HapASeg

Haplotype-Aware Segmentation algorithm for estimating homologue-specific somatic copy number alterations

Installation

HapASeg requires several dependencies that can be complex to install. We provide a Docker build script, hapaseg_local/Dockerfile, to create a container image for running HapASeg locally. (Note: hapaseg_local/Dockerfile is distinct from the Dockerfile located in the root directory).

To build the hapaseg_local image, run the following command from the root of the repository:

docker build -f ./hapaseg_local/Dockerfile -t hapaseg_image .

Reference Files

HapASeg relies on several reference files. These can be downloaded automatically by executing:

docker run -v {workdir}:/workdir/ hapaseg_image hapaseg_local_install_ref_files --ref-build {ref_build} /workdir/ref_files/

• Replace {ref_build} with the reference genome build of your input BAMs (hg19, hg38, or both).
• Replace {workdir} with the local path where the reference files should be saved.

Note: By default, reference files are downloaded from Zenodo, which may be slow. To skip the download of large reference genome FASTA files, you can replace the file paths in hapaseg_local/ref_file_config.py with the paths to your local reference FASTA files.

Usage

Once the reference files are downloaded, HapASeg can be executed with the following command:

docker run -v {workdir}:/workdir/ hapaseg_image hapaseg_local [options]

Replace {workdir} with the local path where your reference files are stored. If your sample BAM files are located outside of the workdir, you can mount additional directories using extra -v flags. Use the --help flag to see all available run options.

Resource Allocation

hapaseg_local includes several subroutines that are amenable to parallelization. You can specify the maximum resources you would like HapASeg to use with the --max-cpus and --max-mem flags.

• Default: HapASeg will attempt to use all available resources.
• Minimum Requirement: The method requires at least 12GB of memory to run.

Publication

Details on the method and relevant benchmarking can be found in:

Priebe et al. 2026, Genome Biology. DOI: https://doi.org/10.1186/s13059-026-03971-w

Benchmarking source code for the publication is available in /benchmarking.

WolF

HapASeg is optimized for the wolF workflow management platform. The tasks and full workflow are located in /wolF.