Automating a Variant Calling Workflow

Overview

Teaching: 30 min
Exercises: 15 min

Questions

How can I make my workflow more efficient and less error-prone?

Objectives

Write a shell script with multiple variables.

Incorporate a for loop into a shell script.

What is a shell script?

You wrote a simple shell script in a previous lesson that we used to extract bad reads from our FASTQ files and put them into a new file.

Here’s the script you wrote:

grep -B1 -A2 NNNNNNNNNN *.fastq > scripted_bad_reads.txt

echo "Script finished!"

That script was only two lines long, but shell scripts can be much more complicated than that and can be used to perform a large number of operations on one or many files. This saves you the effort of having to type each of those commands over for each of your data files and makes your work less error-prone and more reproducible. For example, the variant calling workflow we just carried out had about eight steps where we had to type a command into our terminal. Most of these commands were pretty long. If we wanted to do this for all six of our data files, that would be forty-eight steps. If we had 50 samples (a more realistic number), it would be 400 steps! You can see why we want to automate this.

We’ve also used for loops in previous lessons to iterate one or two commands over multiple input files. In these for loops you used variables to enable you to run the loop on multiple files. We will be using variable assignments like this in our new shell scripts.

Here’s the for loop you wrote for unzipping .zip files:

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

And here’s the one you wrote for running Trimmomatic on all of our .fastq sample files.

$ for infile in *.fastq
> do
> outfile=${infile}_trim.fastq
> java -jar ~/Trimmomatic-0.32/trimmomatic-0.32.jar SE $infile $outfile SLIDINGWINDOW:4:20 MINLEN:20
> done

In this lesson, we will create two shell scripts. The first will run our FastQC analysis, including creating our summary file. To do this, we’ll take each of the commands we entered to run FastQC and process the output files and put them into a single file with a .sh extension. The .sh is not essential, but serves as a reminder to ourselves and to the computer that this is a shell script.

Analyzing Quality with FastQC

We will use the command touch to create a new file where we will write our shell script. We will create this script in a new directory called scripts/. Previously, we used nano to create and open a new file. The command touch allows us to create a new file without opening that file.

$ cd ~/dc_workshop
$ mkdir scripts
$ cd scripts
$ touch read_qc.sh
$ ls 

read_qc.sh

We now have an empty file called read_qc.sh in our scripts/ directory. We will now open this file in nano and start building our script.

$ nano read_qc.sh

Enter the following pieces of code into your shell script (not into your terminal prompt).

Our first line will move us into the untrimmed_fastq/ directory when we run our script.

cd ~/dc_workshop/data/untrimmed_fastq/

These next two lines will give us a status message to tell us that we are currently running FastQC, then will run FastQC on all of the files in our current directory with a .fastq extension.

echo "Running FastQC ..."
~/FastQC/fastqc *.fastq

Our next line will create a new directory to hold our FastQC output files. Here we are using the -p option for mkdir. This option forces mkdir to create the new directory, even if one of the parent directories doesn’t already exist. It is a good idea to use this option in your shell scripts to avoid running into errors if you don’t have the directory structure you think you do.’

mkdir -p ~/dc_workshop/results/fastqc_untrimmed_reads

Our next three lines first give us a status message to tell us we are saving the results from FastQC, then moves all of the files with a .zip or a .html extension to the directory we just created for storing our FastQC results.

echo "Saving FastQC results..."
mv *.zip ~/dc_workshop/results/fastqc_untrimmed_reads/
mv *.html ~/dc_workshop/results/fastqc_untrimmed_reads/

The next line moves us to the results directory where we’ve stored our output.

cd ~/dc_workshop/results/fastqc_untrimmed_reads/

The next five lines should look very familiar. First we give ourselves a status message to tell us that we’re unzipping our ZIP files. Then we run our for loop to unzip all of the .zip files in this directory.

echo "Unzipping..."
for filename in *.zip
    do
    unzip $filename
    done

Next we concatenate all of our summary files into a single output file, with a status message to remind ourselves that this is what we’re doing.

echo "Saving summary..."
cat */summary.txt > ~/dc_workshop/docs/fastqc_summaries.txt

Using echo statements

We’ve used echo statements to add progress statements to our script. Our script will print these statements as it is running and therefore we will be able to see how far our script has progressed.

Your full shell script should now look like this:

cd ~/dc_workshop/data/untrimmed_fastq/

echo "Running FastQC ..."
~/FastQC/fastqc *.fastq

mkdir -p ~/dc_workshop/results/fastqc_untrimmed_reads

echo "Saving FastQC results..."
mv *.zip ~/dc_workshop/results/fastqc_untrimmed_reads/
mv *.html ~/dc_workshop/results/fastqc_untrimmed_reads/

cd ~/dc_workshop/results/fastqc_untrimmed_reads/

echo "Unzipping..."
for filename in *.zip
    do
    unzip $filename
    done

echo "Saving summary..."
cat */summary.txt > ~/dc_workshop/docs/fastqc_summaries.txt

Save your file and exit nano. We can now run our script:

$ bash read_qc.sh

Running FastQC ...
Started analysis of SRR097977.fastq
Approx 5% complete for SRR097977.fastq
Approx 10% complete for SRR097977.fastq
Approx 15% complete for SRR097977.fastq
Approx 20% complete for SRR097977.fastq
Approx 25% complete for SRR097977.fastq
. 
. 
. 

For each of your sample files, FastQC will ask if you want to replace the existing version with a new version. This is because we have already run FastQC on this samples files and generated all of the outputs. We are now doing this again using our scripts. Go ahead and select A each time this message appears. It will appear once per sample file (six times total).

replace SRR097977_fastqc/Icons/fastqc_icon.png? [y]es, [n]o, [A]ll, [N]one, [r]ename:

Automating the Rest of our Variant Calling Workflow

Now we will create a second shell script to complete the other steps of our variant calling workflow. To do this, we will take all of the individual commands that we wrote before, put them into a single file, add variables so that the script knows to iterate through our input files and do a few other formatting that we’ll explain as we go. This is very similar to what we did with our read_qc.sh script, but will be a bit more complex.

Our variant calling workflow will do the following steps

Index the reference genome for use by bwa and samtools
Align reads to reference genome
Convert the format of the alignment to sorted BAM, with some intermediate steps.
Calculate the read coverage of positions in the genome
Detect the single nucleotide polymorphisms (SNPs)
Filter and report the SNP variants in VCF (variant calling format)

We will be creating a script together to do all of these steps.

First, we will create a new script in our scripts/ directory using touch.

$ cd ~/dc_workshop/scripts
$ touch run_variant_calling.sh
$ ls 

read_qc.sh  run_variant_calling.sh

We now have a new empty file called run_variant_calling.sh in our scripts/ directory. We will open this file in nano and start building our script, like we did before.

$ nano run_variant_calling.sh

Enter the following pieces of code into your shell script (not into your terminal prompt).

First we will change our working directory so that we can create new results subdirectories in the right location.

cd ~/dc_workshop/results

Next we tell our script where to find the reference genome by assigning the genome variable to the path to our reference genome:

genome=~/dc_workshop/data/ref_genome/ecoli_rel606.fasta

Creating Variables

Within the Bash shell you can create variables at any time (as we did above, and during the ‘for’ loop lesson). Assign any name and the value using the assignment operator: ‘=’. You can check the current definition of your variable by typing into your script: echo $variable_name.

Next we index our reference genome for BWA.

bwa index $genome

And create the directory structure to store our results in:

mkdir -p sai sam bam bcf vcf

We will now use a loop to run the variant calling workflow on each of our FASTQ files. The full list of commands within the loop will be executed once for each of the FASTQ files in the data/trimmed_fastq/ directory. We will include a few echo statements to give us status updates on our progress.

The first thing we do is assign the name of the FASTQ file we’re currently working with to a variable called fq and tell the script to echo the filename back to us so we can check which file we’re on.

for fq in ~/dc_workshop/data/trimmed_fastq_small/*.fastq
    do
    echo "working with file $fq"
    done

Indentation

All of the statements within your for loop (i.e. everything after the for line and including the done line) need to be indented. This indicates to the shell interpretor that these statements are all part of the for loop and should be done once per input.

Exercise

This is a good time to check that our script is assigning the FASTQ filename variables correctly. Save your script and run it. What output do you see?
Solution
$ bash run_variant_calling.sh
[bwa_index] Pack FASTA... 0.04 sec
[bwa_index] Construct BWT for the packed sequence...
[bwa_index] 1.10 seconds elapse.
[bwa_index] Update BWT... 0.03 sec
[bwa_index] Pack forward-only FASTA... 0.02 sec
[bwa_index] Construct SA from BWT and Occ... 0.64 sec
[main] Version: 0.7.5a-r405
[main] CMD: bwa index /home/dcuser/dc_workshop/data/ref_genome/ecoli_rel606.fasta
[main] Real time: 1.892 sec; CPU: 1.829 sec
working with file /home/dcuser/dc_workshop/data/trimmed_fastq_small/SRR097977.fastq_trim.fastq
working with file /home/dcuser/dc_workshop/data/trimmed_fastq_small/SRR098026.fastq_trim.fastq
working with file /home/dcuser/dc_workshop/data/trimmed_fastq_small/SRR098027.fastq_trim.fastq
working with file /home/dcuser/dc_workshop/data/trimmed_fastq_small/SRR098028.fastq_trim.fastq
working with file /home/dcuser/dc_workshop/data/trimmed_fastq_small/SRR098281.fastq_trim.fastq
working with file /home/dcuser/dc_workshop/data/trimmed_fastq_small/SRR098283.fastq_trim.fastq
You should see “working with file . . . “ for each of the six FASTQ files in our trimmed_fastq/ directory. If you don’t see this output, then you’ll need to troubleshoot your script. A common problem is that your directory might not be specified correctly. Ask for help if you get stuck here!

Now that we’ve tested the components of our loops so far, we will add our next few steps. Remove the line done from the end of your script and add the next two lines. These lines extract the base name of the file (excluding the path and .fastq extension) and assign it to a new variable called base variable. Add done again at the end so we can test our script.

    base=$(basename $fq .fastq_trim.fastq)
    echo "base name is $base"
    done

Now if you save and run your script, the final lines of your output should look like this:

working with file /home/dcuser/dc_workshop/data/trimmed_fastq_small/SRR097977.fastq_trim.fastq
base name is SRR097977
working with file /home/dcuser/dc_workshop/data/trimmed_fastq_small/SRR098026.fastq_trim.fastq
base name is SRR098026
working with file /home/dcuser/dc_workshop/data/trimmed_fastq_small/SRR098027.fastq_trim.fastq
base name is SRR098027
working with file /home/dcuser/dc_workshop/data/trimmed_fastq_small/SRR098028.fastq_trim.fastq
base name is SRR098028
working with file /home/dcuser/dc_workshop/data/trimmed_fastq_small/SRR098281.fastq_trim.fastq
base name is SRR098281
working with file /home/dcuser/dc_workshop/data/trimmed_fastq_small/SRR098283.fastq_trim.fastq
base name is SRR098283

For each file, you see two statements printed to the terminal window. This is because we have two echo statements. The first tells you which file the loop is currently working with. The second tells you the base name of the file. This base name is going to be used to create our output files.

Next we will create variables to store the names of our output files. This will make your script easier to read because you won’t need to type out the full name of each of the files. We’re using the base variable that we just defined, and adding different file name extensions to represent the files that will come out of each step in our workflow. Remember to delete the done line from your script before adding these lines.

    fq=~/dc_workshop/data/trimmed_fastq_small/${base}.fastq_trim.fastq
    sai=~/dc_workshop/results/sai/${base}_aligned.sai
    sam=~/dc_workshop/results/sam/${base}_aligned.sam
    bam=~/dc_workshop/results/bam/${base}_aligned.bam
    sorted_bam=~/dc_workshop/results/bam/${base}_aligned_sorted.bam
    raw_bcf=~/dc_workshop/results/bcf/${base}_raw.bcf
    variants=~/dc_workshop/results/bcf/${base}_variants.bcf
    final_variants=~/dc_workshop/results/vcf/${base}_final_variants.vcf     

Now that we’ve created our variables, we can start doing the steps of our workflow. Remove the done line from the end of your script and add the following lines.

1) align the reads to the reference genome and output a .sai file:

    bwa aln $genome $fq > $sai

2) convert the output to SAM format:

    bwa samse $genome $sai $fq > $sam

3) convert the SAM file to BAM format:

    samtools view -S -b $sam > $bam

4) sort the BAM file:

    samtools sort -f $bam $sorted_bam

5) index the BAM file for display purposes:

    samtools index $sorted_bam

6) do the first pass on variant calling by counting read coverage

    samtools mpileup -g -f $genome $sorted_bam > $raw_bcf

7) call SNPs with bcftools:

    bcftools view -bvcg $raw_bcf > $variants

8) filter the SNPs for the final output:

    bcftools view $variants | /usr/share/samtools/vcfutils.pl varFilter - > $final_variants
    done

We added a done line after the SNP filtering step because this is the last step in our for loop.

Your script should now look like this:

cd ~/dc_workshop/results

genome=~/dc_workshop/data/ref_genome/ecoli_rel606.fasta

bwa index $genome

mkdir -p sai sam bam bcf vcf

for fq in ~/dc_workshop/data/trimmed_fastq_small/*.fastq
    do
    echo "working with file $fq"

    base=$(basename $fq .fastq_trim.fastq)
    echo "base name is $base"

    fq=~/dc_workshop/data/trimmed_fastq_small/$base.fastq_trim.fastq
    sai=~/dc_workshop/results/sai/${base}_aligned.sai
    sam=~/dc_workshop/results/sam/${base}_aligned.sam
    bam=~/dc_workshop/results/bam/${base}_aligned.bam
    sorted_bam=~/dc_workshop/results/bam/${base}_aligned_sorted.bam
    raw_bcf=~/dc_workshop/results/bcf/${base}_raw.bcf
    variants=~/dc_workshop/results/bcf/${base}_variants.bcf
    final_variants=~/dc_workshop/results/vcf/${base}_final_variants.vcf 

    bwa aln $genome $fq > $sai
    bwa samse $genome $sai $fq > $sam
    samtools view -S -b $sam > $bam
    samtools sort -f $bam $sorted_bam
    samtools index $sorted_bam
    samtools mpileup -g -f $genome $sorted_bam > $raw_bcf
    bcftools view -bvcg $raw_bcf > $variants
    bcftools view $variants | /usr/share/samtools/vcfutils.pl varFilter - > $final_variants
    done

Exercise

It’s a good idea to add comments to your code so that you (or a collaborator) can make sense of what you did later. Look through your existing script. Discuss with a neighbor where you should add comments. Add comments (anything following a # character will be interpreted as a comment, bash will not try to run these comments as code).

Now we can run our script:

$ bash run_variant_calling.sh

BWA variations

BWA is a software package for mapping low-divergent sequences against a large reference genome, such as the human genome, and it’s freely available here. It consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM, each being invoked with different sub-commands: aln + samse + sampe for BWA-backtrack, bwasw for BWA-SW and mem for the BWA-MEM algorithm. BWA-backtrack is designed for Illumina sequence reads up to 100bp, while the rest two are better fitted for longer sequences ranged from 70bp to 1Mbp. A general rule of thumb is to use bwa mem for reads longer than 70 bp, whereas bwa aln has a moderately higher mapping rate and a shorter run time for short reads (~36bp). You can find a more indepth discussion in the bwa doc page as well as in this blog post. In this lesson we have been using the aln for performing the alignment, but the same process can be performed with bwa mem which doesn’t require the creation of the index files. The process is modified starting from mkdir step, and omitting all directories relevant to the .sai index files, i.e.:

Create output paths for various intermediate and result files.
$ mkdir -p results/sam results/bam results/bcf results/vcf
Assign file names to variables
$ fq=data/trimmed_fastq/${base}.fastq
$ sam=results/sam/${base}_aligned.sam
$ bam=results/bam/${base}_aligned.bam
$ sorted_bam=results/bam/${base}_aligned_sorted.bam
$ raw_bcf=results/bcf/${base}_raw.bcf
$ variants=results/bcf/${base}_variants.bcf
$ final_variants=results/vcf/${base}_final_variants.vcf  
Run the alignment
$ bwa mem -M $genome $fq > $sam
As an exercise, try and change your existing script file, from using the aln method to the mem method.

In the previous lesson we mentioned that we were using small subsets of our trimmed FASTQ files to run our variant calling workflow, in the interests of time. The output files you now have in your dc_workshop/results directory are based on the small sample FASTQ files (data from the trimmed_fastq_small directory). We’ve also provided the result files from running the run_variant_calling.sh script on the full-sized trimmed FASTQ files. Let’s do a few comparisons.

Exercise (Novice)

How much larger are the full-sized trimmed FASTQ files than the small trimmed FASTQ files we just ran our variant calling script on?

Hint: You can find a copy of the full-sized trimmed FASTQ files in the ~/.dc_sampledata_lite/solutions/wrangling-solutions/trimmed_fastq directory.
Solution
$ ls -lh ~/.dc_sampledata_lite/solutions/wrangling-solutions/trimmed_fastq
total 13G
$ ls -lh ~/dc_workshop/data/trimmed_fastq_small
total 430M

Exercise (Intermediate)

Vizualize the alignment of the reads for our SRR098281.fastq_trim.fastq_small sample. What variant is present at position 145? What is the canonical nucleotide in that position?
Solution
$ samtools tview ~/dc_workshop/results/bam/SRR098281_aligned_sorted.bam ~/dc_workshop/data/ref_genome/ecoli_rel606.fasta
T is the variant. G is canonical.
Now vizualize the alignment of the reads for the full-length trimmed FASTQ file for the SRR098281 sample. What variants are present in position 145?

Hint: You can find a copy of the output files for the full-length trimmed FASTQ file variant calling in the ~/.dc_sampledata_lite/solutions/wrangling-solutions/variant_calling/ directory.
Solution
$ samtools tview ~/.dc_sampledata_lite/solutions/wrangling-solutions/variant_calling/bam/SRR098281_aligned_sorted.bam ~/dc_workshop/data/ref_genome/ecoli_rel606.fasta
In the full-length file, T is still the only variant present at this location.

Bonus Exercise (Advanced)

If you have time after completing the previous two exercises, use run_variant_calling.sh to run the variant calling pipeline on the full-sized trimmed FASTQ files. You should have a copy of these already in ~/dc_workshop/data/trimmed_fastq but if you dont, there is a copy in ~/.dc_sampledata_lite/solutions/wrangling-solutions/trimmed_fastq.

Key Points

We can combine multiple commands into a shell script to automate a workflow.

Use echo statements within your scripts to get an automated progress update.

previous episode

Data Wrangling and Processing for Genomics

lesson home

Automating a Variant Calling Workflow

Overview

What is a shell script?

Analyzing Quality with FastQC

Using `echo` statements

Automating the Rest of our Variant Calling Workflow

Creating Variables

Indentation

Exercise

Solution

Exercise

BWA variations

Exercise (Novice)

Solution

Exercise (Intermediate)

Solution

Solution

Bonus Exercise (Advanced)

Key Points

previous episode

lesson home

previous episode

Data Wrangling and Processing for Genomics

lesson home

Automating a Variant Calling Workflow

Overview

What is a shell script?

Analyzing Quality with FastQC

Using echo statements

Automating the Rest of our Variant Calling Workflow

Creating Variables

Indentation

Exercise

Solution

Exercise

BWA variations

Exercise (Novice)

Solution

Exercise (Intermediate)

Solution

Solution

Bonus Exercise (Advanced)

Key Points

previous episode

lesson home

Using `echo` statements