Back

Viime-Path: An Interactive Metabolic Pathway Generation Tool for Metabolomics Data Analysis

OConnell, T. M.; Baumes, J.

2023-03-10 cancer biology
10.1101/2023.03.07.531550 bioRxiv
Show abstract

IntroductionA primary objective of metabolomics studies is identifying the critical metabolic pathways that are altered under specific conditions. There are a number of tools such as enrichment or over-representation algorithms which can identify pathways, but tools for visualizing these pathways beyond static diagrams are limited. ObjectivesDevelopment of an open-source software tool to enable users to generate metabolic pathway diagrams that can be interactively adjusted to allow for the most lucid and meaningful representation of the data. The ability to input and integrate multiple metabolic pathways and interactively adjust could facilitate the identification of novel metabolic pathways. MethodsThe Viime-Path software package utilizes the metabolic pathways in the Reactome Database. The initial pathway layouts are generated using the Human-like Orthogonal Network Layout (HOLA) algorithm which was designed to take such datasets and map them onto orthogonal network layouts using aesthetic principles derived from the analysis of human-generated networks. The software is written in TypeScript and Vue, utilizing the WebCola graph layout library and D3.js for visualization primitives. ResultsWe have created an easy-to-use, web-based, open-source tool to easily import metabolic pathways from the Reactome database. The tool allows for interactive optimization of pathways to best represent the most salient features for a given analysis. The tool is available on GitHub at https://github.com/girder/viime-path. ConclusionsThe Viime-Path tool will enable the metabolomics community to interactively draw metabolic pathway networks with options to adjust specific components and layout, thus providing new levels of clarity and focus.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.