Overview:

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).

Learning Objectives

1. Familiarize yourself with SAMtools.
2. Use SAMtools to identify variants in the E. coli genomes we mapped in the previous tutorial.

Calling variants in reads mapped by bowtie2

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. If you wish to start this tutorial without completing the Mapping Tutorial, see the bottom section of this page for information about downloading canned data.

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

Load SAMtools

Check if SAMtools is loaded and if not Load the SAMtools module.

module list samtools
module avail samtools
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

Since the $SCRATCH directory on lonestar is effectively infinite for our purposes, we're going to copy the relevant files from our mapping tutorial into a new directory for this tutorial. This should help you identify what files came from what tutorial if you look back at it in the future. Let's copy over just the read alignment file in the SAM format and the reference genome in FASTA format to a new directory called samtools_tutorial.

cds
mkdir samtools_tutorial
cd samtools_tutorial
cp $SCRATCH/bowtie2MappingTutorial/bowtie2/SRR030257.sam .
cp $SCRATCH/bowtie2MappingTutorial/bowtie2/NC_012967.1.fasta .

 

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 NC_012967.1.fasta

Take a look at the new *.fai file that was created by this command see if you have any idea what some of the numbers mean. 

less NC_012967.1.fasta.fai  # can exit with "q"

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.

The following 3 commands are used to convert from SAM to BAM format, sort the BAM file, and index the BAM file. As each command requires the previous command to have been completed it makes more sense to run them on an iDEV node. If you want to submit them to the queue, separate them with a ";" to ensure that they run sequentially on the same node. Under no circumstances should you run this on the head node.

idev -m 60 -q development -A CCBB

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

samtools sort SRR030257.bam SRR030257.sorted
samtools index SRR030257.sorted.bam

Examine the output of the previous commands to get an idea of whats going on. Here are some prompts of how to do that:

• ls samtools_bowtie2 NC_012967.1.fasta SRR030257.sorted.bam.bai NC_012967.1.fasta.fai SRR030257.sam SRR030257.bam SRR030257.sorted.bam

• Samtools appends an extra .bam to whatever we put here, so it would have created SRR030257.sorted.bam.bam, and then we would have had to make a joke about the Flintstones.

• Sure enough, it's the index file for the BAM file.

Call genome variants

Now we use the mpileup command from samtools to compile information about the bases mapped to each reference position. The output is a BCF file. This is a binary form of the text Variant Call Format (VCF).

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

What are all the options doing? 

The samtools mpileup command will take a few minutes to run. You might consider putting it in the background by pressing control-z and then typing the command bg so that you can do some other things in this terminal window at the same time – like starting to run variant calling on the BWA or bowtie mapping results. Remember, there are still many other processors available on this node!

Convert BCF to human-readable VCF:

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

What are these options doing?

Take a look at the samtools_bowtie2/SRR030257.vcf file using less. It has a nice header explaining what the columns mean. Below this are the rows of data describing potential genetic variants.

Optional Exercises

Calling variants in reads mapped by BWA or Bowtie

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

Just be sure you don't write over your old files. Maybe create new directories like samtools_bwa and samtools_bowtie for the output in each case.

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

Filtering VCF files with grep

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_bowtie2/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

Comparing the results of different mappers using bedtools

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 if you generated VCF files for each one.

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_bowtie2/SRR030257.vcf comparison/bowtie2.vcf
cp samtools_bwa/SRR030257.vcf comparison/bwa.vcf
cp samtools_bowtie/SRR030257.vcf comparison/bowtie.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 bowtie2.vcf -b bwa.vcf > common_bowtie2_bwa.vcf

bedtools subtract -a bowtie2.vcf -b common_bowtie2_bwa.vcf > unique_bowtie2.vcf
bedtools subtract -a bwa.vcf -b common_bowtie2_bwa.vcf > unique_bwa.vcf

Further Optional 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...

cds
mkdir mapping
cd mapping
cp -r $BI/gva_course/mapping/bowtie .
cp -r $BI/gva_course/mapping/bwa . 
cp -r $BI/gva_course/mapping/bowtie2 .

• Look at how the reads supporting these variants were aligned to the reference genome in the Integrative Genomics Viewer (IGV) tutorial.
• Look into more sophisticated variant calling with GATK. We recommend starting from the GATK best practice page.