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HowTo UNMASC

Outline

flowchart LR

tbam{{tumor.bam}} --> nbam1{{normal_1.bam}} & nbam2{{normal_2.bam}} --> caller{{Variant Caller}}
tbam{{tumor.bam}} --> dots{{...}} & nbamZ{{normal_Z.bam}} --> caller
fasta{{reference.fasta}} --> caller
caller --> GATK{{GATK MergeVcfs}} --> vcf1{{vc_1.vcf}} & vcf2{{vc_2.vcf}} & dotsv{{... .vcf}} & vcfZ{{vc_Z.vcf}}
cosmic{{COSMIC.vcf}} & vcf1 & vcf2 & dotsv & vcfZ & fasta --> VEP --> anno{{anno.vcf}}
vcf1 & vcf2 & dotsv & vcfZ & anno & targ{{target.bed}} --> prepU{{"prep_UNMASC_VCF()"}}
centro{{centromere.bed}} & dictchrom{{dict_chrom.txt}} & prepU --> UNMASC{{"run_UNMASC()"}}

%% class definitions
classDef myred fill:#f44336,stroke:#f3f6f4,stroke-width:2px
classDef myblue fill:#19daf8,stroke:#f3f6f4,stroke-width:2px
classDef mygreen fill:#79d50d,stroke:#f3f6f4,stroke-width:2px
classDef mymagenta fill:#fc9ffc,stroke:#f3f6f4,stroke-width:2px
classDef myyellow fill:#f6fa13,stroke:#f3f6f4,stroke-width:2px
classDef myorange fill:#f89d3e,stroke:#f3f6f4,stroke-width:2px

%% assign classes to nodes
class tbam myred
class nbam1,nbam2,dots,nbamZ myblue
class fasta mygreen
class vcf1,vcf2,dotsv,vcfZ,anno mymagenta
class cosmic,targ,centro,dictchrom myyellow
class caller,GATK,VEP,prepU,UNMASC myorange

%% clickable nodes
click GATK "https://github.com/broadinstitute/gatk"
click cosmic "https://github.com/pllittle/UNMASC/blob/main/workflow/inputs.md#get-cosmic-vcf"
click VEP "https://uswest.ensembl.org/info/docs/tools/vep/index.html"

Setting Directories and Variables

Click to expand!

Below are fixed variables to specify.

gatk_dir=; [ -z "$gatk_dir" ] \
	&& echo "Set gatk_dir, GATK directory" >&2 \
	&& return 1

git_dir=; [ -z "$git_dir" ] \
	&& echo "Set git_dir, location to store GitHub repos" >&2 \
	&& return 1
[ ! -d $git_dir ] && mkdir $git_dir

stk2_dir=; [ -z "$stk2_dir" ] \
	&& echo "Set stk2_dir, location of Strelka2 dir!" >&2 \
	&& return 1

vep_dir=; [ -z "$vep_dir" ] \
	&& echo "Set vep_dir, VEP directory" >&2 \
	&& return 1

vep_rel=; [ -z "$vep_rel" ] \
	&& echo "Set vep_rel, VEP release number, preferably 105 with GRCh37 and GRCh38 supported" >&2 \
	&& return 1

vep_cache=; [ -z "$vep_cache" ] \
	&& echo "Set vep_cache, VEP homo_sapiens cache, should be vep, refseq, or merged" >&2 \
	&& return 1

hts_dir=; [ -z "$hts_dir" ] \
	&& echo "Set hts_dir, the HTS directory" >&2 \
	&& return 1

cosm_dir=; [ -z "$cosm_dir" ] \
	&& echo "Set cosm_dir, directory containing COSMIC vcf" >&2 \
	&& return 1

cosm_ver; [ -z "$cosm_ver" ] \
	&& echo "Set cosm_ver, COSMIC version number, e.g. 95" >&2 \
	&& return 1

fasta_fn=; [ -z "$fasta_fn" ] \
	&& echo "Set fasta_fn, the reference FASTA" >&2 \
	&& return 1

nthreads=; [ -z "$nthreads" ] \
	&& echo "Set nthreads, number of threads or cores" >&2 \
	&& return 1

genome=; [ -z "$genome" ] \
	&& echo "Set genome, like GRCh37 or GRCh38" >&2 \
	&& return 1

Sample-specific Variables

out_dir=; [ -z "$out_dir" ] \
	&& echo "Set out_dir, output directory" >&2 \
	&& return 1

nbams=; [ -z "$nbams" ] \
	&& echo "Set nbams, file listing all normal bam full paths" >&2 \
	&& return 1
echo -e "Detected $(cat $nbams | wc -l) normal controls." >&2

tbam=; [ -z "$tbam" ] \
	&& echo "Set tbam, tumor bam full path" >&2 \
	&& return 1

Source scripts

Click to expand!

You are welcome to install your own programs and dependencies. I have provided below the steps and scripts I use to automate the programs related to UNMASC.

# Pull my functions

cd $git_dir
[ ! -d baSHic ] && git clone https://github.com/pllittle/baSHic.git >&2
[ -d baSHic ] && cd baSHic && git pull >&2

cd $git_dir
[ ! -d UNMASC ] && git clone https://github.com/pllittle/UNMASC.git >&2
[ -d UNMASC ] && cd UNMASC && git pull >&2

# Source functions

. $git_dir/baSHic/scripts/genomic.sh
[ ! $? -eq 0 ] && echo "Some error in sourcing genomic.sh" >&2 && return 1

. $git_dir/UNMASC/workflow/tumor_only.sh
[ ! $? -eq 0 ] && echo "Some error in sourcing tumor_only.sh" >&2 && return 1

Custom local installations

Click to expand!
  • gcc, libtool, perl, bzip2, xz, zlib, curl, expat, db,
  • HTSlib, VEP, Strelka2

One can control where these programs are installed by adding -a $apps_dir to the code below. apps_dir is just wherever you would like to install these programs.

install_gcc
install_libtool
install_perl
install_bzip2
install_xz
install_zlib
install_curl
install_expat
install_db
install_htslib
install_VEP -r $vep_rel
install_strelka2

If you rely on these functions for installations, these will determine hts_dir, stk2_dir, and vep_dir definitions.

Get COSMIC VCF

Click to expand!

Run the following code to obtain the appropriate COSMIC VCF. The function below will prompt the user to input COSMIC website's login and password. The generated file is needed for TO_workflow() below.

get_COSMIC_canonical -g $genome \
	-v $cosm_ver -h $hts_dir \
	-c $cosm_dir

The function definition is located here.

TO_workflow()

Click to expand!

Currently, TO_workflow is designed for Strelka2 and VEP. If others have alternate workflows, you are welcome to inspect this function's definitions to extract specific steps to execute 😄.

TO_workflow -c $nthreads -f $fasta_fn -g $genome \
	-d $cosm_dir -e $cosm_ver -h $hts_dir -k $gatk_dir \
	-n $nbams -o $out_dir -s $stk2_dir -t $tbam \
	-v $vep_dir -r $vep_rel -a $vep_cache

Preparing UNMASC's vcf

Click to expand!

Assuming UNMASC is successfully installed and the above TO_workflow() was run successfully, the instructions below describes the inputs to construct UNMASC's main input vcf. Otherwise, the user needs to construct vcf ensuring all required columns are available and formatted correctly (refer to ?UNMASC::run_UNMASC).

  • outdir String instructing where UNMASC outputs should be stored. Notice this is different from the Shell variable out_dir from above.
  • DAT A R data.frame containing FILENAME for full path to each control VCF and STUDYNUMBER to identify each normal control.
  • FILTER R list used to specify some liberal pre-filtering of loci based on total read depth and quality score.
  • target_fn String containing the full path to a target BED file with tab-delimited columns with headers Chr (e.g. chr1), Start (start position), and End (end position).
  • anno_fn String containing the full path to the annotated vcf file. If TO_workflow() was used, the R string anno_fn is $out_dir/allvar_ann.vcf.gz.
  • nlines Positive integer specifying how many lines into the anno_fn to initially read in to determine if its formatting matches what prep_UNMASC_VCF() is expecting.
  • ncores Positive integer specifying how many threads/cores are available. This is used here to reduce the computational time to import each control VCF. This argument may be more handy if samples underwent WGS or WES sequencing.
vcf = UNMASC::prep_UNMASC_VCF(
	outdir = outdir,
	DAT = DAT,
	FILTER = NULL,
	target_fn = target_fn,
	anno_fn = anno_fn,
	nlines = 100,
	ncores = 1)

Execution

Click to expand!

Arguments for run_UNMASC() with template inputs.

  • tumorID = "tumor01", tumor sample ID
  • outdir = file.path(".",tumorID), output location for the tumor sample
  • vcf = vcf, the data.frame generated by prep_UNMASC_VCF()
  • tBAM_fn = "path/to/tumor/bam"
  • bed_centromere_fn = "path/to/centromere/start/end/bed/file", tab-delimited file without headers with three columns containing contig, start position, and end position.
  • dict_chrom_fn = "path/to/chromosome/length/file", stored output from samtools view -H tumor.bam
  • qscore_thres = 30, Qscore threshold
  • exac_thres = 5e-3, gnomAD/ExAC population allele frequency threshold for germline filtering
  • ad_thres = 5, alternate depth threshold
  • rd_thres = 10, total depth threshold
  • cut_BAF = 5e-2, cutoff for variants to exclude before running segmentation
  • minBQ = 13, minimum base quality
  • minMQ = 40, minimum mapping quality
  • eps_thres = 0.5, noise mixture proportion threshold for determining a H2M segment
  • psi_thres = 0.02, over-dispersion of beta-binomial threshold for determining a H2M segment
  • hg = "19", labeling for output figures
  • binom = TRUE, set to TRUE to model read counts with binomial distribution. Set to FALSE to explore over both binomial and beta-binomial distributions
  • gender = NA, set to NA if gender is unknown. Otherwise set to "MALE" or "FEMALE"
  • ncores = 1, number of threads/cores available, aids with computational runtime in extracting strand-specific read counts per locus.
# Package main function
UNMASC::run_UNMASC(
	tumorID = tumorID,
	outdir = outdir,
	vcf = vcf,
	tBAM_fn = tBAM_fn,
	bed_centromere_fn = bed_centromere_fn,
	dict_chrom_fn = dict_chrom_fn,
	qscore_thres = qscore_thres,
	exac_thres = exac_thres,
	ad_thres = ad_thres,
	rd_thres = rd_thres,
	cut_BAF = cut_BAF,
	minBQ = minBQ,
	minMQ = minMQ,
	eps_thres = eps_thres,
	psi_thres = psi_thres,
	hg = hg,
	binom = binom,
	gender = gender,
	ncores = ncores)

Outputs

Click to expand!

Below are the main outputs from UNMASC to determine if all steps ran smoothly and to assess if the package or input files requires debugging. If any of these directories or files are missing, check the corresponding Rout file for any error or warning messages or flags for low quality sample information.

  • image.rds: Stores the comprehensive inputs for UNMASC. Once this file is created, run_UNMASC() skips past the time consuming pre-processing of input files. This image can be useful for re-producing results and inspecting errors. To have a clean restart or reset, first remove this file.
  • nCLUST: Figures of normal VAF clustering. If three clusters of normal VAF are not present, the loci supplied to UNMASC may have undergone pre-filtering of the normal VAF.
  • nSEG: Figures of normal VAF segmentation. This is useful for visualizing hard-to-map (H2M) regions and whether or not read counts should be modeled with a binomial or beta-binomial distribution.
  • tSEG: Figures of tumor VAF segmentation. This is useful for assessing the degree of sparsity when characterizing the local germline cluster behavior relative to each potential somatic locus. Also these figures may prove useful for inspecting copy number aberrations due to allelic imbalance (B allele frequencies deviating from 0.5).
  • tumor_genotype.tsv: Experimental output. Attempting to reverse-genotype an individual based on their tumor genomics. Loci are annotated with inferred genotypes, H2M status, strand bias, etc. to aid in isolating higher quality genotype calls. May be useful for performing genotype PCA or mapping NGS-based sample data to microarray sample data.
  • tumorOnly_VCs.tsv: UNMASC's tumor-only variant calls with comprehensive annotation contained in the LABEL column for prioritizing variants. Additional columns contain metrics used to construct the LABEL columns annotations such as strand bias, oxoG artifact, paraffin artifact, H2M status, germline-like loci, etc.