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). This tutorial expects you have already completed the Mapping tutorial.

Learning Objectives

1. Work with a more complex conda installation, and how to troubleshoot it.
2. Familiarize yourself with SAMtools.
3. Use SAMtools to identify variants in the E. coli genomes we mapped in the previous tutorial.

Installing SAMtools 

As we have done with: fastqc, cutadapt, and bowtie2, we want to install samtools and bcftools into a new environment (we'll call this one GVA-SNV). Once again, having access to conda-forge will be required to install the most recent version. 

conda create -n GVA-SNV -c conda-forge -c bioconda samtools bcftools

conda install -c bioconda samtools
samtools --version

samtools: error while loading shared libraries: libcrypto.so.1.0.0: cannot open shared object file: No such file or directory

samtools --version
bcftools --version

samtools --version output:

samtools 1.17
Using htslib 1.17
Copyright (C) 2023 Genome Research Ltd.
# Followed by a bunch of compilation details.

bcftools --version output:

bcftools 1.17
Using htslib 1.17
Copyright (C) 2023 Genome Research Ltd.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.

Calling variants in reads mapped by bowtie2

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 GVA_samtools_tutorial.

cds
mkdir GVA_samtools_tutorial
cd GVA_samtools_tutorial
cp $SCRATCH/GVA_bowtie2_mapping/bowtie2/SRR030257.sam .
cp $SCRATCH/GVA_bowtie2_mapping/NC_012967.1.fasta .

cp: cannot stat '/scratch/01821/ded/GVA_bowtie2_mapping/bowtie2/SRR030257.sam': No such file or directory
cp: cannot stat '/scratch/01821/ded/GVA_bowtie2_mapping/NC_012967.1.fasta': No such file or directory

Index the FASTA reference file

Assuming you have the above output for samtools --version and bcftools --version (both 1.15.1), first, you need to index the reference file. (This isn't the same as 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:

1. convert from SAM to BAM format
2. sort the BAM file
3. index the sorted BAM file

As you might guess this is computationally intense and as such must be iDEV node or submitted as a job (more on this on Friday). If you want to submit them to the job queue, you will want to separate them with a ";" to ensure that they run sequentially rather than simultaneously as each uses the output of the previous command. Under no circumstances should you run this on the head node.

samtools view --threads 64 -b -S -o SRR030257.bam SRR030257.sam
samtools sort --threads 64 SRR030257.bam -o SRR030257.sorted.bam
samtools index -@ 64 SRR030257.sorted.bam

It is expected that the first command generate no output, the second command to generate a single line from the bam_sort_core regarding files and memory blocks, and the third line to again generate no output. While the first 2 commands take a few minutes each and the third command is very quick on a single thread, with 64 additional threads all complete very quickly.

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 -1  # This is the number 1 not the letter L. Several people seem to get confused about this each year, it is not necessary for any reason other than to give a single column of output regardless of window size.

NC_012967.1.fasta
NC_012967.1.fasta.fai
SRR030257.bam
SRR030257.sam
SRR030257.sorted.bam
SRR030257.sorted.bam.bai

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). For more information about VCF files: https://docs.gdc.cancer.gov/Data/File_Formats/VCF_Format/

bcftools mpileup
bcftools mpileup --threads 64 -O u -f NC_012967.1.fasta SRR030257.sorted.bam -o SRR030257.bcf

Convert genome variants to human readable format

Once the mpileup command is complete, convert the BCF file to a "human-readable" VCF file using a bcftools command.

bcftools call -v -c SRR030257.bcf > SRR030257.vcf

What are these options doing?

SRR030257.bcf

> SRR030257.vcf

Take a look at the SRR030257.vcf file using less. It has a nice header explaining what the columns mean, including answers to some of your questions from yesterday's presentations. https://docs.gdc.cancer.gov/Data/File_Formats/VCF_Format/ can be used to figure out the columns are and what types of information they provide. Below this are the rows of data describing potential genetic variants.

Analyzing variants detected

VCF format has alternative Allele Frequency tags denoted by AF= Try the following command to see what frequency our variants exist at.

grep AF1 SRR030257.vcf

If you look at the AF1= values you will see all the lines are either ~ 0.5, or 1.

 awk -F";" '{for(i=1;i<=NF;i++){if ($i ~ /AF1/){print $i}}}' SRR030257.vcf

^splits each line into columns based on where the ";"s, then searches through each column, if the "AF1" is found in the column, that column is printed. From the output it is even clearer that frequencies are coming up.

 awk -F";" '{for(i=1;i<=NF;i++){if ($i ~ /AF1/){print $i}}}' SRR030257.vcf | sort

cat SRR030257.vcf | grep AF1=1 > SRR030257.filtered.vcf


grep AF1=1 SRR030257.vcf > SRR030257.filtered.vcf

This is does give us exactly what we asked for: all the lines that show a variant allele frequency of 1. Unfortunately, we lost all the useful header information at the top of the original  SRR030257.vcf file. 

cat SRR030257.vcf | grep -v AF1=0 > SRR030257.filtered.vcf

grep -v AF1=0 SRR030257.vcf > SRR030257.filtered.vcf

Will preserve all lines that don't have 'AF1=0' value on the line and is one way of doing this. If you look closely at the non-filtered file you will see that the frequencies are given as AF1=0.### so by filtering out lines that have 'AF1=0' in them we get rid of all frequencies that are not 1, including say 'AF1=0.99'. 

cat SRR030257.vcf | grep -v AF1=0.[0-8] > SRR030257.filtered.vcf

grep -v AF1=0.[0-8] SRR030257.vcf > SRR030257.filtered.vcf

Return to GVA2023 course page.

Optional Exercises at the end of class or for Wednesday/Thursday choose your own tutorial time.

Calling variants in trimmed reads.

1. Trim both Read1 and Read2 using info from read preprocessing tutorial.
2. Map reads with bowtie2 using info from read mapping tutorial.
3. Call variants using this tutorial.

Remember in the intro tutorial we talked about file/directory naming. Be sure you don't write over your old files. Maybe create a new directories like GVA_samtools_bowtie_improved for the outputs.

Further Optional Exercises

• If you use additional mapping programs, 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). This will be a separate tutorial for tomorrow.

Return to GVA2023 course page.