Setup output directory.
If you do not have an alignment file in the SAM format you may want to start with Introduction to mapping.
cp bowtie/REL606.5.sam samtools/ cp bowtie/REL606.5.fasta samtools/
Index reference file.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).
|Table of Contents|
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.
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!
We assume that you are still working in the main directory called
intro_to_mapping data that you created on
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 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.
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.)
Then run this command to index the reference file.
samtools faidx samtools/REL606.5.fasta
Take a look at the new *.fai file that was created by this command. Any idea what some of the numbers mean?
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 -bSb -S -o samtools_bowtie/REL606.5SRR030257.bam samtools_bowtie/REL606.5SRR030257.sam |borderStyle=solid}
Sort and index the BAM file.
samtools sort samtools/REL606.5.bam samtools/sorted_REL606.5
Variant call output.
samtools mpileup -uf samtools/REL606.5.fasta samtools/sorted_REL606.5.bam \|bcftools view -vcg - \> samtools/output.vcf
Move all 3 bam files and all 3 vcf files to lonestar
_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?
Expand Check that Check that Code Block title List the contents of the output directory
Code Block title Expected output
NC_012967.1.fasta SRR030257.sorted.bam.bai NC_012967.1.fasta.fai SRR030257.sam SRR030257.bam SRR030257.sorted.bam
- Why didn't we name the output
Expand Answer... Answer...
Samtools appends an extra .bam to whatever we put here, so it would have created SRR030257.sorted.bam.bam, and we would have had to make a joke about the Flintstones.
- Can you guess what a *.bai file is?
Expand Answer... Answer...
Sure enough, it's the index file for the BAM file.
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 using
less. It has a nice header explaining what the columns mean. Below this are the rows of data describing potential genetic variants.
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_bowtie2 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_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?
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!)
Comparing the results of different mappers using bedtools
You will need the output from #Calling variants in reads mapped by BWA or Bowtie2 to complete this exercise.
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.
Finding common mutations.
Finding mutations that are unique for each mapper.
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?