## Badges
| [fair-software.eu](https://fair-software.eu/) recommendations | |
| :-- | :-- |
| (1/5) code repository | [](https://github.com/NPLinker/nplinker) |
| (2/5) license | [](https://github.com/NPLinker/nplinker) |
| (3/5) community registry | [](https://pypi.python.org/project/nplinker/) |
| (4/5) citation | [](https://doi.org/10.5281/zenodo.4680218) |
| (5/5) checklist | Coming soon |
| how FAIR is | [](https://fair-software.eu) |
| **Other best practices** | |
| Documentation | [](https://nplinker.readthedocs.io/en/latest/?badge=latest) |
| Build & Test | [](https://github.com/NPLinker/nplinker/actions/workflows/build.yml) |
| Static analysis | [](https://sonarcloud.io/dashboard?id=NPLinker_nplinker) |
| Coverage | [](https://sonarcloud.io/dashboard?id=NPLinker_nplinker) |
| Citation data consistency | [](https://github.com/NPLinker/nplinker/actions/workflows/cffconvert.yml) |
| **Downloads** |  |
# Natural Products Linker (NPLinker)
NPLinker aims to address the significant bottleneck that exists in the realization of the potential of genome-led metabolite discovery, namely the slow manual matching of predicted biosynthetic gene clusters (BGCs) with metabolites produced during bacterial culture; linking phenotype to genotype.
NPLinker implements a new data-centric approach to alleviate this linking problem by searching for patterns of strain presence and absence between groups of similar spectra (molecular families; MF) and groups of similar BGCs (gene cluster families; GCF). Searches can be performed using a number of available analysis methods employed in isolation or together.
Currently available analysis methods (scoring methods):
- **Metcalf** (standardised): see [Hjörleifsson Eldjárn G, et al. (2021)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008920), [Doroghazi JR, et al. (2014)](https://www.nature.com/articles/nchembio.1659), and the [demo](notebooks/nplinker_demo1.ipynb).
- **Rosetta**: see [Hjörleifsson Eldjárn G, et al. (2021)](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008920) and [Soldatou S, et al. (2021)](https://www.mdpi.com/1660-3397/19/2/103).
- **NPClassScore**: see the preprint [Louwen JJR, et al. (2022)](https://www.researchsquare.com/article/rs-1391827/v2), and the [demo](notebooks/npclassscore_linking/NPClassScore_demo.ipynb).
## Setup and usage
NPLinker is a Python package, you can install it as following:
```
# create a new virtual environment
python -m venv env
source env/bin/activate
# install nplinker package
pip install nplinker
# install nplinker non-pypi dependencies and databases
install-nplinker-deps
```
Due to hardware requirements of some non-pypi dependecies:
- NPLinker can only be installed on `Linux` and `MacOS (Intel chip)`
- `MacOS(Apple Silicon, M1/M2 chip)` user should execute the install commands in a [Rosseta-enabled terminal](https://support.apple.com/en-us/HT211861).
- For `Windows` users, please use our [docker image](https://hub.docker.com/r/nlesc/nplinker).
See the example in [Jupyter notebook](notebooks/nplinker_demo1.ipynb) for a guided introduction to the NPLinker API which shows how to load and examine a dataset. Other notebooks are present showcasing other scoring methods, like for [NPClassScore](notebooks/npclassscore_linking/NPClassScore_demo.ipynb).
If you want to visualize and manipulate NPLinker predictions, check [NPLinker Webapp](https://github.com/NPLinker/nplinker-webapp) for more info.
## Contributing
If you want to contribute to the development of nplinker, have a look at the [contribution guidelines](CONTRIBUTING.md) and [README for developers](README.dev.md).
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