# Inferelator 3.0
[](https://badge.fury.io/py/inferelator)
[](https://github.com/flatironinstitute/inferelator/actions/workflows/python-package.yml/)
[](https://codecov.io/gh/flatironinstitute/inferelator)
[](https://inferelator.readthedocs.io/en/latest/?badge=latest)
The [Inferelator 3.0](https://doi.org/10.1093/bioinformatics/btac117) is a package for gene regulatory network inference that is based on regularized regression.
It is an update of the [Inferelator 2.0](https://ieeexplore.ieee.org/document/5334018), which is an update of the original [Inferelator](https://doi.org/10.1186/gb-2006-7-5-r36)
It is maintained by the Bonneau lab in the [Systems Biology group of the Flatiron Institute](https://www.simonsfoundation.org/flatiron/center-for-computational-biology/systems-biology/).
This repository is the actively developed inferelator package for python. It works for both single-cell and bulk transcriptome experiments.
Includes [AMuSR](https://github.com/simonsfoundation/multitask_inferelator/tree/AMuSR/inferelator_ng)
[(Castro et al 2019)](https://doi.org/10.1371/journal.pcbi.1006591),
elements of [InfereCLaDR](https://github.com/simonsfoundation/inferelator_ng/tree/InfereCLaDR)
[(Tchourine et al 2018)](https://doi.org/10.1016/j.celrep.2018.03.048),
and single-cell workflows [(Jackson et al 2020)](https://elifesciences.org/articles/51254).
We recommend installing this package from PyPi using `python -m pip install inferelator`.
If running locally, also install `joblib` by `python -m pip install joblib` for parallelization.
If running on a cluster, also install `dask` by `python -m pip install dask[complete] dask_jobqueue` for dask-based parallelization.
This package can also be installed from the github repository.
Clone the [inferelator GitHub](https://github.com/flatironinstitute/inferelator) repository and run `python setup.py install`.
Documentation is available at [https://inferelator.readthedocs.io](https://inferelator.readthedocs.io/en/latest/), and
basic workflows for ***Bacillus subtilis*** and ***Saccharomyces cerevisiae*** are included with a tutorial.
All current example data and scripts are available from Zenodo
[](https://doi.org/10.5281/zenodo.3355524).
Raw data
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"description": "# Inferelator 3.0\n\n[](https://badge.fury.io/py/inferelator)\n[](https://github.com/flatironinstitute/inferelator/actions/workflows/python-package.yml/)\n[](https://codecov.io/gh/flatironinstitute/inferelator)\n[](https://inferelator.readthedocs.io/en/latest/?badge=latest)\n\nThe [Inferelator 3.0](https://doi.org/10.1093/bioinformatics/btac117) is a package for gene regulatory network inference that is based on regularized regression. \nIt is an update of the [Inferelator 2.0](https://ieeexplore.ieee.org/document/5334018), which is an update of the original [Inferelator](https://doi.org/10.1186/gb-2006-7-5-r36)\nIt is maintained by the Bonneau lab in the [Systems Biology group of the Flatiron Institute](https://www.simonsfoundation.org/flatiron/center-for-computational-biology/systems-biology/).\n\nThis repository is the actively developed inferelator package for python. It works for both single-cell and bulk transcriptome experiments.\nIncludes [AMuSR](https://github.com/simonsfoundation/multitask_inferelator/tree/AMuSR/inferelator_ng) \n[(Castro et al 2019)](https://doi.org/10.1371/journal.pcbi.1006591), \nelements of [InfereCLaDR](https://github.com/simonsfoundation/inferelator_ng/tree/InfereCLaDR) \n[(Tchourine et al 2018)](https://doi.org/10.1016/j.celrep.2018.03.048), \nand single-cell workflows [(Jackson et al 2020)](https://elifesciences.org/articles/51254).\n\nWe recommend installing this package from PyPi using `python -m pip install inferelator`. \nIf running locally, also install `joblib` by `python -m pip install joblib` for parallelization.\nIf running on a cluster, also install `dask` by `python -m pip install dask[complete] dask_jobqueue` for dask-based parallelization.\n\nThis package can also be installed from the github repository. \nClone the [inferelator GitHub](https://github.com/flatironinstitute/inferelator) repository and run `python setup.py install`.\n\nDocumentation is available at [https://inferelator.readthedocs.io](https://inferelator.readthedocs.io/en/latest/), and\nbasic workflows for ***Bacillus subtilis*** and ***Saccharomyces cerevisiae*** are included with a tutorial. \n\nAll current example data and scripts are available from Zenodo \n[](https://doi.org/10.5281/zenodo.3355524).\n",
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