Bioinformatics for Cancer Genomics 2019

Integrated Assignment - Day 3

Installing programs with root access

Let’s install bowtie!

Without root access

there are many, MANY, MAAAAANY bioinformatics packages available through conda - a python-based package manager. Let’s install that into OUR HOME DIRECTORY which we have permissions to modify.

First, make a directory where we will install our software.

SOFTWARE_HOME=/home/ubuntu/software
mkdir -p $SOFTWARE_HOME
cd $SOFTWARE_HOME

Download the Anaconda installer from here

  • 64-Bit (x86) Installer (533 MB)
  • Right-click / copy link address
  • Should be: https://repo.continuum.io/archive/Anaconda2-5.1.0-Linux-x86_64.sh

or download from commandline

wget https://repo.continuum.io/archive/Anaconda2-5.1.0-Linux-x86_64.sh

Run the install script:

bash Anaconda2-5.1.0-Linux-x86_64.sh
  • Hold Enter to skip Readme
  • Type “yes”
  • Install to: /home/ubuntu/software/anaconda (or wherever you would like to keep this forever)
  • “no” do not modify .bashrc (although you could if you want this to be maintained permanently)
  • “no” do not get microsoft thing

Add conda to path

export PATH="/home/ubuntu/software/anaconda/bin:$PATH"

This line is what the conda installer offered to add to ~/.bashrc

You can add this manually if you would like.

Setup conda channels to download packages from

conda config --add channels r
conda config --add channels bioconda
conda config --add channels BioBuilds

Now you can install packages:

conda install bowtie

Or many packages at once:

conda install \
samtools \
picard \

Continuing with SNV calls

Make a directory to work in and move there

IA_HOME=/home/ubuntu/workspace/IA_wednesday
mkdir -p $IA_HOME
cd $IA_HOME

ANNOVAR Annotations

What does ANNOVAR do?

The following command is what we ran earlier today.

Note that you would need to redefine our environmental variable $ANNOVAR_DIR if you closed your AWS session:

ANNOVAR_DIR=/home/ubuntu/CourseData/CG_data/Module7/install/annovar

Also your mutect_passed.vcf is probably in “/home/ubuntu/workspace/Module7_snv/results/mutect/mutect_passed.vcf”

$ANNOVAR_DIR/table_annovar.pl \
/home/ubuntu/workspace/Module7_snv/results/mutect/mutect_passed.vcf \
$ANNOVAR_DIR/humandb/ \
-buildver hg19 \
-out mutect \
-remove \
-protocol refGene,cytoBand,genomicSuperDups,1000g2015aug_all,avsnp147,dbnsfp30a \
-operation g,r,r,f,f,f \
-nastring . \
--vcfinput

Make environmental variables to refer to out input and output files:

SNV_MODULE_DIR="/home/ubuntu/workspace/Module7_snv"
in_file=$SNV_MODULE_DIR/results/mutect/mutect_passed.vcf
out_file=$SNV_MODULE_DIR/results/annotated/mutect.hg19_multianno.vcf

All header lines contain the phrase “INFO=”. Pull them out with grep.

What is the difference between these headers?

grep "INFO=" $in_file
grep "INFO=" $out_file

To see the lines corresponding to (most of) our selected annotations

grep "INFO=" $out_file | grep -E "refGene|cytoBand|genomicSuperDups|1000g2015aug_all|avsnp147|dbnsfp30a"

Note that “annotation provided by ANNOVAR” is not a terribly helpful descriptor

The ANNOVAR user-guide provides more info

  • http://annovar.openbioinformatics.org/en/latest/user-guide/download/
  • http://annovar.openbioinformatics.org/en/latest/user-guide/filter/

Certain annotations provide a single piece of information:

  • cytoBand = Position along chromosome based on Giemsa-stained chromosomes

While others provide A LOT of information:

  • dbnsfp30a = “SIFT, PolyPhen2 HDIV, PolyPhen2 HVAR, LRT, MutationTaster, MutationAssessor, FATHMM, MetaSVM, MetaLR, VEST, CADD, GERP++, DANN, fitCons, PhyloP and SiPhy scores, but ONLY on coding variants”

Explore a few with the following links:

  • SIFT predicts whether an amino acid substitution affects protein function.

  • PolyPhen-2 (Polymorphism Phenotyping v2) is a tool which predicts possible impact of an amino acid substitution on the structure and function of a human protein using straightforward physical and comparative considerations.

  • SiPhy implements rigorous statistical tests to detect bases under selection from a multiple alignment data.

Adding additional databases to ANNOVAR

DO NOT run the following step today (but in the future, you can download new annotations for use within annovar using:)

annotate_variation.pl -buildver hg19 -downdb -webfrom annovar <Table Name>

We are skipping this today as the downloads can be very large and slow.

Analysing SNV output

We already looked at .bam files to verify SNP calls from reads.

Can we visualize our specific SNPs in the context of other known SNPs?

Use these commands to view very reduced summaries of our generated data.

cat $SNV_MODULE_DIR/results/annotated/mutect.hg19_multianno.txt | cut -f1-3,7,9
cat $SNV_MODULE_DIR/results/annotated/strelka.hg19_multianno.csv | cut -d , -f1-3,7,9

How closely do the two SNV callers agree? What might explain the differences?

Looking at the annotated exonic functions, which SNV(s) might be expected to have functional consequences for the protein?

Interactive exploration of SNVs

To further investigate this, use a web browser to navigate to St. Jude ProteinPaint

  • https://proteinpaint.stjude.org/

Perform the following steps to investigate one of our SNV calls:

  • Enter SOX15 for the gene name of interest.

  • Turn on the “COSMIC” track. “The Catalogue Of Somatic Mutations In Cancer, is the world’s largest and most comprehensive resource for exploring the impact of somatic mutations in human cancer.”

  • Hide “silent” at bottom.
  • Zoom in near the Orange 2 Nonsense at right. (Click and drag along top edge where it says “protein length”.)

  • Further adjust zoom with In / Out near top

  • Hover along bottom legend, just beneath the orange line.
  • What is the genomic location? How does that compare with our SNP calls?
  • Hover beneath the Orange 2 to make a 3 appear.
  • Click 3.

  • Examin the shaded circle that appears. Which cancer types exhibit this mutation?

  • Within the shaded circle, click “List”.
  • Scroll right to see the full details.

  • Are any of the tumor samples familiar?

  • Explore TP53 on your own.
  • Can you find our SNV call?
  • Does it appear to be more or less common than the mutation in SOX15?
  • Is it particularly associated with breast cancer?

Additional commandline SNV practice

If there is time and interest, we can try an additional subset of the data, following the Module7_snv lab from earlier today.

Subset the reads by specifcying a sub-region of the exome bam files using samtools view.

  • b = output bam
  • h = include header

This is another small region that should contain verified SNVs

samtools view -bh \
/home/ubuntu/CourseData/CG_data/Module7/HCC1395/HCC1395_exome_normal.ordered.bam \
12:48000000-50000000 \
-o HCC1395_exome_normal.12.48MB-50MB.bam

samtools view -bh \
/home/ubuntu/CourseData/CG_data/Module7/HCC1395/HCC1395_exome_tumour.ordered.bam \
12:48000000-50000000 \
-o HCC1395_exome_tumour.12.48MB-50MB.bam

Database resources

UCSC Genome Browser

  • https://genome.ucsc.edu/
    • Downloads
    • Genome Data
    • Human
    • Full Dataset

Download a zip containining separate fasta files for each chromosome, unzip, then concatenate these files into one.

wget http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz
gunzip hg38.fa.gz
cat *.fa > hg38_all.fa

TCGA (moved to “Genomic Data Commons”)

cBioPortal

Data for cancer genomics

StatQuest YouTube Series (Joshua Starmer @ UNC-Chapel Hill)

Some personal favorites:

Some cool tools

FastQC

FastQ Screen