config_AML.yaml

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# Output
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output_dir: "results_AML"
n_reports: -1 # Number of top pyjacker hits for which pyjacker will generate a detailed report with figures. 
              # if -1, will generate figures for all putative enhancer hijacking events.
              # setting this to a lower value will reduce the runtime, since fewer (or even zero) figures will need to be generated.
image_format: "svg" # Format of the images generated in the report. Can be svg, png or pdf
image_dpi: 200 # Resolution of the images generated in the report, if the format is png

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# Required inputs
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RNA_TPM_file: "data/TPM_ckAML.tsv"
breakpoints: "data/breakpoints_ckAML.tsv"

gtf: data/Homo_sapiens.GRCh37.75.gtf.gz
cytobands: "data/cytobands_hg19.tsv"

#############################################
# Optional but strongly recommended inputs
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# Copy number alterations (used to correct for gene expression, and also add breakpoints if missed by the SV caller)
CNAs: "data/CNAs_ckAML.tsv"

# Directory containing the output of fast_ase (or GATK ASEReadcounter). This directory contains one tsv file per sample, called {sample}.tsv
ase_dir: "data/ASE_ckAML"

# Allele-specific expression will be ignored for imprinted genes, if they are provided.
imprinted_genes_file: "data/imprinted_genes.txt"

# pyjacker looks for SVs in the same TAD as a gene (+ a margin). 
# If not TADs are provided, it will look for SVs which are up to max_dist_bp2tss from the TSS.
TADs_file: "data/TADs_HSPC_hg19.bed"
#max_dist_bp2tss: 1500000 

# Provide enhancers scored by ROSE, for the correct cell type.
# This is optional, but if provided, it will be used to score the candidate enhancer hijacking events, in addition to outlier expression and allele-specific expression.
enhancers: "data/enhancers_myeloid_hg19.tsv"

##############################################
# Additional parameters
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# Provide fusion transcripts (used for annotating the results)
fusions: "data/fusions_ckAML.tsv"

# These weights influence the scoring of the candidate enhancer hijacking events, but their default values should work well.
weight_OHE: 4 # weight for the outlier high expression score
weight_ASE: 2 # weight for the allele specific expression
weight_enhancers: 1 # weight for the enhancers score
weight_deletion: 5 # weight for the deletion (penalize if a gene is deleted in a sample)

# Number of times to iterate through all genes when generating the null distribution for the false discovery rate.
# A smaller number of iterations is faster, but leads to an FDR which is not as precise.
n_iterations_FDR: 50

n_threads: 6 # Number of threads to use