RaMP-DB 3.0: a relational database for multi-omic data interpretation

Authors

• KY Mehta
• A Patt
• T Sheils
• A Tisch
• J Sayer
• J Braisted
• KJ Kelleher
• EA Mathé

Abstract

RaMP-DB is a public, up-to-date and comprehensive resource for biological, chemical, ontology and reaction annotations with searching and enrichment analyses for metabolomic and multi-omic data interpretation. We present here our version 3.0 which includes some key new features: updates to currently included knowledge sources, conversion to an SQLite database, the addition of Rhea reactions, interactive visualizations, and additional enrichment functions for chemical annotations. RaMP-DB parses information from HMDB, KEGG (through HMDB), Reactome, WikiPathways, LIPIDMAPS, Rhea, and ChEBI and includes 254,860 chemical structures, of which 43,338 are lipids, 15,389 genes, 53,745 pathways, 15,849 curated reactions, 791,513 predicted reactions, and 699 ontologies. Two major components of RaMP-DB analyses are lookups and enrichment analyses. First, RaMP-DB supports queries to ‘lookup’ information (e.g., pathways, chemical class, ontologies) about user-input analytes. These lookups enable users to globally evaluate how much is known about analytes of interest. Second, RaMP-DB supports multi-omic pathway enrichment and chemical class enrichment for metabolites. Functions to generate interactive visualizations, such as sunburst and UpSet plots, have also been added to further explore results of lookups and analyses. These plotting functions take from outputs of existing functions to streamline data exploration. The RaMP-DB R package includes vignettes, example data, documented functions, and all the code is publicly available at https://github.com/ncats/RaMP-DB. The website UI https://rampdb.nih.gov/ has been redesigned to condense similar query pages and expose query options supported by the RaMP R package. Overall, the RaMP-DB updated resource and associated analytics facilitates multi-omic data interpretation via analysis queries and interactive visualizations.

Scientific Focus Area: Computational Biology

This page was last updated on Tuesday, August 6, 2024