A PyTorch implementation for the ICLR paper:
Encoding Unitig-level Assembly Graphs with Heterophilous Constraints for Metagenomic Contigs Binning [OpenReview]
UnitigBIN consists of two main components: Learning, which uses a graph neural network to model the unitig-level assembly graph while adhering to constraints, and Binning, a contig-level framework. In the Binning stage, a Matching algorithm is employed to initialize markered contigs, Propagating labels are used to annotate unmarked contigs while satisfying constraints, and a local Refining strategy is incorporated to fine-tune binning assignment.
Python 3.6
networkx == 1.11
numpy == 1.18
sklearn == 0.22
pytorch == 1.3.1
To reproduce the experiments on Sim5G dataset, simply run:
python main.py -d Sim5GAll readers are welcome to star/fork this repository and use it to reproduce our experiments or train your own data. Please kindly cite our paper:
@inproceedings{Xue2023UnitigBIN,
title={Encoding Unitig-level Assembly Graphs with Heterophilous Constraints for Metagenomic Contigs Binning},
author={Xue, Hansheng and Mallawaarachchi, Vijini and Xie, Lexing and Rajan, Vaibhav},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024}
}