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. We assume that you are still in the main directory of
intro_to_mapping data that you copied to
Load the SAMtools module (if not already loaded).
Can you figure out what version of samtools is loaded on TACC and where it is installed?
This should work:
Prepare your directories
Create a new output directory called
samtools_bowtie or whatever makes sense to you.
Let's copy over the read alignment file in the SAM format and the reference genome in FASTA format to the new directory, so that we don't have so many files cluttering our space.
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.)
Take a look at the new *.fai file that was created by this command. Any idea what some of the numbers mean?
q to exit less.
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.
Sort and index the BAM file.
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.bamin 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).
What are all the options doing?
Convert BCF to human-readable 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.
VCF format has alternative Allele Frequency tags denoted by AF1. Try the following command to see what values we have in our files.
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?
What does the -v flag do in grep?
Is not practical, since we will lose vital VCF formatting and may not be able to use this file in the future.
Will preserve all lines that don't have a AF1=0 value and is one way of doing this.
Is a way of doing it in-line and not requiring you to make another file. (But it writes over your existing file!)
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
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:
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
Finding common mutations.
Finding mutations that are unique for each mapper.
- 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?
Look at how the reads supporting these variants were aligned to the reference genome in the Integrative Genomics Viewer (IGV) tutorial.