<!--
Copyright (C) 2024 Roberto Rossini <roberros@uio.no>
SPDX-License-Identifier: MIT
-->
# StripePy
---
<!-- markdownlint-disable MD033 -->
<table>
<tr>
<td>Paper</td>
<td>
<a href="https://doi.org/10.1093/bioinformatics/btaf351">
<img src="https://img.shields.io/badge/CITE-Bioinformatics%20(2025)-blue" alt="Bioinformatics 2025">
</a>
</td>
</tr>
<tr>
<td>Downloads</td>
<td>
<a href="https://anaconda.org/bioconda/stripepy-hic">
<img src="https://img.shields.io/conda/vn/bioconda/stripepy-hic?label=bioconda&logo=Anaconda" alt="Bioconda">
</a>
 
<a href="https://pypi.org/project/stripepy-hic/">
<img src="https://img.shields.io/pypi/v/stripepy-hic" alt="PyPI">
</a>
 
<a href="https://doi.org/10.5281/zenodo.14394041">
<img src="https://zenodo.org/badge/DOI/10.5281/zenodo.14394042.svg" alt="Zenodo">
</a>
</td>
</tr>
<tr>
<tr>
<td>Documentation</td>
<td>
<a href="https://stripepy.readthedocs.io">
<img src="https://app.readthedocs.org/projects/stripepy/badge/?version=stable&style=flat" alt="Documentation">
</a>
</td>
</tr>
<td>CI</td>
<td>
<a href="https://github.com/paulsengroup/StripePy/actions/workflows/ci.yml">
<img src="https://github.com/paulsengroup/StripePy/actions/workflows/ci.yml/badge.svg" alt="Ubuntu CI Status">
</a>
 
<a href="https://github.com/paulsengroup/StripePy/actions/workflows/build-dockerfile.yml">
<img src="https://github.com/paulsengroup/StripePy/actions/workflows/build-dockerfile.yml/badge.svg" alt="Build Dockerfile Status">
</a>
</td>
</tr>
<tr>
<td>License</td>
<td>
<a href="https://github.com/paulsengroup/StripePy/blob/main/LICENCE">
<img src="https://img.shields.io/badge/license-MIT-green" alt="License">
</a>
</td>
</tr>
</table>
<!-- markdownlint-enable MD033 -->
---
StripePy is a CLI application written in Python that recognizes architectural stripes found in the interaction matrix files
generated by Chromosome Conformation Capture experiments, such as Hi-C and Micro-C.
StripePy is developed on Linux and macOS and is also tested on Windows. Installing StripePy is quick and easy using pip:
```bash
pip install 'stripepy-hic[all]'
```
For other installation options (conda, source, and Docker or Singularity/Apptainer), and details on ensuring StripePy is in your `PATH`, please refer to the official [documentation](https://stripepy.readthedocs.io).
## Why Choose StripePy?
StripePy stands out with several key features that make it a fast and robust stripe caller:
- **Broad Format Support**: Compatible with major formats: `.hic`, `.cool` and `.mcool`; outputs to `.hdf5` and `BEDPE`.
- **User-Friendly**: Designed with an intuitive command-line interface, making stripe analysis accessible even to less experienced users.
- **Stripe descriptors**: Computes stripe width, height, and generates various statistics for post-processing, e.g., ranking and filtering.
- **Optimized performance**: Outperforms other tools over diverse datasets and a simulated benchmark, StripeBench.
- **Exceptional speed & Low Memory**: Significantly faster than existing tools (2x Chromosight, 66x Stripenn), with much lower memory usage.
## Key Features
StripePy is organized into a few subcommands:
<!-- markdownlint-disable MD059 -->
- `stripepy download`: download a minified sample dataset suitable to quickly test StripePy - [link](https://stripepy.readthedocs.io/en/stable/downloading_sample_datasets.html).
- `stripepy call`: run the stripe detection algorithm and store the identified stripes in a `.hdf5` file - [link](https://stripepy.readthedocs.io/en/stable/detect_stripes.html).
- `stripepy view`: take the `result.hdf5` file generated by `stripepy call` and extract stripes in BEDPE format - [link](https://stripepy.readthedocs.io/en/stable/fetch_stripes.html).
- `stripepy plot`: generate various kinds of plots to inspect the stripes identified by `stripepy call`- [link](https://stripepy.readthedocs.io/en/stable/generate_plots.html).
<!-- markdownlint-enable MD059 -->
For a quick introduction to the tool, refer to the [Quickstart](https://stripepy.readthedocs.io/en/stable/quickstart.html) section in the documentation.
For more information on the subcommands, please run `stripepy --help` and refer to the [documentation](https://stripepy.readthedocs.io/en/stable/cli_reference.html) and the [paper](https://doi.org/10.1093/bioinformatics/btaf351).
## Getting help
For any issues regarding StripePy installation, walkthrough, and output interpretation please open a [discussion](https://github.com/paulsengroup/StripePy/discussions) on GitHub.
If you've found a bug or would like to suggest a new feature, please open a new [issue](https://github.com/paulsengroup/StripePy/issues) instead.
## Citing
If you use StripePy in your research, please cite the following publication:
Andrea Raffo, Roberto Rossini, Jonas Paulsen\
StripePy: fast and robust characterization of architectural stripes\
_Bioinformatics_, Volume 41, Issue 6, June 2025, btaf351\
[https://doi.org/10.1093/bioinformatics/btaf351](https://doi.org/10.1093/bioinformatics/btaf351)
<details>
<summary>BibTex</summary>
```bibtex
@article{stripepy,
author = {Raffo, Andrea and Rossini, Roberto and Paulsen, Jonas},
title = {{StripePy: fast and robust characterization of architectural stripes}},
journal = {Bioinformatics},
volume = {41},
number = {6},
pages = {btaf351},
year = {2025},
month = {06},
issn = {1367-4811},
doi = {10.1093/bioinformatics/btaf351},
url = {https://doi.org/10.1093/bioinformatics/btaf351},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/41/6/btaf351/63484367/btaf351.pdf},
}
```
</details>
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Installing StripePy is quick and easy using pip:\n\n```bash\npip install 'stripepy-hic[all]'\n```\n\nFor other installation options (conda, source, and Docker or Singularity/Apptainer), and details on ensuring StripePy is in your `PATH`, please refer to the official [documentation](https://stripepy.readthedocs.io).\n\n## Why Choose StripePy?\n\nStripePy stands out with several key features that make it a fast and robust stripe caller:\n\n- **Broad Format Support**: Compatible with major formats: `.hic`, `.cool` and `.mcool`; outputs to `.hdf5` and `BEDPE`.\n- **User-Friendly**: Designed with an intuitive command-line interface, making stripe analysis accessible even to less experienced users.\n- **Stripe descriptors**: Computes stripe width, height, and generates various statistics for post-processing, e.g., ranking and filtering.\n- **Optimized performance**: Outperforms other tools over diverse datasets and a simulated benchmark, StripeBench.\n- **Exceptional speed & Low Memory**: Significantly faster than existing tools (2x Chromosight, 66x Stripenn), with much lower memory usage.\n\n## Key Features\n\nStripePy is organized into a few subcommands:\n\n<!-- markdownlint-disable MD059 -->\n\n- `stripepy download`: download a minified sample dataset suitable to quickly test StripePy - [link](https://stripepy.readthedocs.io/en/stable/downloading_sample_datasets.html).\n- `stripepy call`: run the stripe detection algorithm and store the identified stripes in a `.hdf5` file - [link](https://stripepy.readthedocs.io/en/stable/detect_stripes.html).\n- `stripepy view`: take the `result.hdf5` file generated by `stripepy call` and extract stripes in BEDPE format - [link](https://stripepy.readthedocs.io/en/stable/fetch_stripes.html).\n- `stripepy plot`: generate various kinds of plots to inspect the stripes identified by `stripepy call`- [link](https://stripepy.readthedocs.io/en/stable/generate_plots.html).\n\n<!-- markdownlint-enable MD059 -->\n\nFor a quick introduction to the tool, refer to the [Quickstart](https://stripepy.readthedocs.io/en/stable/quickstart.html) section in the documentation.\n\n\n\nFor more information on the subcommands, please run `stripepy --help` and refer to the [documentation](https://stripepy.readthedocs.io/en/stable/cli_reference.html) and the [paper](https://doi.org/10.1093/bioinformatics/btaf351).\n\n## Getting help\n\nFor any issues regarding StripePy installation, walkthrough, and output interpretation please open a [discussion](https://github.com/paulsengroup/StripePy/discussions) on GitHub.\n\nIf you've found a bug or would like to suggest a new feature, please open a new [issue](https://github.com/paulsengroup/StripePy/issues) instead.\n\n## Citing\n\nIf you use StripePy in your research, please cite the following publication:\n\nAndrea Raffo, Roberto Rossini, Jonas Paulsen\\\nStripePy: fast and robust characterization of architectural stripes\\\n_Bioinformatics_, Volume 41, Issue 6, June 2025, btaf351\\\n[https://doi.org/10.1093/bioinformatics/btaf351](https://doi.org/10.1093/bioinformatics/btaf351)\n\n<details>\n<summary>BibTex</summary>\n\n```bibtex\n@article{stripepy,\n author = {Raffo, Andrea and Rossini, Roberto and Paulsen, Jonas},\n title = {{StripePy: fast and robust characterization of architectural stripes}},\n journal = {Bioinformatics},\n volume = {41},\n number = {6},\n pages = {btaf351},\n year = {2025},\n month = {06},\n issn = {1367-4811},\n doi = {10.1093/bioinformatics/btaf351},\n url = {https://doi.org/10.1093/bioinformatics/btaf351},\n eprint = {https://academic.oup.com/bioinformatics/article-pdf/41/6/btaf351/63484367/btaf351.pdf},\n}\n```\n\n</details>\n",
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