Harmonizing pathway databases using Biological Expression Language (BEL)
This project is the continuation of the ComPath web application aimed at exploring, analyzing, and curating pathway knowledge in a gene-centric view. This different approach involves converting all the pathways in these resources into BEL as a pivotal integration schema to harmonize entities and relationships in order across these multiple resources; thus, enabling a more comprehensive evaluation of pathway cross-talks, consensus, and boundaries. Additionally, PathMe is complemented with the PathMe-Viewer, a web application that enables querying, browsing, and navigating pathway knowledge assisted by a user-friendly visualization.
PathMe currently uses the following versions of the databases:
- KEGG: Up-to-date (KEGG does not have tag its releases)
- Reactome: 67 Release
- WikiPathways: March 2020 Release
If you use PathMe in your work, please consider citing:
|||Domingo-Fernández, D., et al. (2019). PathMe: Merging and exploring mechanistic pathway knowledge. BMC Bioinformatics, 20:243.|
pathme can be directly installed from PyPi with pip:
$ python3 -m pip install pathme
To use the latest version install directly from GitHub:
$ python3 -m pip install git+https://github.com/PathwayMerger/PathMe.git
- or in editable mode with:
$ git clone https://github.com/PathwayMerger/PathMe.git $ cd pathme $ python3 -m pip install -e .
How to Use
Before using PathMe, make sure you have installed and populated the Bio2BEL HGNC and Bio2BEL ChEBI databases (Simple run:”python3 -m bio2bel_hgnc populate” and “python3 -m bio2bel_chebi populate”) in your favourite terminal.
Each database has three main commands: download, bel, and summarize:
- Download content
PathMe first requires to download the raw files from the original pathway databases. This can be accomplished by running the command (‘database’ can be either KEGG, Reactome, or WikiPathways). E.g., python3 -m pathme kegg download
$ python3 -m pathme <database> download
- Generate BEL Graphs
Once the raw files are downloaded, you can run the following to command to generate BELGraphs that will be exported as Python pickles files for further analysis. Furthermore, the conversion to BEL can be tuned differently for each database by using specific commands. For example, KEGG parameters are shown when running “python3 -m pathme kegg bel –help”. Finally, please bear in mind that converting the Reactome files take up to 8 hours due to the large amount of its RDF file.
$ python3 -m pathme <database> bel
Summarizes the result of the conversion to BEL.
$ python3 -m pathme <database> summarize
The KEGG module of PathMe is able to handle KGML differently depending on the goal. By default, KEGG groups together the complex of nodes (e.g., gene families) into one node as it is depicted in the KEGG cartoons and represented in the KGML files. However, this behavior can be modified by adding the parameter –flatten=True in the exporting command. Example:
$ python3 -m pathme kegg bel --flatten
Run the following command to see the different formats that you can export PathMe to (e.g., CX, SPIA, etc.):
$ python3 -m pathme export --help
PathMe is a scientific software that has been developed in an academic capacity, and thus comes with no warranty or guarantee of maintenance, support, or back-up of data.
PathMe makes use of KEGG KGML files that are downloaded via the KEGG API for academic purposes (please make sure you comply their Terms and Conditions).
|||Kanehisa, et al. (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353-D361.|
|||Kanehisa, M., et al. (2016). KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457-D462.|
|||Kanehisa, M. and Goto, S. (2000). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27-30.|
|||Fabregat, A et al. (2016). The Reactome Pathway Knowledgebase. Nucleic Acids Research 44. Database issue: D481–D487.|
|||Croft, D et al. (2014). The Reactome Pathway Knowledgebase. Nucleic Acids Research 42.Database issue: D472–D477.|
|||Slenter, D.N., et al. (2017). WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Research, doi.org/10.1093/nar/gkx1064|
|||Kutmon, M., et al. (2016). WikiPathways: capturing the full diversity of pathway knowledge Nucl. Acids Res., 44, D488-D494.|
|||Kelder, T., et al. (2011). WikiPathways: building research communities on biological pathways. Nucleic Acids Res. Jan;40(Database issue):D1301-7|
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