UMCCR WGS tumor/normal reporting

umccrise is a Snakemake workflow that post-processes results from the Illumina DRAGEN WGS tumor/normal pipeline and generates HTML reports helpful for researchers and curators at UMCCR.

• Summary
• Detailed Workflow
• History
• Example Reports
• Usage
• Installation
• Reference data
  • Versioning
  • Syncing with AWS S3
• Testing
• AWS
• Advanced usage
  • Inputs with named arguments
  • Controlling the number of CPUs
  • Running selected stages
  • Custom input
  • Running on selected samples
• Updating
• Development
• Docker
• Building reference data
  • PURPLE
  • GNOMAD
  • PCGR
  • Problem regions
  • Coding regions (SAGE)
  • Ensembl annotation
  • Hotspots
  • Other HMF files
  • Fusions
  • SnpEff
  • DVC

Summary

In summary, umccrise can:

• Filter artefacts and germline leakage from somatic variant calls
• Run PCGR to annotate, prioritize and report somatic variants
• Run CPSR to annotate, prioritize and report germline variants
• Filter, annotate, prioritize and report structural variants (SVs) from Manta
• Run PURPLE to call copy number variants (CNVs), recover SVs, and infer tumor purity & ploidy
• Generate a MultiQC report that summarizes quality control statistics in context of background "gold standard" samples
• Generate a cancer report with mutational signatures, inferred HRD status, circos plots, prioritized copy number and structural variant calls
• Run CACAO to calculate coverage in common hotspots, as well as goleft indexcov to estimate coverage problems
• Run Conpair to estimate tumor/normal concordance and sample contamination
• Run oviraptor to detect viral integration sites and affected genes

Detailed Workflow

See workflow.md for a detailed description of the workflow.

History

See HISTORY.md for the version history.

Example Reports

Below are example reports for a HCC1395/HCC1395BL cell line tumor/normal pair sequenced and validated by the SEQC-II consortium.

1. MultiQC (quality control metrics and plots)

2. Cancer report (mutational signatures, circos plots, CNV, SV, oncoviruses)

3. CPSR (germline variants)

4. PCGR (somatic variants)

5. CACAO (coverage reports)

Usage

Given input data from DRAGEN somatic and germline output folders, or a custom set of BAM or VCF files, umccrise can be run with:

umccrise <input-data ...> -o umccrised

For more options, see Advanced usage.

Installation

Create a umccrise directory and install the umccrise GitHub repo along with the required conda environments with the following:

mkdir umccrise
cd umccrise
git clone https://github.com/umccr/umccrise umccrise.git
bash umccrise.git/install.sh

The above will generate a load_umccrise.sh script that can be sourced to load the umccrise conda environment on demand:

source load_umccrise.sh

Reference data

umccrise needs a 64G bundle of reference data to run. From within the UMCCR AWS setup, sign in to AWS, and run umccrise_refdata_pull

aws sso login --profile sso-dev-admin
umccrise_refdata_pull
export UMCCRISE_GENOMES=${PWD}/refdata/genomes

Alternatively, you can specify a custom path with --genomes <path>. The path can be a tarball and will be automatically extracted.

The path can also be a location on S3 or GDS, prefixed with s3:// or gds://. E.g.:

umccrise /input --genomes s3://umccr-refdata-dev/genomes

Versioned locations would also be checked. For the case above, umccrise will check the following locations in the order specified:

• s3://umccr-refdata-dev/genomes_102
• s3://umccr-refdata-dev/genomes_10, and
• s3://umccr-refdata-dev/genomes, assuming that the reference_data package version is 1.0.2.

umccrise will sync the reference data locally into a ~/umccrise_genomes directory. You can symlink any other path to that path if you want a different location. If the data is already downloaded, umccrise will only attempt to update the changed files or upload new ones. To avoid attempts to check S3/GDS again at all, specify the downloaded location directly: --genomes ~/umccrise_genomes

Another option to specify the reference data is through an environment variable $UMCCRISE_GENOMES

If you have access to UMCCR's AWS account, you can sync the reference data from s3://umccr-refdata-dev. If you have access to UMCCR's NCI Gadi account, you can sync the data from /g/data3/gx8/extras/umccrise/genomes. Otherwise, you can build the bundle from scratch following the details below.

Versioning

The reference data is versioned as a python package at https://github.com/umccr/reference_data

Syncing with AWS S3

ref_data_version=1.0.0
aws s3 sync hg38 s3://umccr-refdata-dev/genomes_${ref_data_version//./}/hg38
aws s3 sync hg38-manifest.txt s3://umccr-refdata-dev/genomes_${ref_data_version//./}/hg38-manifest.txt

Testing

Load the umccrise environment, clone the repo with toy test data, and run nosetests:

source load_umccrise.sh
git clone https://github.com/umccr/umccrise_test_data
TEST_OPTS="-c -j2" nosetests -s umccrise_test_data/test.py

AWS

umccrise on AWS is run via AWS Batch in a defined compute environment. This is set up and maintained via the umccrise Terraform Stack. This stack also defines the version of umccrise that is used within AWS and how umccrise jobs are triggered.

Advanced usage

Inputs with named arguments

Inputs can be provided to umccrise as a positional argument (see Usage) or alternatively as named arguments (see examples below). This is useful when dealing with DRAGEN input, which have two paired input directories (somatic and germline). The patient and sample identifiers can also be explicitly set for DRAGEN data - in some instances this is required as these identifiers cannot be automatically inferred.

# DRAGEN input with named arguments
umccrise --dragen_somatic_dir PATH --dragen_germline_dir PATH -o umccrised/

# Explicitly setting subject identifier for provided DRAGEN input
umccrise --dragen_somatic_dir PATH --dragen_germline_dir PATH --dragen_subject_id IDENTIFIER -o umccrised/

Controlling the number of CPUs

To set the number of allowed CPUs to use, set the -j option:

umccrise <input-folder> -j30

Running selected stages

The umccrise workflow includes multiple processing stages, that can optionally be run in isolation. The following stages are run by default:

• conpair
• structural
• somatic, germline (part of small_variants)
• pcgr
• cpsr
• purple
• mosdepth, goleft, cacao (part of coverage)
• oncoviruses
• cancer_report
• multiqc

The following stages are optionally available and can be enabled with -T:

• microbiome
• immuno

Example:

# Run only multiqc and PCGR:
umccrise /bcbio/final/ -T multiqc -T pcgr

To exclude stages, use -E:

# Runs all default stages excluding `conpair` report for contamination and T/N concordance
umccrise /bcbio/final/ -E conpair

Custom input

umccrise supports bcbio-nextgen and DRAGEN projects as input. However, you can also feed custom files as multiple positional arguments. VCF and BAM files are supported. The sample name will be extracted from VCF and BAM headers. For now, the VCF file is assumed to contain T/N somatic small variant calls, and the BAM file is assumed to be from the tumor.

umccrise sample1.bam sample2.bam sample1.vcf.gz sample3.vcf.gz -o umccrised -j10

You can also provide a TSV file as input. If any input file has an extention .tsv (e.g. umccrise input.tsv) the file is assumed as a TSV file with a header, and any of the following columns in arbitrary order:

• sample
• wgs (WGS tumor BAM, required)
• normal (WGS normal BAM, required)
• exome (optional tumor BAM)
• exome_normal (optional normal BAM)
• rna (optional WTS BAM, however required for neoantigens)
• rna_bcbio (optional path to RNAseq bcbio workflow, required for neoantigens)
• rna_sample (sample name in the RNAseq bcbio workflow above, required for neoantigens)
• somatic_vcf (tumor/normal somatic VCF calls, optional. If not provided, SAGE will be run)
• germline_vcf (germline variant calls, optional)
• sv_vcf (SV calls, optional)

Running on selected samples

By default, umccrise will process all batches in the run in parallel. You can submit only certain samples/batches using -s/--sample arguments, e.g.:

# of all samples in a project, takes only sample1 and sample3, plus all corresponding normal/tumor matches:
umccrise /input/project/final -s sample1 -s sample3

Or you might want to exclude certain samples/batches with -e/--exclude:

# takes all samples in a project, excluding sample1 and sample2 and corresponding normal/tumor matches:
umccrise /input/project -e sample1 -e sample2

Updating

source load_umccrise.sh            # load the umccrise environment
cd umccrise ; git pull ; cd ..     # if the umccrise codebase changed

If dependencies changed:

conda activate miniconda/envs/umccrise
conda env update -f umccrise/envs/umccrise.yml -p miniconda/envs/umccrise
conda env update -f umccrise/envs/pcgr_linux.yml -p miniconda/envs/umccrise_pcgr
# conda env update -f umccrise/envs/pcgr_macos.yml -p miniconda/envs/umccrise_pcgr  # for macos
conda env update -f umccrise/envs/hmf.yml -p miniconda/envs/umccrise_hmf

Development

Changes pulled in umccrise repository clone folder will affect immidiately due to use of the -e option in pip install -e. To do the same for other related packages, you can clone them as well (or move already cloned repos from ./umccrise/envs/src, and run pip install -e on them as well:

source load_umccrise.sh
git clone https://github.com/vladsaveliev/NGS_Utils ngs_utils   ; pip install -e ngs_utils
git clone https://github.com/umccr/reference_data               ; pip install -e reference_data
git clone https://github.com/umccr/vcf_stuff                    ; pip install -e vcf_stuff

Docker

You can pull the ready-to-run docker image from DockerHub:

docker pull umccr/umccrise:latest

An example command to run umccrise on docker could be (although YMMV):

docker run -t --cpus 4 \
    -v=$PWD/umccrise_test_data/results/bcbio_test_project_docker:/output_dir \
    -v=$PWD/umccrise_test_data/data/bcbio_test_project:/bcbio_project \
    -v=/codebuild/output/refdata/genomes:/work/genomes \
    umccr/umccrise /bcbio_project -o /output_dir --genomes /work/genomes

This example assumes that:

1. You are running this umccrise container against the umccrise_test_data
2. You have figured out the genome data files and directory hierarchy for /work/genomes. See the building reference data section below.

Building reference data

To build the bundle from scratch, follow instructions for each kind of data below.

PURPLE

• Download hg19 and hg38 versions of the likely heterozygous sites for AMBER from the HMF website at https://resources.hartwigmedicalfoundation.nl/ -> HMFTools-Resources -> Amber3 (link valid as of May 2020)

mv GermlineHetPon.hg19.vcf.gz genomes/GRCh37/hmf
mv GermlineHetPon.hg38.vcf.gz genomes/hg38/hmf

• Download hg19 and hg38 versions of GC profile for COBALT from the HMF website at https://resources.hartwigmedicalfoundation.nl/ -> HMFTools-Resources -> Cobalt (link valid as of May 2020)

mv GC_profile.hg19.1000bp.cnp.gz genomes/GRCh37/hmf
mv GC_profile.hg38.1000bp.cnp.gz genomes/hg38/hmf

GNOMAD

Version 2.1 (latest, 500G, hosted by Broad Institute):

wget -c https://storage.googleapis.com/gnomad-public/release/2.1/vcf/genomes/gnomad.genomes.r2.1.sites.vcf.bgz | \
    # can optionally remove LCR becaues we annoatate vs LCR futher anyway, but the file will be already small enough:
    #bcftools filter -i 'FILTER="PASS" & segdup=0 & lcr=0 & decoy=0 & (AN_popmax>=500 & AF_popmax>=0.01 | \
    #AN_popmax>=100 & AF_popmax>=0.01)' gnomad.genomes.r2.1.sites.vcf.bgz -Ob | \
    bcftools annotate -x ID,^INFO/AN_popmax,^INFO/AF_popmax,FORMAT -Oz -o gnomad_genome.r2.1.common_pass_clean.vcf.gz
tabix -p vcf gnomad_genome.r2.1.common_pass_clean.vcf.gz

Normalise (see chapmanb/cloudbiolinux#279, however after all just 5 indels will be changed, so not a big deal):

ref=GRCh37.fa
norm_vcf gnomad_genome.r2.1.common_pass_clean.vcf.gz -o gnomad_genome.r2.1.common_pass_clean.norm.vcf.gz --ref-fasta $ref

Counts:

$ bcftools view gnomad_genome.r2.1.common_pass_clean.vcf.gz -H | wc    # with lcr&segdup&decoy
24671774

$ bcftools view gnomad_genome.common_pass_clean.vcf.gz -H | wc
21273673

Convert to hg38

# spartan
CrossMap.py vcf /data/cephfs/punim0010/extras/hg19ToHg38.over.chain.gz ../GRCh37/gnomad_genome.r2.1.common_pass_clean.norm.vcf.gz hg38.fa gnomad_genome.r2.1.common_pass_clean.norm.vcf.unsorted
bcftools view gnomad_genome.r2.1.common_pass_clean.norm.vcf.unsorted | bcftools sort -Oz -o gnomad_genome.r2.1.common_pass_clean.norm.vcf.gz
tabix -p vcf gnomad_genome.r2.1.common_pass_clean.norm.vcf.gz

PCGR

The PCGR data bundle gets refreshed every release, so please select the appropriate one from PCGR's README!

# Download the data bundles
pip install gdown  # or use  `$(pwd)/miniconda/envs/${ENV_NAME}_pcgr/bin/gdown`
gdown https://drive.google.com/uc?id=<GDOCS_ID_SEE_PCGR_DATABUNDLE_README> -O - | tar xvfz - # hg19
gdown https://drive.google.com/uc?id=<GDOCS_ID_SEE_PCGR_DATABUNDLE_README> -O - | tar xvfz - # hg38

# (Optional) if you are running on AWS, upload the PCGR data bundles to S3 like this:
gdown https://drive.google.com/uc?id=<GDOCS_ID_SEE_PCGR_DATABUNDLE_README> -O - | aws s3 cp - s3://umccr-umccrise-refdata-dev/Hsapiens/GRCh37/PCGR/pcgr.databundle.grch37.YYYMMDD.tgz
gdown https://drive.google.com/uc?id=<GDOCS_ID_SEE_PCGR_DATABUNDLE_README> -O - | aws s3 cp - s3://umccr-umccrise-refdata-dev/Hsapiens/hg38/PCGR/pcgr.databundle.grch38.YYYMMDD.tgz

Problem regions

Copy GRCh37 from bcbio-nextgen:

cp -r /g/data/gx8/local/development/bcbio/genomes/Hsapiens/GRCh37/coverage/problem_regions problem_regions

Generate SegDup:

cd problem_regions
wget http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/genomicSuperDups.txt.gz .
gunzip -c genomicSuperDups.txt.gz | cut -f2,3,4 >> segdup.bed_tmp
gunzip -c genomicSuperDups.txt.gz | cut -f8,9,10 >> segdup.bed_tmp
grep -v gl segdup.bed_tmp | sed 's/chr//' | bedtools sort -i - | bedtools merge -i - > segdup.bed
bgzip -f segdup.bed && tabix -f -p bed segdup.bed.gz
rm segdup.bed_tmp genomicSuperDups.txt.gz

Generate ENCODE:

wget https://www.encodeproject.org/files/ENCFF356LFX/@@download/ENCFF356LFX.bed.gz
gunzip -c ENCFF356LFX.bed.gz | bgzip -c > hg38/problem_regions/ENCODE/encode4_unified_blacklist.bed.gz
tabix -p bed hg38/problem_regions/ENCODE/encode4_unified_blacklist.bed.gz
rm ENCFF356LFX.bed.gz

Generate the regions for variant calling: hg38-noalt chromosomes excluding the ENCODE blacklist

bedtools subtract -a hg38_noalt.bed -b problem_regions/ENCODE/encode4_unified_blacklist.bed.gz > hg38_noalt_noBlacklist.bed

The blacklist removes ~2.3% of the noalt genome size:

$ bedsize hg38_noalt_noBlacklist.bed
3016716091

$ bedsize hg38_noalt.bed
3088286376

The blacklisted regions contain no hotspots:

bedtools intersect \
    -a problem_regions/ENCODE/encode4_unified_blacklist.bed.gz\
    -b hotspots/merged.vcf.gz

Lift over to hg38:

convert () {
    f=$(basename $1)
    echo "Processing $f"
    zless $1 | py -x "('chr' + x) if not x.startswith('MT') else 'chrM'" | grep -v chrG > $f.hg19
    CrossMap.py bed /g/data3/gx8/extras/hg19ToHg38.over.chain.gz $f.hg19 $f.unsorted
    bedtools sort -i $f.unsorted | bgzip -c > $f
    tabix -p vcf $f
}

convert ../../GRCh37/problem_regions/segdup.bed.gz

mkdir GA4GH repeats

cd GA4GH
for fp in $(ls ../../../GRCh37/problem_regions/GA4GH/*.bed.gz) ; do convert $fp ; done

cd ../repeats
convert ../../../GRCh37/problem_regions/repeats/LCR.bed.gz
convert ../../../GRCh37/problem_regions/repeats/polyx.bed.gz

cat ../../../GRCh37/problem_regions/repeats/sv_repeat_telomere_centromere.bed | py -x "('chr' + x) if not x.startswith('MT') else 'chrM'" | grep -v chrG > sv_repeat_telomere_centromere.bed_hg19
CrossMap.py bed /g/data3/gx8/extras/hg19ToHg38.over.chain.gz sv_repeat_telomere_centromere.bed_hg19 sv_repeat_telomere_centromere.bed_unsorted
bedtools sort -i sv_repeat_telomere_centromere.bed_unsorted | > sv_repeat_telomere_centromere.bed

Coding regions (SAGE)

cd GRCh37/hmf
generate_bed.py -g GRCh37 \
   --principal --key-genes --features CDS | sort -k1,1V -k2,2n | grep -v ^MT | grep -v ^GL \
   | bedtools merge -c 4 -o collapse -i - > coding_regions.bed

cd hg38/hmf
generate_bed.py -g hg38 \
   --principal --key-genes --features CDS | sort -k1,1V -k2,2n | grep -v ^chrM \
   | bedtools merge -c 4 -o collapse -i - \
   > coding_regions.bed

Ensembl annotation

Using pyensembl package:

# use ENSEMBL_VERSION=75 for GRCh37, ENSEMBL_VERSION=95 for hg38
export PYENSEMBL_CACHE_DIR=$ENSEMBL_DIR
if [ ! -d $PYENSEMBL_CACHE_DIR/pyensembl ] ; then
    # In 2 steps: first on loging node to make it download the files:
    pyensembl install --release $ENSEMBL_VERSION --species human
    # when it starts `Reading GTF from`, go into a worker node and run again.
fi

Hotspots

Combining Hartwig's and PCGR hotspots. Stats:

• Hartwig's: 10211 changes in 3650 locations,
• PCGR: 10627 changes in 2494 locations.
• Overlap: 2960 changes in 968 locations.

The overlap is small, so we better merge sources into a single VCF.

First, download HMF TSV file and convert to VCF:

wget https://nc.hartwigmedicalfoundation.nl/index.php/s/a8lgLsUrZI5gndd/download?path=%2FHMF-Pipeline-Resources&files=KnownHotspots.tsv.gz -O KnownHotspots.tsv.gz

echo "##fileformat=VCFv4.2" > hmf.vcf
echo '##INFO=<ID=HMF,Number=.,Type=Flag,Description="Hotspot is from HMF">' >> hmf.vcf
echo "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO" >> hmf.vcf
gunzip -c KnownHotspots.tsv.gz | py -x "print('\t'.join([x.split()[0], x.split()[1], '.', x.split()[2], x.split()[3], '.', '.', 'HMF']))" >> hmf.vcf
bgzip hmf.vcf
tabix -p vcf hmf.vcf.gz

Prepare PCGR hotspots:

SRC=/Users/vsaveliev/bio/genomes/pcgr/data/grch37/cancer_hotspots/cancer_hotspots.vcf.gz
bcftools view -h $SRC | grep ^## > pcgr.vcf
echo '##INFO=<ID=PCGR,Number=.,Type=Flag,Description="Hotspot is from PCGR (cancerhotspots.org_v2)">' >> pcgr.vcf
bcftools view -h $SRC | grep ^#CRHOM >> pcgr.vcf
bcftools view -H $SRC | bioawk -t '{ print $1,$2,$3,$4,$5,$6,$7,$8";PCGR" }' >> pcgr.vcf

bgzip pcgr.vcf
tabix -p vcf pcgr.vcf.gz

Merge:

bcftools merge -m none hmf.vcf.gz pcgr.vcf.gz -Oz -o merged.vcf.gz
tabix -p vcf merged.vcf.gz
# Adding into the workflows repo:
gunzip -c merged.vcf.gz | grep -v ^## > /Users/vsaveliev/git/umccr/workflows/genes/hotspots/hotspots.tsv

Convert to hg38

cd ../hg38/hotspots
INP=../../GRCh37/hotspots/merged.vcf.gz

gunzip -c $INP \
| py -x "x.replace('##contig=<ID=', '##contig=<ID=chr') if x.startswith('#') else 'chr' + x" \
| py -x "x.replace('chrMT', 'chrM')" \
| grep -v chrG \
| gzip -c > merged_hg19.vcf.gz

CrossMap.py vcf /g/data3/gx8/extras/hg19ToHg38.over.chain.gz merged_hg19.vcf.gz ../hg38.fa merged_unsorted.vcf
bcftools sort merged_unsorted.vcf -Oz -o merged.vcf.gz
tabix -p vcf merged.vcf.gz

Other HMF files

Download NA12878_GIAB_highconf_IllFB-IllGATKHC-CG-Ion-Solid_ALLCHROM_v3.2.2_highconf.bed.gz and out_150_hg19.mappability.bed.gz from https://resources.hartwigmedicalfoundation.nl/ -> HMFTools-Resources -> Sage (link valid as of May 2020)

To hg38:

convert ../../GRCh37/hmf/NA12878_GIAB_highconf_IllFB-IllGATKHC-CG-Ion-Solid_ALLCHROM_v3.2.2_highconf.bed.gz
convert ../../GRCh37/hmf/out_150_hg19.mappability.bed.gz

Fusions

We use HMF fusions for SV prioritization. See NGS_Utils/ngs_utils/refernece_data/__init__.py for details.

SnpEff

cd GRCh37
mkdir snpeff
cd snpeff
wget https://sourceforge.net/projects/snpeff/files/databases/v4_3/snpEff_v4_3_GRCh37.75.zip
unzip *.zip
rm *.zip

cd hg38
mkdir snpeff
cd snpeff
wget https://sourceforge.net/projects/snpeff/files/databases/v4_3/snpEff_v4_3_GRCh38.92.zip
unzip *.zip
rm *.zip

DVC

We use DVC to track reference data in the reference_data repo

git clone [email protected]:umccr/reference_data.git reference_data.git
cd reference_data.git

# The first time we did:
rsync -trv /g/data/gx8/extras/umccrise_genomes/hg38 genomes/
dvc init
dvc add genomes/hg38
# Alternatively, could used `run`:
#dvc run -n copy_hg38 -o genomes/hg38 rsync -trv /g/data/gx8/extras/umccrise_genomes/hg38 genomes/
dvc remote add -d storage s3://umccr-refdata-dev/dvc-storage
dvc push

# To pull the ref data, do:
dvc pull

# To update, do:
rsync -trv /g/data/gx8/extras/umccrise_genomes/hg38 genomes/
dvc add genomes/hg38
git add genomes/.gitignore genomes/hg38.dvc
dvc push