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https://github.com/open2c/inspectro

Hi-C spectral analysis workflow
https://github.com/open2c/inspectro

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Hi-C spectral analysis workflow

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# Inspectro

Spectral decomposition for characterizing long-range interaction profiles in Hi-C maps.

Snakemake workflow for the unsupervised method presented in [Spracklin, Abdennur et al., 2022](https://www.nature.com/articles/s41594-022-00892-7).

```bibtex
@article{spracklin2022diverse,
title={Diverse silent chromatin states modulate genome compartmentalization and loop extrusion barriers},
author={Spracklin, George and Abdennur, Nezar and Imakaev, Maxim and Chowdhury, Neil and Pradhan, Sriharsa and Mirny, Leonid A and Dekker, Job},
journal={Nature Structural \& Molecular Biology},
pages={1--14},
year={2022},
publisher={Nature Publishing Group}
}
```

### Steps
0. Set up your environment with [conda](https://docs.conda.io/en/latest/miniconda.html) or [mamba](https://github.com/mamba-org/mamba):

```sh
conda env create -n spec -f environment.yml
conda activate spec
```

1. Place supplementary bigwig file information in `config/track_metadata.tsv` to include in graphical outputs. Must be tab-delimited, including header. Must have columns:

* `Name`: a display name
* `ID`: a unique identifier to use in the database (can be the same as `Name`)
* `FileFormat`: must be the string `bigWig`
* `Path`: a local path to the file

2. Edit `config.yaml`.

3. Run:

```sh
# Generate pre-aggregated supplementary bigwig tracks if provided.
$ snakemake make_track_db --force --cores all

# Run the pipeline
$ snakemake --cores all
```

## Releases

Versions `v0.1.*` will always follow the decomposition and clustering method as described in the NSMB paper, incorporating bug fixes and maintenance.

Future versions of the pipeline (`v0.2` and up) may include new additions and enhancements to the original method, which will be documented.