Augusta 1.0.7

Python package for inference of the gene regulatory network and the boolean network using RNA-Seq data.

Python package: From RNA-Seq to the Boolean Network through the Gene Regulatory Network

Documentation and tutorials are available at augusta.readthedocs.io.

News

Augusta is FAIR!

We are excited to announce that Augusta has undergone a successful FAIRification process. This process was part of the FAIR-IMPACT workshop, which aims to ensure that research software is Findable, Accessible, Interoperable, and Reusable (FAIR). You can read more about our FAIRification journey and how it enhances Augusta’s usability and collaboration potential in the FAIR-IMPACT Implementation Story: FAIRification of Augusta, Research Software for Gene Regulatory Networks and Boolean Models Inference | FAIR-IMPACT.

Credits

The Augusta project is based on research detailed in the following paper. Please cite this paper when using or referencing our work:

Augusta: From RNA‐Seq to gene regulatory networks and Boolean models. Jana Musilova, Zdenek Vafek, Bhanwar Lal Puniya, Ralf Zimmer, Tomas Helikar, and Karel Sedlar. Computational and Structural Biotechnology Journal, 2024. DOI: 10.1016/j.csbj.2024.01.013.

Contributors

• Jana Musilova, [e-mail]

• Zdenek Vafek

• Karel Sedlar, [e-mail]

Quick Guide

Dependencies:

• Python: version 3.7 or 3.8

• Docker

Installation:

We highly recomment installing and using Augusta in a virtual environment.

$ conda create -n Augusta_venv python=3.7 anaconda
$ conda activate Augusta_venv

$ pip install Augusta

Usage:

See Inputs for details about input files and variables.

$ python
>>> import Augusta

GRN and BN inference using RNA-Seq:

>>> Augusta.RNASeq_to_BN(count_table_input = 'MyCT_file.csv', promoter_length = My_number, genbank_file_input = 'MyGB_file.gb', normalization_type = 'My_string', motifs_max_time = My_seconds)

GRN inference using RNA-Seq:

>>> Augusta.RNASeq_to_GRN(count_table_input = 'MyCT_file.csv', promoter_length = My_number, genbank_file_input = 'MyGB_file.gb', normalization_type = 'My_string', motifs_max_time = My_seconds)

BN inference using GRN:

>>> Augusta.GRN_to_BN(GRN_input = 'MyGRN_file.csv', promoter_length = My_number, genbank_file_input = 'MyGB_file.gb', add_dbs_info = 'My_string')

GRN refinement:

>>> Augusta.refineGRN(GRN_input = 'MyGRN_file.csv', genbank_file_input = 'MyGB_file.gb', count_table_input = 'MyCT_file.csv', promoter_length = My_number, motifs_max_time = My_seconds)