHHGJJJJJJJJJHGIJJJIJJJJIJJJJIIIIJJJJJJJJJJJJJIIJJJHHHHHFFFFFEEEEEDDDDDDDDDDDDDDDDDCDEDDBDBDDBDDDDDDDDDBDEEDDDD7@BDDDDDD>AA>?B?<@BDD@BDC?BDA?
This read has more consistent quality at its end than the first read that we looked at, but still has a range of quality scores, most of them high. We will look at variations in position-based quality in just a moment.

At this point, lets validate that all the relevant tools are installed. (If you are using the original AWS AMI then these should be preinstalled.)

On NeSI, we load the tools by running a script that has the needed software and versions specified.

$ source ~/obss_2022/genomic_dna/modload.sh`

Modules

On NeSI, software is managed through modules which enable many versions of the same program to co-exist. This particular modload.sh script contains the commands that loads the specific software modules needed for our analysis. It uses the module load command which is then followed by the module you wish to use. In the case of fastqc we use module load FastQC. To find the modules for software managed on NeSI you can use module spider <program name>, e.g. module spider fastqc will return the names of the modules related to fastqc.

For more information about using modules on NeSI see their documentation.

Now we can make sure that the tools have loaded.

$ fastqc -h
            FastQC - A high throughput sequence QC analysis tool

SYNOPSIS

        fastqc seqfile1 seqfile2 .. seqfileN

    fastqc [-o output dir] [--(no)extract] [-f fastq|bam|sam]
           [-c contaminant file] seqfile1 .. seqfileN

DESCRIPTION

    FastQC reads a set of sequence files and produces from each one a quality
    control report consisting of a number of different modules, each one of
    which will help to identify a different potential type of problem in your
    data.

    If no files to process are specified on the command line then the program
    will start as an interactive graphical application.  If files are provided
    on the command line then the program will run with no user interaction
    required.  In this mode it is suitable for inclusion into a standardised
    analysis pipeline.

    The options for the program as as follows:

    -h --help       Print this help file and exit

    -v --version    Print the version of the program and exit

    -o --outdir     Create all output files in the specified output directory.                                                                                    
                    Please note that this directory must exist as the program
                    will not create it.  If this option is not set then the
                    output file for each sequence file is created in the same
                    directory as the sequence file which was processed.

    --casava        Files come from raw casava output. Files in the same sample
                    group (differing only by the group number) will be analysed
                    as a set rather than individually. Sequences with the filter
                    flag set in the header will be excluded from the analysis.
                    Files must have the same names given to them by casava
                    (including being gzipped and ending with .gz) otherwise they
                    won't be grouped together correctly.

    --nano          Files come from naopore sequences and are in fast5 format. In
                    this mode you can pass in directories to process and the program
                    will take in all fast5 files within those directories and produce
                    a single output file from the sequences found in all files.

    --nofilter      If running with --casava then don't remove read flagged by
                    casava as poor quality when performing the QC analysis.

    --extract       If set then the zipped output file will be uncompressed in
                    the same directory after it has been created.  By default
                    this option will be set if fastqc is run in non-interactive
                    mode.

    -j --java       Provides the full path to the java binary you want to use to
                    launch fastqc. If not supplied then java is assumed to be in
                    your path.

    --noextract     Do not uncompress the output file after creating it.  You
                    should set this option if you do not wish to uncompress
                    the output when running in non-interactive mode.

    --nogroup       Disable grouping of bases for reads >50bp. All reports will
                    show data for every base in the read.  WARNING: Using this
                    option will cause fastqc to crash and burn if you use it on
                    really long reads, and your plots may end up a ridiculous size.
                    You have been warned!

    -f --format     Bypasses the normal sequence file format detection and
                    forces the program to use the specified format.  Valid
                    formats are bam,sam,bam_mapped,sam_mapped and fastq

    -t --threads    Specifies the number of files which can be processed
                    simultaneously.  Each thread will be allocated 250MB of
                    memory so you shouldn't run more threads than your
                    available memory will cope with, and not more than
                    6 threads on a 32 bit machine

    -c              Specifies a non-default file which contains the list of
    --contaminants  contaminants to screen overrepresented sequences against.
                    The file must contain sets of named contaminants in the
                    form name[tab]sequence.  Lines prefixed with a hash will
                    be ignored.

    -a              Specifies a non-default file which contains the list of
    --adapters      adapter sequences which will be explicity searched against
                    the library. The file must contain sets of named adapters
                    in the form name[tab]sequence.  Lines prefixed with a hash
                    will be ignored.
	
    -l              Specifies a non-default file which contains a set of criteria
    --limits        which will be used to determine the warn/error limits for the
                    various modules.  This file can also be used to selectively
                    remove some modules from the output all together.  The format
                    needs to mirror the default limits.txt file found in the
                    Configuration folder.

   -k --kmers       Specifies the length of Kmer to look for in the Kmer content
                    module. Specified Kmer length must be between 2 and 10. Default
                    length is 7 if not specified.

   -q --quiet       Supress all progress messages on stdout and only report errors.

   -d --dir         Selects a directory to be used for temporary files written when
                    generating report images. Defaults to system temp directory if
                    not specified.

BUGS

    Any bugs in fastqc should be reported either to simon.andrews@babraham.ac.uk
    or in www.bioinformatics.babraham.ac.uk/bugzilla/

if fastqc is not installed then you would expect to see an error like

$ fastqc -h 
The program 'fastqc' is currently not installed. You can install it by typing:
sudo apt-get install fastqc

If this happens check with your instructor before trying to install it.

Assessing Quality using FastQC

In real life, you won’t be assessing the quality of your reads by visually inspecting your FASTQ files. Rather, you’ll be using a software program to assess read quality and filter out poor quality reads. We’ll first use a program called FastQC to visualize the quality of our reads. Later in our workflow, we’ll use another program to filter out poor quality reads.

FastQC has a number of features which can give you a quick impression of any problems your data may have, so you can take these issues into consideration before moving forward with your analyses. Rather than looking at quality scores for each individual read, FastQC looks at quality collectively across all reads within a sample. The image below shows one FastQC-generated plot that indicates a very high quality sample:

good_quality

The x-axis displays the base position in the read, and the y-axis shows quality scores. In this example, the sample contains reads that are 40 bp long. This is much shorter than the reads we are working with in our workflow. For each position, there is a box-and-whisker plot showing the distribution of quality scores for all reads at that position. The horizontal red line indicates the median quality score and the yellow box shows the 1st to 3rd quartile range. This means that 50% of reads have a quality score that falls within the range of the yellow box at that position. The whiskers show the absolute range, which covers the lowest (0th quartile) to highest (4th quartile) values.

For each position in this sample, the quality values do not drop much lower than 32. This is a high quality score. The plot background is also color-coded to identify good (green), acceptable (yellow), and bad (red) quality scores.

Now let’s take a look at a quality plot on the other end of the spectrum.

bad_quality

Here, we see positions within the read in which the boxes span a much wider range. Also, quality scores drop quite low into the “bad” range, particularly on the tail end of the reads. The FastQC tool produces several other diagnostic plots to assess sample quality, in addition to the one plotted above.

Running FastQC

We will now assess the quality of the reads that we downloaded. First, make sure you’re still in the untrimmed_fastq directory

$ cd ~/obss_2022/genomic_dna/data/untrimmed_fastq/

Exercise

How big are the files? (Hint: Look at the options for the ls command to see how to show file sizes.)

Solution

$ ls -l -h

-rw-rw-r-- 1 dcuser dcuser 545M Jul  6 20:27 SRR2584863_1.fastq
-rw-rw-r-- 1 dcuser dcuser 183M Jul  6 20:29 SRR2584863_2.fastq.gz
-rw-rw-r-- 1 dcuser dcuser 309M Jul  6 20:34 SRR2584866_1.fastq.gz
-rw-rw-r-- 1 dcuser dcuser 296M Jul  6 20:37 SRR2584866_2.fastq.gz
-rw-rw-r-- 1 dcuser dcuser 124M Jul  6 20:22 SRR2589044_1.fastq.gz
-rw-rw-r-- 1 dcuser dcuser 128M Jul  6 20:24 SRR2589044_2.fastq.gz

There are six FASTQ files ranging from 124M (124MB) to 545M.

FastQC can accept multiple file names as input, and on both zipped and unzipped files, so we can use the *.fastq* wildcard to run FastQC on all of the FASTQ files in this directory.

$ fastqc *.fastq*

You will see an automatically updating output message telling you the progress of the analysis. It will start like this:

Started analysis of SRR2584863_1.fastq
Approx 5% complete for SRR2584863_1.fastq
Approx 10% complete for SRR2584863_1.fastq
Approx 15% complete for SRR2584863_1.fastq
Approx 20% complete for SRR2584863_1.fastq
Approx 25% complete for SRR2584863_1.fastq
Approx 30% complete for SRR2584863_1.fastq
Approx 35% complete for SRR2584863_1.fastq
Approx 40% complete for SRR2584863_1.fastq
Approx 45% complete for SRR2584863_1.fastq

In total, it should take about five minutes for FastQC to run on all six of our FASTQ files. When the analysis completes, your prompt will return. So your screen will look something like this:

Approx 80% complete for SRR2589044_2.fastq.gz
Approx 85% complete for SRR2589044_2.fastq.gz
Approx 90% complete for SRR2589044_2.fastq.gz
Approx 95% complete for SRR2589044_2.fastq.gz
Analysis complete for SRR2589044_2.fastq.gz
$

The FastQC program has created several new files within our data/untrimmed_fastq/ directory.

$ ls

SRR2584863_1.fastq        SRR2584866_1_fastqc.html  SRR2589044_1_fastqc.html
SRR2584863_1_fastqc.html  SRR2584866_1_fastqc.zip   SRR2589044_1_fastqc.zip
SRR2584863_1_fastqc.zip   SRR2584866_1.fastq.gz     SRR2589044_1.fastq.gz
SRR2584863_2_fastqc.html  SRR2584866_2_fastqc.html  SRR2589044_2_fastqc.html
SRR2584863_2_fastqc.zip   SRR2584866_2_fastqc.zip   SRR2589044_2_fastqc.zip
SRR2584863_2.fastq.gz     SRR2584866_2.fastq.gz     SRR2589044_2.fastq.gz

For each input FASTQ file, FastQC has created a .zip file and a .html file. The .zip file extension indicates that this is actually a compressed set of multiple output files. We’ll be working with these output files soon. The .html file is a stable webpage displaying the summary report for each of our samples.

We want to keep our data files and our results files separate, so we will move these output files into a new directory within our results/ directory.

$ mkdir -p ~/obss_2022/genomic_dna/results/fastqc_untrimmed_reads 
$ mv *.zip ~/obss_2022/genomic_dna/results/fastqc_untrimmed_reads/ 
$ mv *.html ~/obss_2022/genomic_dna/results/fastqc_untrimmed_reads/ 

Now we can navigate into this results directory and do some closer inspection of our output files.

$ cd ~/obss_2022/genomic_dna/results/fastqc_untrimmed_reads/

Viewing the FastQC results

We can use the explorer as part of Jupyter hub to navigate to the directory containing the .html files and click on each one to view the results of Fastqc.

Original instructions for AWS

If we were working on our local computers, we’d be able to look at each of these HTML files by opening them in a web browser.

However, these files are currently sitting on our remote AWS instance, where our local computer can’t see them. And, since we are only logging into the AWS instance via the command line - it doesn’t have any web browser setup to display these files either.

So the easiest way to look at these webpage summary reports will be to transfer them to our local computers (i.e. your laptop).

To transfer a file from a remote server to our own machines, we will use scp, which we learned yesterday in the Shell Genomics lesson.

First we will make a new directory on our computer to store the HTML files we’re transferring. Let’s put it on our desktop for now. Open a new tab in your terminal program (you can use the pull down menu at the top of your screen or the Cmd+t keyboard shortcut) and type:
$ mkdir -p ~/Desktop/fastqc_html 
Now we can transfer our HTML files to our local computer using scp.
$ scp dcuser@ec2-34-238-162-94.compute-1.amazonaws.com:~/dc_workshop/results/fastqc_untrimmed_reads/*.html ~/Desktop/fastqc_html
As a reminder, the first part of the command dcuser@ec2-34-238-162-94.compute-1.amazonaws.com is the address for your remote computer. Make sure you replace everything after dcuser@ with your instance number (the one you used to log in).

The second part starts with a : and then gives the absolute path of the files you want to transfer from your remote computer. Don’t forget the :. We used a wildcard (*.html) to indicate that we want all of the HTML files.

The third part of the command gives the absolute path of the location you want to put the files. This is on your local computer and is the directory we just created ~/Desktop/fastqc_html.

You should see a status output like this:
SRR2584863_1_fastqc.html                      100%  249KB 152.3KB/s   00:01    
SRR2584863_2_fastqc.html                      100%  254KB 219.8KB/s   00:01    
SRR2584866_1_fastqc.html                      100%  254KB 271.8KB/s   00:00    
SRR2584866_2_fastqc.html                      100%  251KB 252.8KB/s   00:00    
SRR2589044_1_fastqc.html                      100%  249KB 370.1KB/s   00:00    
SRR2589044_2_fastqc.html                      100%  251KB 592.2KB/s   00:00  

Now we can go to our new directory and open the 6 HTML files.

Depending on your system, you should be able to select and open them all at once via a right click menu in your file browser.

Exercise

Discuss your results with a neighbor. Which sample(s) looks the best in terms of per base sequence quality? Which sample(s) look the worst?

Solution

All of the reads contain usable data, but the quality decreases toward the end of the reads.

Decoding the other FastQC outputs

We’ve now looked at quite a few “Per base sequence quality” FastQC graphs, but there are nine other graphs that we haven’t talked about! Below we have provided a brief overview of interpretations for each of these plots. For more information, please see the FastQC documentation here

Per tile sequence quality: the machines that perform sequencing are divided into tiles. This plot displays patterns in base quality along these tiles. Consistently low scores are often found around the edges, but hot spots can also occur in the middle if an air bubble was introduced at some point during the run.
Per sequence quality scores: a density plot of quality for all reads at all positions. This plot shows what quality scores are most common.
Per base sequence content: plots the proportion of each base position over all of the reads. Typically, we expect to see each base roughly 25% of the time at each position, but this often fails at the beginning or end of the read due to quality or adapter content.
Per sequence GC content: a density plot of average GC content in each of the reads.
Per base N content: the percent of times that ‘N’ occurs at a position in all reads. If there is an increase at a particular position, this might indicate that something went wrong during sequencing.
Sequence Length Distribution: the distribution of sequence lengths of all reads in the file. If the data is raw, there is often on sharp peak, however if the reads have been trimmed, there may be a distribution of shorter lengths.
Sequence Duplication Levels: A distribution of duplicated sequences. In sequencing, we expect most reads to only occur once. If some sequences are occurring more than once, it might indicate enrichment bias (e.g. from PCR). If the samples are high coverage (or RNA-seq or amplicon), this might not be true.
Overrepresented sequences: A list of sequences that occur more frequently than would be expected by chance.
Adapter Content: a graph indicating where adapater sequences occur in the reads.
K-mer Content: a graph showing any sequences which may show a positional bias within the reads.

Working with the FastQC text output

Now that we’ve looked at our HTML reports to get a feel for the data, let’s look more closely at the other output files. Go back to the tab in Jupyter with your terminal and make sure you’re in our results subdirectory.

$ cd ~/obss_2022/genomic_dna/results/fastqc_untrimmed_reads/ 
$ ls 

SRR2584863_1_fastqc.html  SRR2584866_1_fastqc.html  SRR2589044_1_fastqc.html
SRR2584863_1_fastqc.zip   SRR2584866_1_fastqc.zip   SRR2589044_1_fastqc.zip
SRR2584863_2_fastqc.html  SRR2584866_2_fastqc.html  SRR2589044_2_fastqc.html
SRR2584863_2_fastqc.zip   SRR2584866_2_fastqc.zip   SRR2589044_2_fastqc.zip

Our .zip files are compressed files. They each contain multiple different types of output files for a single input FASTQ file. To view the contents of a .zip file, we can use the program unzip to decompress these files. Let’s try doing them all at once using a wildcard.

$ unzip *.zip

Archive:  SRR2584863_1_fastqc.zip
caution: filename not matched:  SRR2584863_2_fastqc.zip
caution: filename not matched:  SRR2584866_1_fastqc.zip
caution: filename not matched:  SRR2584866_2_fastqc.zip
caution: filename not matched:  SRR2589044_1_fastqc.zip
caution: filename not matched:  SRR2589044_2_fastqc.zip

This didn’t work. We unzipped the first file and then got a warning message for each of the other .zip files. This is because unzip expects to get only one zip file as input. We could go through and unzip each file one at a time, but this is very time consuming and error-prone. Someday you may have 500 files to unzip!

A more efficient way is to use a for loop like we learned in the Shell Genomics lesson to iterate through all of our .zip files. Let’s see what that looks like and then we’ll discuss what we’re doing with each line of our loop.

$ for filename in *.zip
> do
> unzip $filename
> done

In this example, the input is six filenames (one filename for each of our .zip files). Each time the loop iterates, it will assign a file name to the variable filename and run the unzip command. The first time through the loop, $filename is SRR2584863_1_fastqc.zip. The interpreter runs the command unzip on SRR2584863_1_fastqc.zip. For the second iteration, $filename becomes SRR2584863_2_fastqc.zip. This time, the shell runs unzip on SRR2584863_2_fastqc.zip. It then repeats this process for the four other .zip files in our directory.

When we run our for loop, you will see output that starts like this:

Archive:  SRR2589044_2_fastqc.zip
   creating: SRR2589044_2_fastqc/
   creating: SRR2589044_2_fastqc/Icons/
   creating: SRR2589044_2_fastqc/Images/
  inflating: SRR2589044_2_fastqc/Icons/fastqc_icon.png  
  inflating: SRR2589044_2_fastqc/Icons/warning.png  
  inflating: SRR2589044_2_fastqc/Icons/error.png  
  inflating: SRR2589044_2_fastqc/Icons/tick.png  
  inflating: SRR2589044_2_fastqc/summary.txt  
  inflating: SRR2589044_2_fastqc/Images/per_base_quality.png  
  inflating: SRR2589044_2_fastqc/Images/per_tile_quality.png  
  inflating: SRR2589044_2_fastqc/Images/per_sequence_quality.png  
  inflating: SRR2589044_2_fastqc/Images/per_base_sequence_content.png  
  inflating: SRR2589044_2_fastqc/Images/per_sequence_gc_content.png  
  inflating: SRR2589044_2_fastqc/Images/per_base_n_content.png  
  inflating: SRR2589044_2_fastqc/Images/sequence_length_distribution.png  
  inflating: SRR2589044_2_fastqc/Images/duplication_levels.png  
  inflating: SRR2589044_2_fastqc/Images/adapter_content.png  
  inflating: SRR2589044_2_fastqc/fastqc_report.html  
  inflating: SRR2589044_2_fastqc/fastqc_data.txt  
  inflating: SRR2589044_2_fastqc/fastqc.fo  

The unzip program is decompressing the .zip files and creating a new directory (with subdirectories) for each of our samples, to store all of the different output that is produced by FastQC. There are a lot of files here. The one we’re going to focus on is the summary.txt file.

If you list the files in our directory now you will see:

SRR2584863_1_fastqc       SRR2584866_1_fastqc       SRR2589044_1_fastqc
SRR2584863_1_fastqc.html  SRR2584866_1_fastqc.html  SRR2589044_1_fastqc.html
SRR2584863_1_fastqc.zip   SRR2584866_1_fastqc.zip   SRR2589044_1_fastqc.zip
SRR2584863_2_fastqc       SRR2584866_2_fastqc       SRR2589044_2_fastqc
SRR2584863_2_fastqc.html  SRR2584866_2_fastqc.html  SRR2589044_2_fastqc.html
SRR2584863_2_fastqc.zip   SRR2584866_2_fastqc.zip   SRR2589044_2_fastqc.zip

The .html files and the uncompressed .zip files are still present, but now we also have a new directory for each of our samples. We can see for sure that it’s a directory if we use the -F flag for ls.

$ ls -F

SRR2584863_1_fastqc/      SRR2584866_1_fastqc/      SRR2589044_1_fastqc/
SRR2584863_1_fastqc.html  SRR2584866_1_fastqc.html  SRR2589044_1_fastqc.html
SRR2584863_1_fastqc.zip   SRR2584866_1_fastqc.zip   SRR2589044_1_fastqc.zip
SRR2584863_2_fastqc/      SRR2584866_2_fastqc/      SRR2589044_2_fastqc/
SRR2584863_2_fastqc.html  SRR2584866_2_fastqc.html  SRR2589044_2_fastqc.html
SRR2584863_2_fastqc.zip   SRR2584866_2_fastqc.zip   SRR2589044_2_fastqc.zip

Let’s see what files are present within one of these output directories.

$ ls -F SRR2584863_1_fastqc/

fastqc_data.txt  fastqc.fo  fastqc_report.html	Icons/	Images/  summary.txt

Use less to preview the summary.txt file for this sample.

$ less SRR2584863_1_fastqc/summary.txt

PASS    Basic Statistics        SRR2584863_1.fastq
PASS    Per base sequence quality       SRR2584863_1.fastq
PASS    Per tile sequence quality       SRR2584863_1.fastq
PASS    Per sequence quality scores     SRR2584863_1.fastq
WARN    Per base sequence content       SRR2584863_1.fastq
WARN    Per sequence GC content SRR2584863_1.fastq
PASS    Per base N content      SRR2584863_1.fastq
PASS    Sequence Length Distribution    SRR2584863_1.fastq
PASS    Sequence Duplication Levels     SRR2584863_1.fastq
PASS    Overrepresented sequences       SRR2584863_1.fastq
WARN    Adapter Content SRR2584863_1.fastq

The summary file gives us a list of tests that FastQC ran, and tells us whether this sample passed, failed, or is borderline (WARN). Remember, to quit from less you must type q.

Documenting Our Work

We can make a record of the results we obtained for all our samples by concatenating all of our summary.txt files into a single file using the cat command. We’ll call this fastqc_summaries.txt and move it to ~/obss_2022/genomic_dna/docs.

$ cat */summary.txt > ~/obss_2022/genomic_dna/docs/fastqc_summaries.txt

Exercise

Which samples failed at least one of FastQC’s quality tests? What test(s) did those samples fail?

Solution

We can get the list of all failed tests using grep.

$ cd ~/obss_2022/genomic_dna/docs
$ grep FAIL fastqc_summaries.txt

FAIL    Per base sequence quality       SRR2584863_2.fastq.gz
FAIL    Per tile sequence quality       SRR2584863_2.fastq.gz
FAIL    Per base sequence content       SRR2584863_2.fastq.gz
FAIL    Per base sequence quality       SRR2584866_1.fastq.gz
FAIL    Per base sequence content       SRR2584866_1.fastq.gz
FAIL    Adapter Content SRR2584866_1.fastq.gz
FAIL    Adapter Content SRR2584866_2.fastq.gz
FAIL    Adapter Content SRR2589044_1.fastq.gz
FAIL    Per base sequence quality       SRR2589044_2.fastq.gz
FAIL    Per tile sequence quality       SRR2589044_2.fastq.gz
FAIL    Per base sequence content       SRR2589044_2.fastq.gz
FAIL    Adapter Content SRR2589044_2.fastq.gz

Other notes – Optional

Quality Encodings Vary

Although we’ve used a particular quality encoding system to demonstrate interpretation of read quality, different sequencing machines use different encoding systems. This means that, depending on which sequencer you use to generate your data, a # may not be an indicator of a poor quality base call.

This mainly relates to older Solexa/Illumina data, but it’s essential that you know which sequencing platform was used to generate your data, so that you can tell your quality control program which encoding to use. If you choose the wrong encoding, you run the risk of throwing away good reads or (even worse) not throwing away bad reads!

Same Symbols, Different Meanings

Here we see > being used as a shell prompt, whereas > is also used to redirect output. Similarly, $ is used as a shell prompt, but, as we saw earlier, it is also used to ask the shell to get the value of a variable.

If the shell prints > or $ then it expects you to type something, and the symbol is a prompt.

If you type > or $ yourself, it is an instruction from you that the shell should redirect output or get the value of a variable.

Key Points

Quality encodings vary across sequencing platforms.

for loops let you perform the same set of operations on multiple files with a single command.

previous episode

Data Wrangling and Processing for Genomics

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Assessing Read Quality

Overview

Bioinformatic workflows

Starting with Data

Original way to obtain the data (Use if not on NeSI)

Quality Control

Details on the FASTQ format

Exercise

Solution

Modules

Assessing Quality using FastQC

Running FastQC

Exercise

Solution

Viewing the FastQC results

Original instructions for AWS

Exercise

Solution

Decoding the other FastQC outputs

Working with the FastQC text output

Documenting Our Work

Exercise

Solution

Other notes – Optional

Quality Encodings Vary

Same Symbols, Different Meanings

Key Points

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