RaMP-DB 3.0 is now released and includes an updated backend database with expanded annotations for >250,000 metabolites, ~34,000 genes/proteins, >122,000 pathways, and > 66,000 reactions. Annotations include biological pathways, chemical classes and structures (for metabolites only), ontologies (metabolites only), and enzyme-metabolite relationships based on chemical reactions. Annotations are drawn from HMDB, KEGG (through HMDB), Lipid-MAPS, WikiPathways, Reactome, CheBI, PFOCR, RefMet, and Rhea reaction database.
This R package allows users to interface with this up-do-date and comprehensive resource. Functionalities include 1) Queries: simple and batch queries for pathways, ontologies, chemical annotations, and reaction-level gene-metabolite relationships; 2) Enrichment Analysis: pathway and chemical enrichment analyses; 3) Exploration of Results: interactive visualizations of metabolite/gene annotations.
The code used to build the backend RaMP-DB database is freely available at https://github.com/ncats/RaMP-Backend.
Please click here to view our latest manuscript.
Visit our linktree here to access the many components related to RaMP-DB, incuding links to the front-end, back-end, vignette, publication, API, and the user-friendly website.
Our new revamped web interface can be found at https://rampdb.nih.gov/. The code for the front-end is publicly available at https://github.com/ncats/ncats-frontend-library/tree/main/apps/ramp-client.
API access is now available here.
The purpose of RaMP-DB is to provide a publicly available database that integrates metabolite and gene/protein biological, chemical, reaction, and ontology information from multiple sources. The database structure and data is available as an SQLite database file and it is directly downloaded when using the RaMP-DB package. Please see the Installation Instructions for further information. Please note that this project is in continuous development and we appreciate any feedback (through email below or create a GitHub issue)! Thanks to those that have provided some thus far.
For any questions or feedback, please send us a note at [email protected].
If you find a bug, please submit an issue through this GitHub repo.
Detailed instructions for installing RaMP locally are below. We've also put together a vignette to get you started on the analyses. Click here for vignette.
If you use RaMP-DB, please cite the following work:
Braisted J, Patt A, Tindall C, Sheils T, Neyra J, Spencer K, Eicher T, Mathé EA. RaMP-DB 2.0: a renovated knowledgebase for deriving biological and chemical insight from metabolites, proteins, and genes. Bioinformatics. 2023 Jan 1;39(1):btac726. doi: 10.1093/bioinformatics/btac726. PMID: 36373969; PMCID: PMC9825745. To access, click here
Zhang, B., et al., RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites. Metabolites, 2018. 8(1). PMID: 29470400; PMCID: PMC5876005; DOI: 10.3390/metabo8010016 To access, click here
In order to use this R package locally, you will need to install the R code under this repository.
Special Note: If you do not have BiocFileCache already installed, you will need this prior to installing RaMP-DB. To do so, use the following:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("BiocFileCache")
You can install this package directly from GitHub using the install_github() function available through the devtools package. In the R Console, type the following:
# Locally install RaMP
install.packages("devtools")
library(devtools)
install_github("ncats/RAMP-DB")
# Load the package
library(RaMP)
# initializes the RaMP database object, downloading and caching the latest SQLite database
# if no version already exists in local cache.
rampDB <- RaMP()
# note that you can use the following method to check database versions hosted in your
# computer's local cache and databases that are available to download in our remote repository.
RaMP::listAvailableRaMPDbVersions()
# using that list of available RaMP DB versions, one can specify the database version to use
# if the selected version is not available on your computer, but is in our remote repository at GitHub,
# the SQLite DB file will be automatically downloaded into local file cache.
# RaMP is using the BiocFileCache package to manage a local file cache.
rampDB <- RaMP(version = "2.5.4")
When gene/protein or metabolite ids are input for queries, IDs should be prepended with their database of origin, e.g. kegg:C02712, hmdb:HMDB04824, or CAS:2566-39-4. The list of metabolite or gene/protien IDs may be of mixed source. Remember to include the colon in the prefix. The id prefixes that are currently included in RaMP are:
| Analyte Type | ID Prefix Types |
|---|---|
| Metabolites | hmdb, pubchem, chebi, chemspider, kegg, CAS, LIPIDMAPS, swisslipids, lipidbank, wikidata, plantfa, kegg_glycan, refmet, rhea-comp |
| Genes/Proteins | ensembl, entrez, gene_symbol, uniprot, hmdb, ncbiprotein, EN, wikidata, chebi, brenda |
The following RaMP-DB functions can be used to list all represented id prefix types.
rampDB <- RaMP()
RaMP::getPrefixesFromAnalytes(db = rampDB, analyteType = 'metabolite')
RaMP::getPrefixesFromAnalytes(db = rampDB, analyteType = 'gene')
- Ewy Mathé - [email protected]
- Andrew Patt - [email protected]
- Khyati Mehta - [email protected]
- Adam Tisch - [email protected]
- Keith Kelleher - [email protected]
- Tim Sheils - [email protected]
- Haley Chatelaine - [email protected]