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
- Familiarize yourself with SAMtools.
- 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.
Can you figure out what version of samtools is loaded on TACC and where it is installed?
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.
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"
As you can see, the less command also works perfectly well with files that are not in danger of crashing anything without cluttering your terminal with lines of a file.
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.
Do not run on head node
Many commands past this point are computationally intensive. You should run them through an idev shell or by qsub. We recommend idev for the tutorial.
idev -m 60 -q development -A UT-2015-05-18
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:
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. 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? Try calling samtools mpileup without any options to see if you can figure it out before clicking below to
The samtools mpileup command will take a few minutes to run. As practice for a fairly common occurrence when working with the iDEV environment, once the command is running, you should try 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. Remember, there are still many other processors available on this node!
Once the mpileup command is complete, convert the BCF file to a "human-readable" VCF file using the bcftools command (the which command will tell you where this command is located and examination of that path should tell you how you have access to it).
bcftools view -v -c -g SRR030257.bcf > SRR030257.vcf
What are these options doing?
Take a look at the 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 AF= Try the following command to see what values we have in our files.
grep AF1 SRR030257.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 -i samtools_bowtie2/SRR030257.vcf comparison/bowtie2.vcf cp -i samtools_bwa/SRR030257.vcf comparison/bwa.vcf cp -i 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.
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...
If you do not have the output from the Mapping tutorial, run these commands to copy over the output that would have been produced. Then, you can immediately start this tutorial!
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.
