Warning- This repository is under development as we are in the process of cleaning up and organising codes. There might be missing files.
Welcome to our project! We've constructed a computational pipeline aimed at elucidating the transcriptional regulation of metabolism in C.elegans at a systems level. Primarily written in Python, this pipeline also utilizes components of MATLAB and shell script. It has been rigorously developed for a development dataset, tissue dataset, and a compendium of 177 expression datasets. Importantly, our pipeline can be applied to any expression dataset, whether it's RNA-Seq or microarray.
Our pipeline comprises the following key modules:
- During Development - Utilizing Variation Score (VS)
- Across Tissues - Utilizing Coefficient of Variation (CV)
- Across a Compendium of Expression Datasets - Utilizing CV
- Enrichment and depletion analysis of regulated genes
- Allows gene-by-gene queries of all C.elegans genes to associate them with metabolic (sub)-pathways.
- For all iCEL genes in the metabolic network model, it generates a clustered heatmap of the query gene with other closely associated metabolic network genes based on coflux and coexpression.
- For all non-iCEL genes, it identifies the pathway enrichment of closely associated metabolic network model genes.
- Python-Centric: Our pipeline is primarily written in Python, ensuring high readability and maintainability.
- Broad Applicability: The methodologies implemented in this pipeline can be extrapolated to any organism for which large gene expression profile compendia and high-quality metabolic network models are available, including humans.
- Interactive Web Tool - WormClust: This feature enables users to assess the association of a specific gene with the metabolic network based on similarities in gene expression. It is consistent with the methodology proposed by Nanda et al., 2023.
Please refer to the attached requirements.txt file for software dependencies.
The repository can be cloned and the dependencies installed as follows:
- Clone the repository:
git clone https://github.com/WalhoutLab/WormClust.git - Navigate to the cloned directory:
cd WormClust - Install the required dependencies:
pip install -r requirements.txt - Alternatively, Create a Conda environment from the requirements.txt file:
conda create --name myenv --file requirements.txt - Activate the Conda environment:
conda activate myenv
- Shivani Nanda - GitHub
- Safak Yilmaz - Email: LutfuSSafak.Yilmaz@umassmed.edu
For bug reports, contributions, or further queries, please reach out to us. Email communication is preferred. Contact Shivani Nanda (shivani.nanda@umassmed.edu) or Safak Yilmaz (LutfuSSafak.Yilmaz@umassmed.edu).
Project Link: https://github.com/WalhoutLab/WormClust
WormClust Web Server Link: http://wormflux.umassmed.edu/WormClust/wormclust.php