Multipath2.0: Extending Multilayer Reproducible Pathway Models with Omics Data

Abstract

Background

Biological systems are often perceived as independent and consequently analyzed individually. In the field of omics, multiple disciplines target the study of specific types of molecules, such as genomics. The support of more data sources in these analyses is becoming more crucial for understanding the interplay of biological systems. However, this requires integration of heterogeneous knowledge, which is considered highly challenging in bioinformatics and biomedicine. Therefore, the R package Multipath was developed to model biological pathways as multilayered graphs and integrate influencing knowledge including proteins and drugs. In its previous form, Multipath generated multilayer models of BioPAX-encoded pathways and included features to integrate drug and protein information from DrugBank and UniProtKB respectively. Although the model showed remarkable utility, including further data sources ensures enriching and expanding its capabilities.

Results

In this paper, a new version Multipath 2.0 is presented. The update additionally supports the two databases KEGG Genes and OMIM, which serve as the source for gene and disease entries and interactions. Information on the interactions between the previously and newly added nodes are extracted and integrated. The Multipath 2.0 offers features to update the original multilayer model and integrate the corresponding nodes and edges into two additional layers referring to KEGG Genes and OMIM. Furthermore, the embedded nodes are inter- and intra-connected using interactions from the original and newly supported data sources.

Conclusion

The R Package Multipath is presented with the main functions that are newly developed to support the integration of the databases KEGG Genes and OMIM. The model comprises multiple information relevant to the analysis of pathway data, and offers a reproducible and simplified view of complex, intertwined systems. Through the application of such highly integrated models the inference of new knowledge becomes easier and contributes to many fields such as drug repurposing and biomarker discovery.