Variant calling tutorial

SAMtools is a suite of commands for dealing with databases of mapped reads. You'll be using it quite a bit throughout the course. It includes programs for performing variant calling (mpileup-bcftools).

Calling variants in reads mapped by bowtie

Right now, we'll be using it to call variants (find mutations) in the re-sequenced E. coli genome from the Mapping tutorial. You will need the output SAM files from that tutorial to continue here.

cds
mkdir intro_to_mapping
cd intro_to_mapping
cp -r $BI/ngs_course/intro_to_mapping/bowtie .
cp -r $BI/ngs_course/intro_to_mapping/bwa . 
cp -r $BI/ngs_course/intro_to_mapping/bowtie2 .

We assume that you are still working in the main directory called intro_to_mapping data that you created on $SCRATCH.

Load SAMtools

Load the SAMtools module (if not already loaded).

module load samtools

Can you figure out what version of samtools is loaded on TACC and where it is installed?

samtools
which samtools

Prepare your directories

Create a new output directory called samtools_bowtie or whatever makes sense to you.

Let's copy over just the read alignment file in the SAM format and the reference genome in FASTA format to this new directory, so that we don't have so many files cluttering our space.

mdkdir samtools_bowtie
cp bowtie/SRR030257.sam samtools_bowtie
cp NC_012967.1.fasta samtools_bowtie

Index the FASTA reference file

First, you need to index the reference file. (This isn't indexing it for read mapping. It's indexing it so that SAMtools can quickly jump to a certain base in the reference.)

samtools faidx samtools_bowtie/NC_012967.1.fasta

Take a look at the new *.fai file that was created by this command. Any idea what some of the numbers mean?

less samtools_bowtie/NC_012967.1.fasta.fai

Convert mapped reads from SAM to BAM, sort, and index

SAM is a text file, so it is slow to access information about how any given read was mapped. SAMtools and many of the commands that we will run later work on BAM files (essentially GZIP compressed binary forms of the text SAM files). These can be loaded much more quickly. Typically, they also need to be sorted, so that when the program wants to look at all reads overlapping position 4,129,888, it can easily find them all at once without having to search through the entire BAM file.

Convert from SAM to BAM format.

samtools view -b -S -o samtools_bowtie/SRR030257.bam samtools_bowtie/SRR030257.sam

Sort and index the BAM file.

samtools sort samtools_bowtie/SRR030257.bam samtools_bowtie/SRR030257.sorted
samtools index samtools_bowtie/SRR030257.sorted.bam

This is a really common sequence of commands, so you might want to add it to your personal cheat sheet.

• What new files were created by these commands?
• Why didn't we name the output SRR030257.sorted.bam in the sort command?
• Can you guess what a *.bai file is?

Hint: You might be tempted to gzip BAM files when copying them from one computer to another. Don't bother! They are already internally compressed, so you won't be able to shrink the file. On the other hand, compressing SAM files will save a fair bit of space.

Call genome variants

Now we use the mpileup command from samtools to compile information about the bases mapped to each reference position.

Output BCF file. This is a binary form of the text Variant Call Format (VCF).

samtools mpileup -u -f samtools_bowtie/NC_012967.1.fasta  samtools_bowtie/SRR030257.sorted.bam > samtools_bowtie/SRR030257.bcf

What are all the options doing?

Convert BCF to human-readable VCF:

bcftools view -v -c -g samtools_bowtie/SRR030257.bcf > samtools_bowtie/SRR030257.vcf

What are these options doing?

Take a look at the samtools_bowtie/SRR030257.vcf file. It has a nice header explaining what the columns mean.

Exercise

VCF format has alternative Allele Frequency tags denoted by AF1. Try the following command to see what values we have in our files.

grep AF1 samtools_bowtie/SRR030257.vcf

Optional: For the data we are dealing with, predictions with an allele frequency not equal to 1 are not really applicable. (The reference genome is haploid. There aren't any heterozygotes.) How can we remove these lines from the file?

grep -v *something*

cat input.vcf | grep AF1=1 > output.vcf

cat input.vcf | grep -v AF1=0 > output.vcf

sed -i '/AF1=0/ d' input.vcf

Calling variants in reads mapped by BWA or Bowtie2

Follow the same directions to call variants in the BWA or Bowtie2 mapped reads.

Just be sure you don't write over your old files. Maybe create new directories like samtools_bwa and samtools_bowtie2.

You could also try running all of the commands from inside of the samtools_bwa directory, just for a change of pace.

Comparing the results of different mappers

Often you want to compare the results of variant calling on different samples or using different pipelines. Bedtools is a suite of utility programs that work on a variety of file formats, one of which is conveniently VCF format. It provides many ways of slicing, dicing, and comparing the information in VCF files. For example, we can use it to find out what predictions are the same and which are different from the variant calling on reads mapped with different programs.

Set up a new output directory and copy the respective VCF files to it, renaming them so that we know where they came from:

mkdir comparison
cp samtools_bowtie/SRR030257.vcf comparison/bowtie.vcf
cp samtools_bwa/SRR030257.vcf comparison/bwa.vcf
cp samtools_bowtie2/SRR030257.vcf comparison/bowtie2.vcf
cd comparison

Use the subcommands bedtools intersect and bedtools subtract we can find equal and different predictions between mappers. Try to figure out how to to do this on your own first. Hint: Remember that adding > output.vcf to the end of a command will pipe the output that is to the terminal into a file, so that you can save it.

module load bedtools

bedtools intersect -a bowtie.vcf -b bwa.vcf > common_bowtie_bwa.vcf

bedtools subtract -a bowtie.vcf -b common_bowtie_bwa.vcf > unique_bowtie.vcf
bedtools subtract -a bwa.vcf -b common_bowtie_bwa.vcf > unique_bwa.vcf

Exercises

• Which mapper finds more variants?
• Can you figure out how to filter the VCF files on various criteria, like coverage, quality, ... ?
• How many high quality mutations are there in these E. coli samples relative to the reference genome?

From here...

Look at how the reads supporting these variants were aligned to the reference genome in the Integrative Genomics Viewer (IGV) tutorial.