To get started with this lesson, we will need to grab some data from an outside
wget on the command line.
Make sure you are in the dc_workshop directory first
$ cd ~/dc_sample_data $ ls $ r_genomics sra_metadata untrimmed_fastq variant_calling.tar.gz
The file 'variant_calling.tar.gz' is what is commonly called a "tarball", which is a compressed archive similar to the .zip files we have seen before. We can decompress this archive using the command below.
$ tar -zxvf variant_calling.tar.gz
This will create a directory tree that contains some input data (reference genome and fastq files) and a shell script that details the series of commands used to run the variant calling workflow.
$ tree variant_calling variant_calling ├── data │ ├── ref_genome │ │ └── ecoli_rel606.fasta │ └── trimmed_fastq │ ├── SRR097977.fastq │ ├── SRR098026.fastq │ ├── SRR098027.fastq │ ├── SRR098028.fastq │ ├── SRR098281.fastq │ └── SRR098283.fastq └── run_variant_calling.sh 3 directories, 8 files
Without getting into the details yet, the variant calling workflow will do the following steps
Let's walk through the commands in the workflow
The first command is to change to our working directory so the script can find all the files it expects
$ cd ~/dc_workshop/variant_calling
Assign the name/location of our reference genome to a variable ($genome)
Tip: Within the Bash shell you can create variables at any time (as we did above, and during the 'for' loop lesson). Assign and name and the value using the assignment operator: '='. You can check the the shell knows the definition of your variable by typing: echo $variable_name.
We need to index the reference genome for bwa and samtools. bwa and samtools are programs that are pre-installed on our server.
bwa index $genome samtools faidx $genome
Create output paths for various intermediate and result files The -p option means mkdir will create the whole path if it does not exist (no error or message will give given if it does exist)
$ mkdir -p results/sai $ mkdir -p results/sam $ mkdir -p results/bam $ mkdir -p results/bcf $ mkdir -p results/vcf
We will now use a loop to run the variant calling work flow of each of our fastq files, so the list of command below will be execute once for each fastq files.
We would start the loop like this, so the name of each fastq file will by assigned to $fq
$ for fq in data/trimmed_fastq/*.fastq > do > # etc...
In the script, it is a good idea to use echo for debugging/reporting to the screen
$ echo "working with file $fq"
This command will extract the base name of the file (without the path and .fastq extension) and assign it to the $base variable
$ base=$(basename $fq .fastq) $ echo "base name is $base"
We will assign various file names to variables both for convenience but also to make it easier to see what is going on in the commands below.
$ fq=data/trimmed_fastq/$base\.fastq $ sai=results/sai/$base\_aligned.sai $ 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
Our data are now staged. The series of command below will run the steps of the analytical workflow
Align the reads to the reference genome
$ bwa aln $genome $fq > $sai
Convert the output to the SAM format
$ bwa samse $genome $sai $fq > $sam
Convert the SAM file to BAM format
$ samtools view -S -b $sam > $bam
Sort the BAM file
$ samtools sort -f $bam $sorted_bam
Index the BAM file for display purposes
$ samtools index $sorted_bam
Do the first pass on variant calling by counting read coverage
$ samtools mpileup -g -f $genome $sorted_bam > $raw_bcf
Do the SNP calling with bcftools
$ bcftools view -bvcg $raw_bcf > $variants
Filter the SNPs for the final output
$ bcftools view $variants | /usr/share/samtools/vcfutils.pl varFilter - > $final_variants
Run the script: dcuser/dcsampledata/variantcalling/runvariant_calling.sh
$ bash run_variant_calling.sh