Bacteria, as the first and most diverse forms of life on Earth, play a crucial role in various biological processes. Metagenomics, the identification of bacterial genomes from DNA sequences obtained directly from environmental samples, is a key field of study in understanding microbial diversity and functionality. Traditional bio-marker based binning methods have limitations, leading to the exploration of bio-marker free methods. However, existing binning methods still lack high accuracy and completeness, binning only a fraction of genome fragments.
This repository contains the implementation of a novel method for improving and completing the Metagenomics data binning process by incorporating biological networks and gene ontology graphs into the initial binning result. The proposed method utilizes the concept that fragments involved in the same biological processes, cellular compartments, or molecular functions likely belong to the same genome. Extensive testing on various datasets, previously binned using base methods, demonstrated significant improvement in binning accuracy and completeness.
code/: The source code for the proposed method, implemented in [programming language].datasets/: Sample datasets used for testing and evaluating the method's performance.documentation/: Additional documentation, including a user guide and technical specifications.results/: The results of the experiments conducted to validate the effectiveness of the proposed method.dependencies/: List of dependencies and installation instructions required to run the code.
To utilize the method, follow these steps:
- Clone or download the repository to your local machine.
- Install the necessary dependencies as outlined in the provided instructions.
- Prepare the input metagenomic sequence fragments for binning.
- Execute the main script, providing the necessary input parameters.
- Analyze the output results and evaluate the binning performance.
For detailed instructions and usage examples, refer to the documentation provided in the repository.
Contributions to this project are welcome. If you have any suggestions, bug fixes, or improvements, please feel free to open an issue or submit a pull request.
This project is licensed under the MIT License.