Lab Module 5 - Somatic Mutations and Annotation

Lab Module 5 - Somatic Mutations and Annotation

This lab is designed to provide an introduction into Somatic Nucleotide Variation detection using two common programs: Strelka and MuTect. This lab will also go over simple annotation of the files using Annovar and manipulation of vcf files using SnpEff

Setup

First login into the server, and then enter the workspace directory:

cd ~/workspace

In order to keep our files in one location, we’re going to create a new directory for this module and enter it:

mkdir Module5
cd Module5

Now to ease the use of commands that will follow, we’re going to set some environment variables. These variables will stand in for the directories where our softwares of interest are location.

Environment variables are only temporary. This means that once you log out of the server, these variables will be erased and must be reset if you’d like to use them again.

Now we’ll set out environment variables for MuTect and SnpEff (which will be used for processing the outputs from the callers):

MUTECT_DIR=/usr/local/mutect
SNPEFF_DIR=/usr/local/snpEff
ANNOVAR_DIR=/usr/local/annovar

Linking the Sequencing and Referencing Data

For this lab module, we’ll be using exome data on the HCC1395 breast cancer cell line. The tumour and normal bams have already been processed and placed on the server. So we’ll create a soft link to the directory that it’s stored. We’ll also create a soft link to where the reference genome is stored, as well as a folder we’ll use later on in the lab:

ln -s /home/ubuntu/CourseData/CG_data/Module5/HCC1395
ln -s /home/ubuntu/CourseData/CG_data/Module5/ref_data
ln -s /home/ubuntu/CourseData/CG_data/Module5/snv_analysis

For this lab we’re going to limit our analysis to just the 7MB and 8MB region of chromosome 17 to ensure processing occurs quickly. The files we’ll be focusing on can be viewed using the following command:

ls HCC1395/HCC1395_exome*.17*

You should see the files:

  • HCC1395_exome_normal.17.7MB-8MB.bam
  • HCC1395_exome_normal.17.7MB-8MB.bam.bai
  • HCC1395_exome_tumour.17.7MB-8MB.bam
  • HCC1395_exome_tumour.17.7MB-8MB.bam.bai

We can take a look at the contents of our bam files by using samtools. The header contains information about the reference genome, commands used to generate the file, and on read groups (information about sample/flow cell/lane etc.), as well as the main contents of the file contain the one aligned read (read name, chromosome, position etc.) per line:

samtools view -h HCC1395/HCC1395_exome_normal.17.7MB-8MB.bam | less -s

Predicting SNVs

MuTect

In order to run MuTect, we simply run the java application of the program in one go. For our MuTect run, we’re going to use two files to help filter out noise and focus on damaging mutations: common SNV’s present in the general population as provided by dbSNP, and potentially damaging SNV’s as documented in COSMIC. These files are stored in the same path as our MUTECT_DIR variable.

Looking at the dbsnp file:

less $MUTECT_DIR/dbsnp_132_b37.leftAligned.vcf

And now the cosmic file:

less $MUTECT_DIR/b37_cosmic_v54_120711.vcf

In order to run MuTect, we’ll be running the following command:

mkdir results; mkdir results/mutect;
/usr/local/java6/bin/java -Xmx4g -jar $MUTECT_DIR/muTect-1.1.4.jar \
--analysis_type MuTect --reference_sequence ref_data/Homo_sapiens.GRCh37.75.dna.primary_assembly.reordered.fa \
--dbsnp $MUTECT_DIR/dbsnp_132_b37.leftAligned.vcf --cosmic $MUTECT_DIR/b37_cosmic_v54_120711.vcf \
--intervals 17:7000000-8000000 --input_file:normal HCC1395/HCC1395_exome_normal.17.7MB-8MB.bam \
--input_file:tumor HCC1395/HCC1395_exome_tumour.17.7MB-8MB.bam --vcf results/mutect/HCC1395.17.7MB-8MB_summary.vcf \
--out results/mutect/HCC1395.17.7MB-8MB_stats.out

The -Xmx4g option allocates 4 GB of RAM for MuTect to run it’s analysis. MuTect will output a vcf file with the calls.

The calls file contains information about the each SNV including information such as the reference allele, alternate allele and whether the SNV should be kept based on MuTect’s quality filtering.

less -s results/mutect/HCC1395.17.7MB-8MB_summary.vcf

Now to subset for just the SNV’s that have passed quality filtering, we use the following commands:

grep -v "REJECT" results/mutect/HCC1395.17.7MB-8MB_summary.vcf > results/mutect/mutect_passed.vcf
grep -v "REJECT" results/mutect/HCC1395.17.7MB-8MB_stats.out > results/mutect/mutect_passed.stats.out

To take a look into the end of files, we can use the command tail:

tail results/mutect/mutect_passed.vcf

Strelka

Next, we’ll call mutations using Strelka. Strelka provides aligner specific config files for bwa, eland, and isaac, each with preconfigured default parameters that work well with the aligner used. Since the bam file we’re working with were processed using bwa, we’ll make a local copy of that file:

mkdir config
cp /usr/local/etc/strelka_config_bwa_default.ini config/strelka_config_bwa.ini

Our data is exome and so the coverage of the file is different, we need to change the isSkipDepthFilters parameter in the strelka_config_bwa.ini file. The default setting of isSkipDepthFilters = 0 must simply be changed to isSkipDepthFilters = 1, and we’ll accomplish this using the vim editor:

vim config/strelka_config_bwa.ini

In order to quit the editor while saving changes, press ESC, following by :x! and pressing enter. The reason why we do this is described on the Strelka FAQ page:

The depth filter is designed to filter out all variants which are called above a multiple of the mean chromosome depth, the default configuration is set to filter variants with a depth greater than 3x the chromosomal mean. If you are using exome/targeted sequencing data, the depth filter should be turned off…

However in whole exome sequencing data, the mean chromosomal depth will be extremely small, resulting in nearly all variants being (improperly) filtered out.

If you were doing this for whole genome sequencing data, then you should leave this parameter set to 0 as the depth of coverage won’t be as high.

A Strelka analysis is performed in 2 steps. In the first step we provide Strelka with all the information it requires to run the analysis, including the tumour and normal bam filenames, the config, and the reference genome. Strelka will create an output directory with the setup required to run the analysis.

configureStrelkaWorkflow.pl \
    --tumor HCC1395/HCC1395_exome_tumour.17.7MB-8MB.bam \
    --normal HCC1395/HCC1395_exome_normal.17.7MB-8MB.bam \
    --ref ref_data/Homo_sapiens.GRCh37.75.dna.primary_assembly.reordered.fa \
    --config config/strelka_config_bwa.ini \
    --output-dir results/strelka/

The output directory will contain a makefile that can be used with the tool make.

To run the Strelka analysis, use make and specify the directory constructed by configureStrelkaWorkflow.pl with make’s '-C' option.

make -C results/strelka/ -j 2

The -j 2 parameter specifies that we want to use 2 cores to run Strelka. Change this number to increase or decrease the parallelization of the job. The more cores the faster the job will be, but the higher the load on the machine that is running Strelka.

If you have access to a grid engine cluster, you can replace the command make with qmake to launch Strelka on the cluster.

Strelka has the benefit of calling SNVs and small indels. Additionally, Strelka calculates variant quality and filters the data in two tiers. The filenames starting with passed contain high quality candidates, and filenames starting with all contain high quality and marginal quality candidates.

The Strelka results are in VCF format, with additional fields explained on the strelka website.

less -s results/strelka/results/passed.somatic.snvs.vcf
less -s results/strelka/results/passed.somatic.indels.vcf

Annotating our mutations

Now that we have our list of mutations, we can annotate them with multiple layers of information, such as the gene name, the function of the location (intronic, exonic), whether the mutation belongs to dbSNP or the 1000 genome project, and multiple other layers.

Let’s place these results in a new folder within our results folder:

mkdir results/annotated/

To run Annovar on the MuTect output, we use the following command:

table_annovar.pl results/mutect/mutect_passed.vcf $ANNOVAR_DIR/humandb/ -buildver hg19 -out results/annotated/mutect -remove -protocol refGene,cytoBand,genomicSuperDups,1000g2015aug_all,avsnp147,dbnsfp30a -operation g,r,r,f,f,f -nastring . --vcfinput

We can view the resulting file by going less on it:

less results/annotated/mutect.hg19_multianno.vcf

Unfortunately our strelka vcf doesn’t have the mandatory info field for genotype GT in the format that annovar requires, and so some manipulation of our strelka results are needed. Annovar requires a minimum of the chromosome, start position, end position, reference allele, and alternate allele.

The following command will remove the header information, format the file as needed by annovar while retaining other information, and storing it into our new file passed.somatic.snvs.txt.

grep -v "##" results/strelka/results/passed.somatic.snvs.vcf | awk '{print $1"\t"$2"\t"$2"\t"$4"\t"$5"\t"$6"\t"$7"\t"$8"\t"$9"\t"$10"\t"$11}' > results/strelka/results/passed.somatic.snvs.txt'

Now we can annotate the Strelka output as we did before:

table_annovar.pl results/strelka/results/passed.somatic.snvs.txt $ANNOVAR_DIR/humandb/ -buildver hg19 -out results/annotated/strelka -remove -protocol refGene,cytoBand,genomicSuperDups,1000g2015aug_all,avsnp147,dbnsfp30a -operation g,r,r,f,f,f -nastring . -csvout

The output from this is stored in comma separated format as strelka.hg19_multianno.csv under the folder Module5/results/annotated/, which can be downloaded and viewed locally in excel.

Parsing specific fields from our vcf files

The VCF format is sometimes not useful for visualization and data exploration purposes which often requires the data to be in tabular format. We can convert from VCF format to tabular format using the extractField() function from SnpSift/SnpEff. Since each mutation caller has a different set of output values in the VCF file, the command needs be adjusted for each mutation caller depending on the fields present in the header.

For example, to convert the MuTect VCF file into a tabular format containing only the chromosome, position, reference allele, alternate allele, function of gene, gene name, cytoband, exonic function, allele frequency of normal, and allele frequency of control:

java -jar $SNPEFF_DIR/SnpSift.jar extractFields -e "." results/annotated/mutect.hg19_multianno.vcf CHROM POS REF ALT Func.refGene Gene.refGene cytoBand ExonicFunc.refGene avsnp147 GEN[0].FA GEN[1].FA > results/mutect_passed_tabbed.txt

We can view the new tab delimited file:

To convert the Strelka VCF file into a tabular format into a similar format containing the chromosome, position, reference allele, alternate allele, depth of normal, depth of tumour, # of A’s, C’s, G’s, and T’s in normal and tumour:

java -jar $SNPEFF_DIR/SnpSift.jar extractFields -e "."  results/strelka/results/passed.somatic.snvs.vcf CHROM POS REF ALT GEN[0].DP GEN[1].DP GEN[0].AU GEN[1].AU GEN[0].CU GEN[1].CU GEN[0].GU GEN[1].GU GEN[0].TU GEN[1].TU > results/strelka_snvs_tabbed.txt

The -e parameter specifies how to represent empty fields. In this case, the “.” character is placed for any empty fields. This facilitates loading and completeness of data. For more details on the extractField() function see the SnpSift documentation.

We can view the first 10 lines of our tabbed file by running the following commands (one at a time):

head results/mutect_passed_tabbed.txt
head results/strelka_snvs_tabbed.txt

Data Exploration

Integrative Genomics Viewer (IGV)

A common step after prediction of SNVs is to visualize these mutations in IGV. Let’s load these bam into IGV. Open IGV, then:

  1. Change the genome to hg19 (if it isn’t already)
  2. File -> Load from URL …
    • http://cbwxx.dyndns.info/Module5/HCC1395/HCC1395_exome_tumour.17.7MB-8MB.bam
    • http://cbwxx.dyndns.info/Module5/HCC1395/HCC1395_exome_normal.17.7MB-8MB.bam

Where the xx is your student number. Once the tumour and normal bam have been loaded into IGV, we can investigate a few predicted positions in IGV:

  • 17:7491818
  • 17:7578406
  • 17:7593160
  • 17:7011723
  • 17:7482929

Manually inspecting these predicted SNVs in IGV is a good way to verify the predictions and also identify potential false positives:

When possible, you should always inspect SNVs

Exploration in R

While IGV is good for visualizing individual mutations, looking at more global characteristics would require loading the data into an analysis language like R.

We will use exome-wide SNV predictions for MuTect for these analyses; specifically, we’re only going to look at the stats.out output from MuTect that has been run on the whole exome file. The processed tabular text files along with the snv_analysis.Rmd RMarkdown file that contains the R code is available in our snv_analysis folder. Alternatively, open a new RMarkdown file and paste the contents of the file here.

Now let’s launch our RStudio instance and continue our analysis there.