Metadata-Version: 2.4
Name: sigma2
Version: 0.0.5a0
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Dist: pandas>=1.3.0
Requires-Dist: numpy>=1.20.0
Requires-Dist: scipy>=1.7.0
Requires-Dist: pyranges>=0.0.68
Requires-Dist: tqdm>=4.60.0
Requires-Dist: pyyaml>=5.4.0
Requires-Dist: appdirs>=1.4.0
Requires-Dist: h5py>=3.0
Requires-Dist: joblib>=1.0
Requires-Dist: scikit-learn>=1.0
Requires-Dist: tabulate>=0.8
Requires-Dist: pysam>=0.19
Requires-Dist: matplotlib>=3.5
Requires-Dist: seaborn>=0.12
Requires-Dist: kneed>=0.8
Requires-Dist: psutil>=5.8
Requires-Dist: shap>=0.41
Requires-Dist: ipython>=8.0 ; extra == 'notebook'
Requires-Dist: pybedtools>=0.9 ; extra == 'panel'
Provides-Extra: notebook
Provides-Extra: panel
License-File: LICENSE
Summary: Mutational signature analysis for low statistics SNV data
Keywords: bioinformatics,genomics,mutational-signatures,cancer
Author-email: Jan P Hummel <jan_hummel@hms.harvard.edu>, Doga C Gülhan <dgulhan@mgh.harvard.edu>
Requires-Python: >=3.9, <3.13
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Documentation, https://compbio.hms.harvard.edu/sigma/docs/category/python-package
Project-URL: Homepage, https://compbio.hms.harvard.edu/sigma
Project-URL: Repository, https://github.com/sigscape/SigMA2

Copyright (c) 2019-2025 - President and Fellows of Harvard College. All rights reserved.

SigMA2 - Mutational Signature Analysis in Python
=================================================

SigMA2 is the Python version of [SigMA](https://github.com/parklab/SigMA), a tool developed to detect *mutational Signature 3*, which is associated with homologous recombination (HR) deficiency. It supports hybrid capture panels, whole-exome, and whole-genome sequencing (WGS) data. While traditional signature analysis tools struggle with data containing few single nucleotide variants (SNVs), SigMA2 is designed to perform well in such settings.

![Image description](https://github.com/parklab/SigMA/blob/master/shiny/www/workflow.png)

## Installation

### From PyPI (Recommended)

SigMA2 is currently an **alpha pre-release**, so `--pre` is required:

```bash
pip install --pre sigma2
```

Requires Python 3.9-3.12. The upper bound comes from a transitive dependency
(`pyranges` -> `ncls`), which publishes no wheels for 3.13 or newer.

One dependency, `sorted-nearest`, is source-only on every Python version and is
compiled during install, so a working C toolchain is needed (`build-essential`
on Debian/Ubuntu, `base-devel` on Arch, Xcode command line tools on macOS).

### From Source

```bash
git clone https://github.com/sigscape/SigMA2.git
conda env create -f ./SigMA2/environment.yml
conda activate sigmapy310
pip install -e ./SigMA2
```

## Reference data

The package itself ships everything a run needs in the way of models: the data
store, the SigMA v1 classifiers, and the panel definitions. Bulk reference data
(germline VCFs and repeat regions, ~1.4 GB) is downloaded on demand instead:

```bash
sigma2 data list          # what exists and what is installed
sigma2 data fetch --all   # download everything
sigma2 data verify        # re-check checksums
sigma2 info               # versions, data directory, disk usage
```

Files land in a per-user data directory; set `$SIGMA2_DATA_DIR` to override it,
which is the usual choice on a cluster where one pre-seeded copy is shared.

Reference genomes are separate and larger (~3 GB each): `sigma2 data genome hg19`.

> **Access note.** Until the accompanying paper is published, the bulk data
> lives in a private repository. `sigma2 data fetch` therefore needs GitHub
> credentials with access to it — either run `gh auth login`, or set
> `SIGMA2_GITHUB_TOKEN`. Without access the fetch fails with a 404; the code
> itself is fully functional, and any data you already hold can be used by
> pointing `$SIGMA2_DATA_DIR` at it. Please contact the authors (below) to
> request access.

## Quick Start
Follow [these Jupyter Notebooks](examples) in numerical order to get started with SigMA2 and its built-in functions!

## Documentation & Further Information
Find more [here](https://compbio.hms.harvard.edu/sigma)

## Contact

Requests for use of the Software for or on behalf of for-profit entities or for any commercial purposes, please contact:

Office of Technology Development
Harvard University
Smith Campus Center, Suite 727E
1350 Massachusetts Avenue
Cambridge, MA 02138 USA
Telephone: (617) 495-3067
E-mail: otd@harvard.edu

## Citation

If you use SigMA2 in your work, please cite the original SigMA publication as follows:

> Detecting the mutational signature of homologous recombination deficiency in clinical samples `<br/>`
> Doga C. Gulhan, Jake June-Koo Lee, Giorgio E. M. Melloni, Isidro Cortés-Ciriano & Peter J. Park `<br/>`
> Nature Genetics (2019)`<br/>`
> [https://doi.org/10.1038/s41588-019-0390-2](https://doi.org/10.1038/s41588-019-0390-2)

