Config file options
Under construction
This page is still under construction and will contain more details in the future.
The config file has the following options: see comments for explanations. These defaults will always be used if not changed in the config file used during command invocation.
# Multiple arrays can be defined here, but arrays defined in global config saved in the cache are also available.
# This file will take precedence over the global config, unless the file names here can not be used.
# Each array needs all required entries, but the `stemcnv-check make-staticdata` command will generate files
# marked as auto-generatable. By default both the files and an update to a global array definition file will be
# written into the cache directory (unless --no-cache is used). By default this file is at
# ~/.cache/stemcnv-check/global_array_definitions.yaml
# Once the array definitions are in the global file, you need to either delete the 'array_definition' block here
# or also update it with the information written out by `stemcnv-check make-staticdata` (which is the same as the
# entry written into the global array definition config), since this config takes precedence over the global file.
# If no global config was used during the `make-staticdata` run, i.e. due to the --no-cache flag the array definitions
# will instead be written to a local file, i.e. 'ExampleArray_config.yaml' in the current working directory.
# In this case you will need to copy the contents of that file into this one, or alternatively into a global array
# definition file, that can still be created.
array_definition:
# This 'ExampleArray' *should* to be renamed to the actual array name
ExampleArray:
genome_version: hg38 #REQUIRED, options: hg38/GRCh38, hg19/GRCh37
# beadpool manifest file (.bpm) from Illumina, needs to match both the SNP array used and
# the desired genome version (usually filenames end with 'A1.bpm' for hg19 and 'A2.bpm' for hg38)
bpm_manifest_file: '' #REQUIRED
# cluster file (.egt) from Illumina, matching the SNP array used, independent of genome version
egt_cluster_file: '' #REQUIRED
# manifest file (.csv) from Illumina, matching the SNP array used and the genome version as .bpm
csv_manifest_file: '' #RECOMMENDED (can be left empty, but this will make most InDel probes unusable)
# PennCNV pfb file, describing the SNPs (derived from vcf/manifest files)
# defaults to: '{{cache}}/array_definitions/{{array_name}}/PennCNV-PFB_{{genome}}.pfb'
penncnv_pfb_file: '__cache-default__' #STATIC, Auto-generatable
# PennCNV GC model file, containing GC content values, calculated by PennCNV
# defaults to: '{{cache}}/array_definitions/{{array_name}}/PennCNV-GCmodel_{{genome}}.gcmodel'
penncnv_GCmodel_file: '__cache-default__' #STATIC, Auto-generatable
# bed file with windows of very high array density, calculated by stemcnv-check
# defaults to: '{{cache}}/array_definitions/{{array_name}}/density_{{genome}}.bed'
array_density_file: '__cache-default__' #STATIC, Auto-generatable
# bed file with windows of probes gaps on the array
# defaults to: '{{cache}}/array_definitions/{{array_name}}/gaps_{{genome}}.bed'
array_gaps_file: '__cache-default__' #STATIC, Auto-generatable
# Folder in which raw data files (.idat) can be found
# Important! idat files should be grouped in a subfolder per array-chip (sentrix_name)
raw_data_folder: '' #REQUIRED, Note: gencall has a hard time following links
# Output folder, where stemcnv-check will write results
data_path: data #REQUIRED
# Output folder, where stemcnv-check will write log files
log_path: logs #REQUIRED
evaluation_settings:
# All CNV calls are given a label based on their check score, filters and reference match.
# The labels described here are always available, but can be changed or new labels can be added
# If not other category fits (which should not occur with default settings),
# then the last defined "Exclude call" label will always be assigned
# Possible values for the "not_allowed_vcf_filters" list are: {vcf_filters}
CNV_call_labels:
Critical de-novo:
minimum_check_score: 55
not_allowed_vcf_filters: ['high_probe_dens', 'probe_gap', 'min_size', 'min_probes', 'min_density']
reference_match: FALSE
Reportable de-novo:
minimum_check_score: 55
not_allowed_vcf_filters: ['min_size', 'min_probes', 'min_density']
reference_match: FALSE
de-novo call:
minimum_check_score: 0
not_allowed_vcf_filters: ['min_size', 'min_probes', 'min_density']
reference_match: FALSE
Reference genotype:
minimum_check_score: 0
not_allowed_vcf_filters: []
reference_match: TRUE
Excluded call:
minimum_check_score: 0
not_allowed_vcf_filters: []
reference_match: FALSE
# Each sample QC measure defined in StemCNV-check is categorised as one of: {sample_labels_names}
# The last two categories are mutually exclusive, and the last one is only used for specific measures (defined by the 'use_last_level' list).
# In the report the color codes for the categories are: {sample_labels_values}
# For each of the sample QC measures, two thresholds for maximum values are defined:
# These determine the transition from the 1st to 2nd, or the 2nd to 3rd/last category.
# If both thresholds are the same value, the 2nd category is skipped and the 1st and 3rd/last are directly adjacent.
summary_stat_warning_levels:
call_rate: [0.99, 0.99] #Note: callrate uses *minimum* thresholds, not maximum
# SNP_pairwise_distance_to_reference is the absolute GT distance between a sample and it's reference
# Note that the expected baseline difference strongly depends on the array platform
# and may need to be adjusted. These values are based on the GSA array (~700k probes)
SNP_pairwise_distance_to_reference: [500, 5000]
loss_gain_log2ratio: [2, 4]
total_calls_CNV: [10, 50]
total_calls_LOH: [30, 75]
reportable_calls_CNV: [5, 10]
reportable_calls_LOH: [5, 10]
critical_calls_CNV: [1, 1]
critical_calls_LOH: [1, 1]
reportable_SNVs: [5, 10]
critical_SNVs: [1, 1]
# CNVs/LOHs gievn one these labels are not counted for QC measures
# Possible labels include the (default) CNV_call_labels defined above, as well as additional labels
# Default labels: {CNV_labels}
call_count_excl_labels: ['Excluded call'] # Fully ignore calls with any of these labels
# These measures use last QC category and are also bolded in the html summary table
use_last_level:
- call_rate
- computed_gender
- SNP_pairwise_distance_to_reference
- critical_SNVs
- critical_calls_CNV
- critical_calls_LOH
collate_output:
# xlsx or tsv output files can be generated
file_format: xlsx
# These columns from the sampletable will be included in the collated summary overview table
summary_extra_sampletable_cols:
- Reference_Sample
# Selection of CNVs for the summary table based on call labels
cnv_collate_call_selection:
# If defined, only CNVs with one of the "whitelist" call labels will be included
# If defined, no CNVs with one of the "blacklist" call labels will be included
# Possible labels include the (default) CNV_call_labels defined above, as well as additional labels
# Default labels: {CNV_labels}
whitelist_call_label: []
blacklist_call_label:
- Excluded call
global_settings:
# By default all conda environments and apptainer images are stored to a common cache
# This default location can also be overwritten by the '--cache-path' cmd-line flag or disabled by '--no-cache'
cache_dir: '~/.cache/stemcnv-check'
# Mehari transcript database file, either '__cache-default__' or a path for the bin.zst database file
# defaults to "{{cache_dir}}/mehari-db/mehari-data-txs-{{genome}}-ensembl-{mehari_db_version}.bin.zst
hg19_mehari_transcript_db: '__cache-default__'
hg38_mehari_transcript_db: '__cache-default__'
# Dosage sensitivity predicitions, as described in Collins et. al. 2022 (doi:10.1016/j.cell.2022.06.036)
# Either '__cache-default__' or a path to the dosage sensitivity data file
# defaults to "{{cache_dir}}/Collins_rCNV_2022.dosage_sensitivity_scores.tsv.gz"
dosage_sensitivity_scores: '__cache-default__'
# Fasta file for the genome sequence, either '__default-ensemble__' or a path to the genome fasta file
# '__default-ensemble__' will download the genome fasta file from ensembl ftp servers
# Note: fasta files can be compressed, but *only* with bgzip!
# defaults to "{{cache_dir}}/fasta/homo_sapiens/{ensembl_release}_{{genome}}/Homo_sapiens.{{genome}}.dna.primary_assembly.fa.gz"
hg19_genome_fasta: '__default-ensemble__'
hg38_genome_fasta: '__default-ensemble__'
# Gene annotation of the genome in gtf format, either '__default-gencode__' (Gencode v45 files) or a path to the gtf file
# defaults to "{{cache_dir}}/static-data/gencode.{{genome}}.v45.gtf.gz"
hg19_gtf_file: '__default-gencode__'
hg38_gtf_file: '__default-gencode__'
# tabular files with chromosome and gband details, derived from UCSC information via make-staticdata
# defaults to "{{cache_dir}}/static-data/UCSC_{{genome}}_chromosome-info.tsv"
hg19_genomeInfo_file: '__default-UCSC__'
hg38_genomeInfo_file: '__default-UCSC__'
settings:
# Select tools to use
# Currently implemented tools (=valid options): PennCNV, CBS
CNV.calling.tools:
- PennCNV
- CBS
probe_filter_sets:
# Each section here defines a set of SNP probe filters
# each set can be applied to individual or all steps of the pipeline, but using only one set is recommended
# SNP probes filters are applied as (soft) filters to the SNP vcf file.
# - GenTrainScore: Illumina score on clustering on probe intensities, usually stable between samples (& partially chips)
# - GenCallScore: Illumina score on Genotype call reliability, usually somewhat stable between samples
# - Position.duplicates: many SNP arrays have some genomic positions covered with multiple probes. Multiple data
# points at the same position are problematic for CNV calling due to signal/noise issues.
# These probes can all be kept, all removed, or a single probe per position with highest GC|GT can be kept
# - Pseudoautosomal: Handling probes in the pseudo-autosomal (PAR1, PAR2) and X-translocated (XTR) regions on the X and Y chromosomes
# These regions are identical or very similar between X and Y and always behave as if diploid,
# which can cause issues on haploid male samples. They can also be generally more problematic to interpret.
# Additionally:
# - SNPs on the Y chromosome are always (soft)filtered for female samples
# - SNPs without properly defined REF & ALT alleles are hard-filtered (i.e. removed from the vcf).
# The latter mainly occurs if the manifest csv is omitted, which causes Indel-probes to be improperly defined.
#
# We recommend to use these filter settings:
standard:
GenTrainScore: 0.15
GenCallScore: 0.15
Position.duplicates: highest-GenCall # keep|remove|highest-GenCall|highest-GenTrain
Pseudoautosomal: remove-male # keep|remove|remove-male
# Default filter set to use for all tools
default_probe_filter_set: standard
PennCNV:
# Specific probe filter set for PennCNV, '_default_' uses `default_probe_filter_set
probe_filter_settings: '_default_'
enable_LOH_calls: True
# Neighbouring CNVs of the same state that are merged if
# a) the gap between them is <= 'merge.gap.absolute' [bp] or <= 'merge.gap.snps' [SNPs] or if
# b) they would touch/overlap after increasing their size each by 'call.extension.percent' [%]
# Any chain of neighbouring CNVs meeting these conditions becomes a single call
call.merging:
merge.gap.absolute: 500
merge.gap.snps: 10
call.extension.percent: 60
maximum.gap.allowed: 500000
# vcf filters / CNV call filters are applied to calls (after merging of nearby calls) as follows:
# [snps] >= min.snp & [length] >= min.length & [density, snps/Mb] >= min.snp.density
filter.minprobes: 5
filter.minlength: 1000
filter.mindensity.Mb: 10 #snps per Mb
CBS:
# Specific probe filter set for CBS, '_default_' uses `default_probe_filter_set
probe_filter_settings: '_default_'
# undo.SD split value for CBS
undo.SD.val: 1
# Neighbouring CNVs of the same state that are merged if
# a) the gap between them is <= 'merge.distance' [bp] or <= 'merge.gap.snps' [SNPs] or if
# b) they would touch/overlap after increasing their size each by 'call.extension.percent' [%]
# Any chain of neighbouring CNVs meeting these conditions becomes a single call
call.merging:
merge.gap.absolute: 500
merge.gap.snps: 10
call.extension.percent: 60
maximum.gap.allowed: 500000
# vcf filters / CNV call filters are applied to calls (after merging of nearby calls) as follows:
# [snps] >= min.snp & [length] >= min.length & [density, snps/Mb] >= min.snp.density
filter.minprobes: 5
filter.minlength: 1000
filter.mindensity.Mb: 10 #snps per Mb
# LRR thresholds for identifying CBS segments as gain/loss on autosomes
LRR.loss: -0.25 #CN1
LRR.loss.large: -1.1 #CN0
LRR.gain: 0.2 #CN3
LRR.gain.large: 0.75 #CN4+
# LRR thresholds for sex chromosomes
LRR.male.XorY.loss: -0.5 #CN0
LRR.male.XorY.gain: 0.28 #CN2
LRR.male.XorY.gain.large: 0.75 #CN3+
LRR.female.X.loss: -0.05 #CN1
LRR.female.XX.loss: -0.9 #CN0
LRR.female.X.gain: 0.5 #CN3
LRR.female.X.gain.large: 1.05 #CN4+
# Values used by `stemcnv-check make-staticdata` to generate density and gap bed files
array_attribute_summary:
density.windows: 100000 #window size for probe density calculation (100kb)
min.gap.size: 'auto-array' #minimum distance between 2 probes to be considered a gap. Number or 'auto-array'
CNV_processing:
call_processing:
# SNP probe counts may change with merging of calls from different tools
# therefore a single probe_filter_settings needs to be used as reference here
probe_filter_settings: '_default_'
# Prefiltering of calls is done (after merging of nearby calls) as follows:
# vcf filters / CNV call filters are applied to calls (after merging of nearby calls) as follows:
filter.minprobes: 5
filter.minlength: 1000
filter.mindensity.Mb: 10 #snps per Mb
## Calls from multiple tools are combined if they match
# This is the minimum coverage the largest single call in an combined group needs to have.
# keep this >=50 to prevent formation/acceptance of chains of overlapping calls
tool.overlap.greatest.call.min.perc: 50
# This is minimum for the median of coverage percentages from all tool in any merged group
tool.overlap.min.cov.sum.perc: 60
## Reference comparison
min.reciprocal.coverage.with.ref: 50
## Probe gap flagging of calls
# Values to determine 'call_has_probe_gap' based on coverage percentage with gap areas (from array attribute
# summary) and log2 number of unique probe positions. The two values represent slope and intercept of
# slope * percent_gap_area + gap_intercept ~ log2(uniq_snp_positions), calls above that line "have gaps"
# These defaults mean that calls with larger % gap area need fewer unique probes to be flagged as "having a gap"
# Specifically, calls with 33% gap need >=373 probes, 50% >=91 probes, 75% >= 12 probes, 85% >= 5 probes to be flagged
gap_area.uniq_probes.rel: [-12, 12.5] # slope, intercept
min.perc.gap_area: 0.33
## HighDensity flagging of calls
# Calls that have a probe density which is higher than the top {{density.quantile.cutoff}} [%] of the array windows
# (calculated from array attribute summary) are flagged as having "high SNP density"
density.quantile.cutoff: 0.99
gene_overlap:
# These options determine which genes are read from the gtf file
exclude_gene_type_regex: []
# Example: ['artifact', 'IG_.*', 'TR_.*', '(un|_)processed_pseudogene']
include_only_these_gene_types: ['lncRNA', 'miRNA', 'protein_coding']
whitelist_hotspot_genes: True
# These genelists are used to mark genes with high impact
# Gene lists files are tabular (tsv) and need the following columns:
# list_name, hotspot, mapping, call_type, check_score, description, description_doi
# list_name, hotspot, mapping, call_type & check_score need to be filled out
# description & description_doi will be used to display extra info in the report
# mapping can be 'gene_name', 'gband', and 'position' and should describe the hotspot
# call_type can be 'any', 'gain', 'loss' or 'LOH'
stemcell_hotspot_list: '__inbuilt__/supplemental-files/genelist-stemcell-hotspots.tsv'
cancer_gene_list: '__inbuilt__/supplemental-files/genelist-cancer-drivers.tsv'
# also available: '__inbuilt__/supplemental-files/genelist-cancer-hotspots.tsv'
# File path for dosage sensitivity score file is defined in global_settings
dosage_sensitive_gene_name_fixes: '__inbuilt__/supplemental-files/gene-names-mapping-dosage-sensitivity.tsv'
# Scoring for CNV and LOH calls
# scoring combines a Size based contribution with scores for overlapping annotated regions
Check_score_values:
# stemcell_hotspot & cancer_gene scores need to be defined in the respective tables
# CNVs/LOHs get the summed scored of each overlapping annotated gene or region (gband/position)
# genes are only scored _once_ per call, i.e. a gene with both stemcell_hotspot and cancer_gene match will only
# contribute the higher of the two annotated scores.
# Dosage sensivity predicition is a based on Collins et. al. 2022 (doi:10.1016/j.cell.2022.06.036)
# CNV loss calls overlapping a gene with pHaplo score >= threshold are scored with the 'dosage_sensitive_gene' score
# CNV gain calls are respectively scored for the pTriplo score
pHaplo_threshold: 0.86
pTriplo_threshold: 0.94
dosage_sensitive_gene: 5
# Genes without any score from the hotspot lists or dosage sensivity are scored as 'any_other_gene'
any_other_gene: 0.2
# These values determine how the base Check-Score is calculated from size & CN.
# The formula used is: copy_factor * log(size) * log(size) - flat_decrease
# The copy_factor changes based on the CN of the call (number of lost/gained copies)
# copy_factor for CN 1 and 3
single_copy_factor: 0.333
# copy_factor for CN 0 and 4
double_copy_factor: 0.5
# copy_factor for CN 2 (LOH)
neutral_copy_factor: 0.275
flat_decrease: 15
# Note: male sex chromosomes have baseline CN=1, and generally use the 1-copy factor unless CN>2
# This file contains precision estimations based on benchmarking data
precision_estimation_file: '__inbuilt__/supplemental-files/precision_estimates.tsv'
SNV_analysis:
probe_filter_settings: "_default_" # "_default_", Filterset name, or "none". Note: None will even include chrY on female samples
snv_hotspot_table: '__inbuilt__/supplemental-files/SNV-stemcell-hotspots.tsv'
flag_GenCall_minimum: 0.2
# Only variants matching at least one of the following criteria are included in SNV analysis
# This means that i.e. intron or synonymous variants in SNV hotspot genes are NOT included in the output files
# Underlying annotations are derived from mehari, specifically from the terms defined by http://www.sequenceontology.org
variant_selection:
Impact: [HIGH, MODERATE]
Annotation_regex: ~
# This will include ALL variants in any ROI region, regardless of annotation
include_all_ROI_overlaps: TRUE
# List of SNV categories that are considered critical or reportable.
# Allowed values: {SNV_category_labels}
critical_SNV:
- 'hotspot-match'
reportable_SNV:
- 'hotspot-gene'
- 'protein-ablation'
# SNVs that can fully remove protein function are summarised in the "protein-ablation" category (generally HIGH impact)
protein_ablation_annotations:
# The 'HIGH' impact category generally contains these variant annotations/groups:
# - stop_gained
# - start_lost
# - stop_lost
# - frameshift_variant
# - splice_acceptor_variant
# - splice_donor_variant
Impact: ['HIGH']
Annotation_regex: ~
# SNVs impacting protein sequence, but not generally removing protein function are summarised as "protein-changing"
protein_change_annotations:
Impact: []
# The missense_variant and (conservative|disruptive)_inframe_(deletion|insertion) annotations are in the 'MODERATE' impact category
Annotation_regex: 'missense_variant|inframe'
# These settings determine which samples are used for the SNP clustering & dendrogram
SNP_clustering:
# Sample-IDs from the sample table, these will be added to the clustering of every sample
sample_ids: []
# Column names of the sample table, these are assumed to contain (comma separated) Sample-IDs
id_columns: []
# Column names of the sample table, Samples are used for clustering if they have the same value in any of these columns
match_columns: ['Chip_Name', 'Sample_Group']
# Maximum number of samples to include in the dendrogram. Note: calculation of clustering (done per sample)
# takes more time for each additional sample included
max_number_samples: 20
vcf_output:
# Which chromosome style to use in the vcf file ("1" vs "chr1")
chrom_style: 'UCSC' # "keep-original", UCSC, or NCBI / Ensembl
reports:
# Any number of reports can be defined, the default is 'StemCNV-check-report'
# All reports inherit from the default settings, but can overwrite specific parts
StemCNV-check-report:
file_type: 'html' #REQUIRED
# Any number of reports can be defined, the default is 'StemCNV-check-report'.
# file_type (html or pdf) needs to be defined for each one.
# Note: report generation is optimised for html format, and pdf reports may have issues, especially with larger tables
#
# StemCNV-check-full-report:
# file_type: 'html' #REQUIRED
# call.data.and.plots:
# _default_:
# # How many plots to show at least, this strongly influences the filesize of the report
# min_number_plots: 100
# include.gene.table.details: 'All'
# # These reduced settings works reasonably well for pdf
# StemCNV-check-report-pdf:
# file_type: 'pdf' #REQUIRED
# exclude_sections: [ QC.settings, QC.PennCNV, QC.CBS, QC.GenCall ]
# call.data.and.plots:
# include.call.table: FALSE
#These are the default settings from which all reports inherit
_default_:
# individual sections can be included (whitelist) or excluded (blacklist) from report.
# Default is special '__all__' for include, but a list of specific sections can also be used
include_sections: '__all__'
exclude_sections: []
# Availbale sections (Note that tool specific ones also depend on pipeline settings):
# {report_sections}
# (Additional) List of columns from the sample_table that are included in the "Sample Information" table
sample.info.extra.cols: ['Chip_Name', 'Chip_Pos']
# CNV calls can, based on the assigned call label, be:
# - fully removed from the report, incl all tables and plots (this option)
# - selected for the de-novo & reference genotype CNV tables (following section)
# - selected for the genome_overview plots (last section)
# Possible labels include the (default) CNV_call_labels defined above, as well as additional labels
# Default labels: {CNV_labels}
# Call labels for the 'de-novo CNV calls' table
CNV_call_labels_removed:
- 'Excluded call'
call.data.and.plots:
# Default and specific settings for each section of plots (denovo, reference_gt, regions_of_interest)
# The specific sections inherit from the default, but can overwrite all or individual values
_default_: &default_plot_settings
# How many plots to fully incorporate into the report at minimum
# Note: Plots are still generated for all CNV calls, but any exceeding this number will only be saved
# separate from the html report and linked from there. Increasing this number increases the report file size.
min_number_plots: 20
# Calls with one of these call lables will be included regardless of the minimum number
always_include_CNVs: []
# Include plots, table of individual calls and table of genes
include.plot: True
include.hotspot.table: True
include.gene.table.details: 'Call' # Choice of: None|Call|All
# Minimum relative size of (each) flanking region compared to call
plot.flanking.region.relative: 2
# Minimum size of total plot region
plot.region.minsize: 2000000
denovo:
<<: *default_plot_settings
# Call labels for the 'de-novo CNV calls' table
call_labels_include:
- 'Critical de-novo'
- 'Reportable de-novo'
- 'de-novo call'
always_include_CNVs:
- 'Critical de-novo'
- 'Reportable de-novo'
reference_gt:
<<: *default_plot_settings
# Call labels for the 'Reference genotype CNV calls' table
call_labels_include:
- Reference genotype
regions_of_interest:
<<: *default_plot_settings
plot.region.minsize: 100000
# # Report settings for the SNV analysis block
# SNV_analysis:
# # Which critical SNV reasons should use red (instead of orange) highlights
# SNV_categories_with_red_highlight:
# - 'ROI-match'
# - 'hotspot-match'
# Settings for the Sample comparison / SNP dendrogram sections
SNP_comparison:
# Selection of sample table columns, to determine shape and color of the samples in the dendrogram.
# Note: You can also use any column from the sample table, incl optional ones you added yourself
dendrogram.color.by: 'Chip_Name'
dendrogram.shape.by: 'Sample_Group'
genome_overview:
# Call labels for the overview plots
call_labels_overview:
- 'Critical de-novo'
- 'Reportable de-novo'
- 'de-novo call'
- 'Reference genotype'
# Include the reference sample in the genome overview plots
show_reference: True
# These constraints define which sample_ids, sentrix_pos (Chip_Pos) and sentrix_name (Chip_Name) are valid
# Edit at your own risk!: if sample_ids to not match this constraint, they will not be run and errors might not be intuitive
wildcard_constraints:
sample_id: "[a-zA-Z0-9-_]+"
sentrix_pos: 'R[0-9]{2}C[0-9]{2}'
sentrix_name: '[0-9]+'
# These settings are used to define the resources snakemake allocates for each tool
tools:
_default_:
threads: 1
memory: 2000 # "2000MB"
runtime: "1h"
partition: 'medium'
GenCall:
threads: 4
memory: 8000 # "8000MB"
runtime: "4h"
# gtc2vcf:
# memory: 1000 # "2000MB"
# runtime: "1h"
# filter_snp_vcf:
# memory: 1000 # "2000MB"
# runtime: "1h"
# mehari:
# memory: 1000 # "2000MB"
# runtime: "1h"
CBS:
memory: 4000 # "4000MB"
runtime: "30m"
CNV.process:
memory: 4000 # "4000MB"
runtime: "30m"
PennCNV:
memory: 1000 # "500MB"
runtime: "30m"
SNV_analysis:
threads: 2
memory: 20000 # "2000MB"
runtime: "4h"
knitr:
memory: 10000 # "10000MB"
runtime: "1h"