| Name | gbintk JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | gbintk (GraphBin-Tk): Assembly graph-based metagenomic binning toolkit |
| upload_time | 2024-09-26 02:13:37 |
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| author | None |
| requires_python | <3.13,>=3.9 |
| license | None |
| keywords |
metagenomics
binning
contigs
bioinformatics
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# GraphBin-Tk: assembly graph-based metagenomic binning toolkit
[](http://bioconda.github.io/recipes/gbintk/README.html)
[](https://anaconda.org/bioconda/gbintk)
[](https://badge.fury.io/py/gbintk)
[](https://github.com/metagentools/gbintk/actions/workflows/testing_python_app.yml)
[](https://codecov.io/gh/metagentools/gbintk)
[](https://github.com/metagentools/gbintk/actions/workflows/codeql.yml)
[](https://gbintk.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/psf/black)
GraphBin-Tk combines assembly graph-based metagenomic bin-refinement and binning techniques [GraphBin](https://github.com/metagentools/GraphBin), [GraphBin2](https://github.com/metagentools/GraphBin2) and [MetaCoAG](https://github.com/metagentools/MetaCoAG) along with additional processing functionality to visualise and evaluate results, into one comprehensive toolkit.
<p align="center">
<img src="https://raw.githubusercontent.com/metagentools/gbintk/master/docs/images/gbintk_workflow.png" width="800" title="Initial binning" alt="Initial binning">
</p>
For detailed instructions on installation and usage, please refer to the documentation hosted at **[Read the Docs](https://gbintk.readthedocs.io/en/latest/)**.
**NEW:** GraphBin-Tk is now available on [bioconda](https://anaconda.org/bioconda/gbintk) and [PyPI](https://pypi.org/project/gbintk/).
## Installing GraphBin-Tk
### Using conda
You can install GraphBin-Tk using the [bioconda](https://anaconda.org/bioconda/gbintk) distribution. You can download `conda` from
[Anaconda](https://www.anaconda.com/distribution/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html). You can also use [`mamba`](https://mamba.readthedocs.io/en/latest/index.html) instead of `conda`.
```shell
# add channels
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
# create conda environment
conda create -n gbintk
# activate conda environment
conda activate gbintk
# install gbintk
conda install -c bioconda gbintk
# check gbintk installation
gbintk --help
```
### Using pip
You can install GraphBin-Tk using `pip` from the [PyPI](https://pypi.org/project/gbintk/) distribution.
```shell
# install gbintk
pip install gbintk
# check gbintk installation
gbintk --help
```
### For development
Please follow the steps below to install `gbintk` using `flit` for development.
```shell
# clone repository
git clone https://github.com/metagentools/gbintk.git
# move to gbintk directory
cd gbintk
# create and activate conda env
conda env create -f environment.yml
conda activate gbintk
# install using flit
flit install -s --python `which python`
# test installation
gbintk --help
```
## Available subcommands in GraphBin-Tk
Run `gbintk --help` or `gbintk -h` to list the help message for GraphBin-Tk.
```shell
Usage: gbintk [OPTIONS] COMMAND [ARGS]...
gbintk (GraphBin-Tk): Assembly graph-based metagenomic binning toolkit
Options:
-v, --version Show the version and exit.
-h, --help Show this message and exit.
Commands:
graphbin GraphBin: Refined Binning of Metagenomic Contigs using...
graphbin2 GraphBin2: Refined and Overlapped Binning of Metagenomic...
metacoag MetaCoAG: Binning Metagenomic Contigs via Composition,...
prepare Format the initial binning result from an existing binning tool
visualise Visualise binning and refinement results
evaluate Evaluate the binning results given a ground truth
```
## Citation
If you use GraphBin-Tk in your work, please cite the relevant tools.
**GraphBin**
> Vijini Mallawaarachchi, Anuradha Wickramarachchi, Yu Lin. GraphBin: Refined binning of metagenomic contigs using assembly graphs. Bioinformatics, Volume 36, Issue 11, June 2020, Pages 3307–3313, DOI: [https://doi.org/10.1093/bioinformatics/btaa180](https://doi.org/10.1093/bioinformatics/btaa180)
**GraphBin2**
> Vijini G. Mallawaarachchi, Anuradha S. Wickramarachchi, and Yu Lin. GraphBin2: Refined and Overlapped Binning of Metagenomic Contigs Using Assembly Graphs. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 8:1-8:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020). DOI: [https://doi.org/10.4230/LIPIcs.WABI.2020.8](https://doi.org/10.4230/LIPIcs.WABI.2020.8)
> Mallawaarachchi, V.G., Wickramarachchi, A.S. & Lin, Y. Improving metagenomic binning results with overlapped bins using assembly graphs. Algorithms Mol Biol 16, 3 (2021). DOI: [https://doi.org/10.1186/s13015-021-00185-6](https://doi.org/10.1186/s13015-021-00185-6)
**MetaCoAG**
> Mallawaarachchi, V., Lin, Y. (2022). MetaCoAG: Binning Metagenomic Contigs via Composition, Coverage and Assembly Graphs. In: Pe'er, I. (eds) Research in Computational Molecular Biology. RECOMB 2022. Lecture Notes in Computer Science(), vol 13278. Springer, Cham. DOI: [https://doi.org/10.1007/978-3-031-04749-7_5](https://doi.org/10.1007/978-3-031-04749-7_5)
> Vijini Mallawaarachchi and Yu Lin. Accurate Binning of Metagenomic Contigs Using Composition, Coverage, and Assembly Graphs. Journal of Computational Biology 2022 29:12, 1357-1376. DOI: [https://doi.org/10.1089/cmb.2022.0262](https://doi.org/10.1089/cmb.2022.0262)
## Funding
GraphBin-Tk is funded by an [Essential Open Source Software for Science
Grant](https://chanzuckerberg.com/eoss/proposals/cogent3-python-apis-for-iq-tree-and-graphbin-via-a-plug-in-architecture/)
from the Chan Zuckerberg Initiative.
<p align="left">
<img src="https://chanzuckerberg.com/wp-content/themes/czi/img/logo.svg" width="300">
</p>
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"description": "# GraphBin-Tk: assembly graph-based metagenomic binning toolkit\n\n\n[](http://bioconda.github.io/recipes/gbintk/README.html)\n[](https://anaconda.org/bioconda/gbintk)\n[](https://badge.fury.io/py/gbintk)\n[](https://github.com/metagentools/gbintk/actions/workflows/testing_python_app.yml)\n[](https://codecov.io/gh/metagentools/gbintk)\n[](https://github.com/metagentools/gbintk/actions/workflows/codeql.yml)\n[](https://gbintk.readthedocs.io/en/latest/?badge=latest)\n[](https://github.com/psf/black)\n\nGraphBin-Tk combines assembly graph-based metagenomic bin-refinement and binning techniques [GraphBin](https://github.com/metagentools/GraphBin), [GraphBin2](https://github.com/metagentools/GraphBin2) and [MetaCoAG](https://github.com/metagentools/MetaCoAG) along with additional processing functionality to visualise and evaluate results, into one comprehensive toolkit.\n\n<p align=\"center\">\n <img src=\"https://raw.githubusercontent.com/metagentools/gbintk/master/docs/images/gbintk_workflow.png\" width=\"800\" title=\"Initial binning\" alt=\"Initial binning\">\n</p>\n\nFor detailed instructions on installation and usage, please refer to the documentation hosted at **[Read the Docs](https://gbintk.readthedocs.io/en/latest/)**.\n\n**NEW:** GraphBin-Tk is now available on [bioconda](https://anaconda.org/bioconda/gbintk) and [PyPI](https://pypi.org/project/gbintk/).\n\n## Installing GraphBin-Tk\n\n### Using conda\n\nYou can install GraphBin-Tk using the [bioconda](https://anaconda.org/bioconda/gbintk) distribution. You can download `conda` from \n[Anaconda](https://www.anaconda.com/distribution/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html). You can also use [`mamba`](https://mamba.readthedocs.io/en/latest/index.html) instead of `conda`.\n\n```shell\n# add channels\nconda config --add channels defaults\nconda config --add channels bioconda\nconda config --add channels conda-forge\n\n# create conda environment\nconda create -n gbintk\n\n# activate conda environment\nconda activate gbintk\n\n# install gbintk\nconda install -c bioconda gbintk\n\n# check gbintk installation\ngbintk --help\n```\n\n### Using pip\n\nYou can install GraphBin-Tk using `pip` from the [PyPI](https://pypi.org/project/gbintk/) distribution.\n\n```shell\n# install gbintk\npip install gbintk\n\n# check gbintk installation\ngbintk --help\n```\n\n### For development\n\nPlease follow the steps below to install `gbintk` using `flit` for development.\n\n```shell\n# clone repository\ngit clone https://github.com/metagentools/gbintk.git\n\n# move to gbintk directory\ncd gbintk\n\n# create and activate conda env\nconda env create -f environment.yml\nconda activate gbintk\n\n# install using flit\nflit install -s --python `which python`\n\n# test installation\ngbintk --help\n```\n\n## Available subcommands in GraphBin-Tk\n\nRun `gbintk --help` or `gbintk -h` to list the help message for GraphBin-Tk.\n\n```shell\nUsage: gbintk [OPTIONS] COMMAND [ARGS]...\n\n gbintk (GraphBin-Tk): Assembly graph-based metagenomic binning toolkit\n\nOptions:\n -v, --version Show the version and exit.\n -h, --help Show this message and exit.\n\nCommands:\n graphbin GraphBin: Refined Binning of Metagenomic Contigs using...\n graphbin2 GraphBin2: Refined and Overlapped Binning of Metagenomic...\n metacoag MetaCoAG: Binning Metagenomic Contigs via Composition,...\n prepare Format the initial binning result from an existing binning tool\n visualise Visualise binning and refinement results\n evaluate Evaluate the binning results given a ground truth\n```\n\n## Citation\n\nIf you use GraphBin-Tk in your work, please cite the relevant tools.\n\n**GraphBin**\n> Vijini Mallawaarachchi, Anuradha Wickramarachchi, Yu Lin. GraphBin: Refined binning of metagenomic contigs using assembly graphs. Bioinformatics, Volume 36, Issue 11, June 2020, Pages 3307\u20133313, DOI: [https://doi.org/10.1093/bioinformatics/btaa180](https://doi.org/10.1093/bioinformatics/btaa180)\n\n**GraphBin2**\n> Vijini G. Mallawaarachchi, Anuradha S. Wickramarachchi, and Yu Lin. GraphBin2: Refined and Overlapped Binning of Metagenomic Contigs Using Assembly Graphs. In 20th International Workshop on Algorithms in Bioinformatics (WABI 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 172, pp. 8:1-8:21, Schloss Dagstuhl \u2013 Leibniz-Zentrum f\u00fcr Informatik (2020). DOI: [https://doi.org/10.4230/LIPIcs.WABI.2020.8](https://doi.org/10.4230/LIPIcs.WABI.2020.8)\n\n> Mallawaarachchi, V.G., Wickramarachchi, A.S. & Lin, Y. Improving metagenomic binning results with overlapped bins using assembly graphs. Algorithms Mol Biol 16, 3 (2021). DOI: [https://doi.org/10.1186/s13015-021-00185-6](https://doi.org/10.1186/s13015-021-00185-6)\n\n**MetaCoAG**\n> Mallawaarachchi, V., Lin, Y. (2022). MetaCoAG: Binning Metagenomic Contigs via Composition, Coverage and Assembly Graphs. In: Pe'er, I. (eds) Research in Computational Molecular Biology. RECOMB 2022. Lecture Notes in Computer Science(), vol 13278. Springer, Cham. DOI: [https://doi.org/10.1007/978-3-031-04749-7_5](https://doi.org/10.1007/978-3-031-04749-7_5)\n\n> Vijini Mallawaarachchi and Yu Lin. Accurate Binning of Metagenomic Contigs Using Composition, Coverage, and Assembly Graphs. Journal of Computational Biology 2022 29:12, 1357-1376. 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