RaMP 3.0 is now released and includes an updated backend database with expanded annotations for >200,000 metabolites and ~16,000 genes/proteins. 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, and Rhea reaction database.
This R package includes functions that allow users to interface with this up-do-date and comprehensive resource. Functionalities include 1) simple and batch queries for pathways, ontologies, chemical annotations, and reaction-level gene-metabolite relationships; 2) pathway and chemical enrichment analyses.
The code used to build the backend RaMP database is freely available at https://github.com/ncats/RaMP-Backend.
Please click here to view our latest manuscript.
Our new revamped web interface can be found at https://rampdb.nih.gov/. The code is publicly available at https://github.com/ncats/RaMP-Client/.
API access is now available here.
The purpose of RaMP is to provide a publicly available database that integrates metabolite and gene/protein biological, chemical and other from multiple sources. The database structure and data is available as an SQLite database file and it is directly downloaded when using the RaMP package. Please see the Installation Instructions for further information. Please note that this project is in continuous development and we appreciated any feedback.
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.
The R packages and associated app perform the following queries:
1. Retrieve analytes (genes, proteins, metabolites) given pathway(s) as input.
2. Retrieve pathway annotations given analytes as input.
3. Retrieve chemical annotations/structures given metabolites as input.
4. Retrieve analytes involved in the same reaction (e.g. enzymes catalyzing reactions involving input metabolites)
5. Retrieve ontologies (e.g. biospecimen location, disease, etc.) given input meteabolites.
6. Retrieve reactions associated with a list of metabolite and gene/protein input ids.
7. Multi-omic pathway enrichment analysis
8. Chemical enrichment analyses
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: There is incompatibility (reported here: https://stat.ethz.ch/pipermail/bioc-devel/2023-October/020003.html) between the version of BiocFileCache installed using BiocManager (2.8.0) and the actual latest version (2.10.1). The latter is needed to be compatible with other dependencies in RaMP-DB. To install the latest version, you will need to download the source file from Bioconductor (https://bioconductor.org/packages/release/bioc/html/BiocFileCache.html), then install using the install.packages() function. For a Mac, this looks like this:
install.packages("/Users/mathee/Downloads/BiocFileCache_2.10.1.tgz")
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 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 |
| Genes/Proteins | ensembl, entrez, gene_symbol, uniprot, hmdb, ncbiprotein, EN, wikidata, chebi |
The following RaMP 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')
- John Braisted - [email protected]
- Tara Eicher - [email protected]
- Ewy Mathé - [email protected]
- Andrew Patt - [email protected]
- Tim Sheils - [email protected]
- Kyle Spencer - [email protected]