Update README.md & added a TODO

README.md

@@ -2,12 +2,41 @@
 
 GRPhIN (Graphlet Characterization of Regulatory and Physical Interaction Networks) is an algorithm for counting graphlets and the specific node positions within each graphlet (called orbits) in mixed regulatory and physical interaction networks. Graph representions of regulatory or physical interactions in isolation may obscure the complete functional context of a protein. PPI networks and GRNs do not exist separately; proteins are transcription factors, genes encode proteins, and physical and regulatory interactions mix and coexist forming their own distinct patterns. Graphlets are small, connected, induced subnetworks that describe patterns, local topologies, and organization in networks.
 
+GRPhIN will be presented at Great Lakes Boinformatics Conference 2025. A preprint is available on bioRxiv:
+
+[GRPhIN: Graphlet Characterization of Regulatory and Physical Interaction Networks](https://www.biorxiv.org/content/10.1101/2025.02.19.639099v1)<br>
+Altaf Barelvi*, Oliver Anderson*, Anna Ritz<br>
+\* *Equal contribution*
+
 GRPhIN takes as input (1) an undirected PPI network and (2) a directed regulatory network and counts all mixed graphlets and their respective orbits ([Figure 6](https://github.com/Reed-CompBio/motifs/blob/main/Complete%20Graphlet%20%26%20Orbit%20Definitions.pdf)). GRPhIN provides additional functional context to the roles a protein may play beyond traditional isolated network types.
 
 ## Usage
-1. Install and activate the GRPhIN conda environment in the root directory with `conda env create -f environment.yml` and `conda activate grphin`.
-2. To run the GRPhIN orbit and graphlet counting algorithm on the example networks, run the `grphin.py` script with `python3 grphin.py`.
-3. Follow the menu options for your organism of interest.
+Install and activate the GRPhIN conda environment in the root directory:
+```conda env create -f environment.yml```
+Once you have created the conda environment you can activate it (e.g. `conda activate grphin`).
+
+To run the GRPhIN orbit and graphlet counting algorithm on the example networks, run the `grphin.py` script with `python grphin.py`.
+
+```
+usage: grphin.py [-h] -u UNDIRECTED -d DIRECTED -o OUTPUT_DIR
+                 [-g GRAPHLETS_ONLY]
+
+GRPhIN Algorithm
+
+options:
+  -h, --help            show this help message and exit
+  -u, --undirected UNDIRECTED
+                        Path to the undirected/PPI edges input file.
+  -d, --directed DIRECTED
+                        Path to the directed/Reg edges input file.
+  -o, --output_dir OUTPUT_DIR
+                        Path to the output directory.
+  -g, --graphlets_only GRAPHLETS_ONLY
+                        Run GRPhIN in graphlets only mode.
+```
+
+## Example Dataset
+
 
 ## Directories
 - **`data/`** – Contains raw data files for case studies.
@@ -23,4 +52,4 @@ GRPhIN takes as input (1) an undirected PPI network and (2) a directed regulator
 - **`orbit_proteins.py`** – Script for finding protein identities and overrepresented orbits in GRPhIN results.
 - **`pageRank.py`** – Script for running simple Random Walk with Restart algorithm to capture a subnetwork based on oxidative stress pathways.
 - **`README.md`** – This file, providing documentation for the repository.
-- **`significance.py`** – Script to calculate the significance of the appearance of mixed graphlets in oxidative stress pathways compared to random networks.
+- **`significance.py`** – Script to calculate the significance of the appearance of mixed graphlets in oxidative stress pathways compared to random networks.