MBC PathNet: integration and visualization of networks connecting functionally related pathways predicted from transcriptomic and proteomic datasets.

Abstract

Motivation: Advances in high-throughput technologies have shifted the focus from bulk to single cell or spatial transcriptomic and proteomic analysis of tissues and cell cultures. The resulting increase in gene and/or protein lists leads to the subsequent growth of up- and downregulated pathways lists. This trend creates the need for pathway-network based integration strategies that allow quick exploration of shared and distinct mechanisms across datasets.

Results: Here, we present Molecular Biology of the Cell (MBC) Pathway Networks (PathNet). MBC PathNet allows for quick and easy integration and visualization of networks of functionally related pathways predicted from gene and protein lists using the Molecular Biology of the Cell Ontology and other ontologies. Within networks of hierarchical parent-child relationships or functional relationships, pathways are visualized as pie charts where each slice represents a dataset that predicted that pathway. Sizes of pies and slices can be selected to represent statistical significance or other quantitative measures. In addition, MBC PathNet can generate bar diagrams, heatmaps, and timelines. Fully automated execution from the command line is supported.

Availability and implementation: iyengarlab.org/mbcpathnet; mbc-ontology.org; github.com/SBCNY/Molecular-Biology-of-the-Cell.