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Knowledge graphs (KGs) are an approach to knowledge representation that uses graph structure to facilitate exploration and analysis of complex data, often leveraging semantic information. They are popular in many research areas, including the life sciences, due to their versatile use, for instance in data storage, integration, reasoning, and more recently in artificial intelligence. The creation of KGs is a complex task; BioCypher helps you in creating and maintaining your own KG. For more overview, usage notes, and a tutorial, read the docs here.
Tutorial and developer docs at https://biocypher.org. For a quickstart into your own pipeline, you can refer to our project template, and for an overview of existing and planned adapters for resources and outputs, as well as other features, visit our GitHub Project Board.
Install the package from PyPI using pip install biocypher. More comprehensive
installation and configuration instructions can be found
here.
Exemplary usage of BioCypher to build a graph database is shown in our tutorial and the various pipelines we have created. You can find these on the Components Project Board.
We are very happy about contributions from the community, large and small! If you would like to contribute to BioCypher development, please refer to our contribution guidelines. :)
If you want to ask informal questions, talk about dev things, or just chat, please join our community at https://biocypher.zulipchat.com!
Imposter syndrome disclaimer: We want your help. No, really. There may be a little voice inside your head that is telling you that you're not ready, that you aren't skilled enough to contribute. We assure you that the little voice in your head is wrong. Most importantly, there are many valuable ways to contribute besides writing code.
This disclaimer was adapted from the Pooch project.
The BioCypher paper has been peer-reviewed in Nature Biotechnology. It is available as a self-archived version on Zenodo, online version here. Before, it was available as a preprint at https://arxiv.org/abs/2212.13543.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 965193 for DECIDER and No 116030 for TransQST.