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PathwayForte Resources/Results

Resources for the benchmarking of KEGG [1] [2] [3], Reactome [4] [5], and WikiPathways [6] [7] [8] on statistical enrichment and predictive Modeling using PathwayForte.

If you find pathway_forte useful for your work, please consider citing [9]:

[9]Mubeen, S., Hoyt, C. T., Gemünd, A., Hofmann-Apitius, M., Fröhlich, H., & Domingo-Fernández, D. (2019). The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling. Front. Genet., 10:1203.

Structure

  • Notebooks. Jupyter notebooks showing the results of the analysis.
  • R. Scripts to download, query and preprocessing TCGA datasets.
  • Input: Directories containing gmt files, TCGA gene expression data, enrichment scores and test data used in the paper (in case proprocessing has been already conducted)

References

KEGG

[1]Kanehisa, M., et al. (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353-D361.
[2]Kanehisa, M., et al. (2016). KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457-D462.
[3]Kanehisa, M. and Goto, S. (2000). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27-30.

Reactome

[4]Fabregat, A. et al. (2016). The Reactome Pathway Knowledgebase. Nucleic Acids Res 44. Database issue: D481–D487.
[5]Croft, D. et al. (2014). The Reactome Pathway Knowledgebase. Nucleic Acids Res 42. Database issue: D472–D477.

WikiPathways

[6]Slenter, D. N., et al (2018). WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Research, 46(D1), D661–D667.
[7]Kutmon, M., et al. (2016). WikiPathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res., 44, D488-D494.
[8]Kelder, T., et al. (201). WikiPathways: building research communities on biological pathways. Nucleic Acids Res. Jan;40(Database issue):D1301-7