Why use ChemRICH ?

Metabolomics aims to answer a fundamental question in biology: How does metabolism change under genetic, environmental or phenotypic perturbations? Combining several metabolomics assays can yield datasets for more than 1,000 structurally identified metabolites per study. However, biological interpretations of metabolic regulation in these datasets is hindered by the limitations of current pathway definitions as well as inherent limits of pathway enrichment statistics. ChemRICH, a statistical enrichment approach that is based on chemical similarity rather than sparse biochemical knowledge annotations. ChemRICH utilizes chemical ontologies abd structrual similarity to group metabolites. Unlike pathway mapping, this strategy yields study-specific, non-overlapping sets of all identified metabolites. Subsequent enrichment statistics is superior to pathway enrichments because ChemRICH sets have a self-contained size where p-values do not rely on the size of a background database. For more details - see ChemRICH article

For performing enrichment for user-provided class annotation, use this tool ChemRICH Class

Input file structure

Structure of the input file. See here an example data file you can use as template.

The input file must have 6 columns, in this order:

  • Column 1 = Metabolite name
  • Column 2 = InChI Keys
  • Column 3= PubChem ID
  • Column 4 = SMILES codes
  • Column 5 = p-value
  • Column 6= fold-change
You can obtain PubChem CIDs and InChI keys from the Chemical Translation Service. You can obtain SMILES from the PubChem Identifier Exchanger tool.

You can also use this excel file for getting identifiers and SMILES codes for your compounds. ChemRICH Metabolite Identifier File .

Input file must satisfy these conditions

  • No duplicate names OR CIDs
  • No Missing names, SMILES, pvalue or fold-change
  • Minimum one class with at least three compounds
  • Minimum one class shall be significantly enriched

Calculate ChemRICH

ChemRICH results

  • This plot shows the ChemRICH enrichment result.
  • It shows the significantly impacted metabolite clusters in your study.
  • Clusters are generated by chemical similarity and ontology mapping.
  • The plot y-axis shows the most significantly altered clusters on the top.
  • Cluster colors give the proportion of increased or decreased compounds (red = increased, blue = decreased). Chemical enrichment statistics is calculated by Kolmogorov–Smirnov test.
  • Only enrichment clusters are shown that are significantly different at p<0.05.

Download results in various format.

  • ChemRICH tree plot (.pptx)
  • ChemRICH cluster plot (.png)
  • ChemRICH results (.xlsx)
  • Access the Interactive Compound plot

Cluster level enrichment results

Compound level data.table

Contact, bug reports and source code.

Dinesh Barupal - dinkumar@ucdavis.edu & Oliver Fiehn - ofiehn@ucdavis.edu

Github repo - barupal/chemrich


Barupal Dinesh K & Fiehn Oliver. ChemRICH : Chemical Similarity Enrichment Analysis for metabolomics datasets. Scientific Reports (2017).