LiquidBayes

LiquidBayes is a Bayesian Network (BN) for inferring tumour fraction and clonal prevalences from whole genome sequencing of cell-free DNA (cfDNA) and Direct Library Preparation (DLP+) of a matched tissue biopsy.

Installation

Using pip:

pip install git+https://git@github.com/Roth-Lab/LiquidBayes.git

Usage

LiquidBayes offers 2 models: cn and cn_snv. The required parameters will differ depending on which model is used. Refer to help page by typing liquid-bayes --help in the terminal.

cn model

Required Parameters

• -i --input-path Path to liquid bam file
• -c --cn-profiles-path Path to file with the copy-number profiles for each clone (.bed format)
• -o --output Write output to this file (.csv format)
• --gc Path to gc content (.wig format)
• --mapp Path to the mappability (.wig format)

Example

liquid-bayes -i input.bam --gc hg38.gc.wig --mapp hg38.map.wig -c cn_profiles.bed -o results.csv -m cn_snv -n 2000 -w 200 -s 1 --progress-bar True --verbose True

cn_snv model

Required Parameters

All parameters in cn model plus:

• -l --liquid-vcf Path to liquid biopsy .vcf file (can be compressed in .gz file)
• -b --tissue-bams Path to clone .bam files (ex. ... -t path_to_clone_1 -t path_to_clone_2 -t path_to_clone_3 ...) - order of clones on the command line must be the same as in copy-number profiles (--cn-profiles-path)
• -t --tissue-vcfs Path to clone .vcf files (ex. ... -t path_to_clone_1 -t path_to_clone_2 -t path_to_clone_3 ...) - order of clones on the command line must be the same as in copy-number profiles (--cn-profiles-path)

Example

liquid-bayes -i input.bam --gc hg38.gc.wig --mapp hg38.map.wig -c cn_profiles.bed -o results.csv -l liquid.vcf.gz -b A.bam -b B.bam -t A.vcf.gz -t B.vcf.gz -m cn_snv -n 2000 -w 200 -s 1 --progress-bar True --verbose True