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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), Doroghazi JR, et al. (2014), and the demo.
- Rosetta: see Hjörleifsson Eldjárn G, et al. (2021) and Soldatou S, et al. (2021).
- NPClassScore: see the preprint Louwen JJR, et al. (2022), and the demo.
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
LinuxandMacOS (Intel chip) MacOS(Apple Silicon, M1/M2 chip)user should execute the install commands in a Rosseta-enabled terminal.- For
Windowsusers, please use our docker image.
See the example in Jupyter notebook 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.
If you want to visualize and manipulate NPLinker predictions, check NPLinker Webapp for more info.
If you want to contribute to the development of nplinker, have a look at the contribution guidelines and README for developers.