MetMashR is an R/Bioconductor package for integrating, harmonising and processing metabolite annotations from multiple sources using modular, reproducible workflows.
Metabolomics is the analysis of small molecules, or metabolites, in biological and environmental samples (e.g. blood, urine and water). Liquid chromatography–mass spectrometry (LC–MS) is widely used in metabolomics and produces complex datasets from which thousands of LC–MS features can be detected. Computational annotation tools attempt to associate these features with candidate chemical compounds, but different tools may produce conflicting annotations, use inconsistent compound names or identifiers, or report multiple candidates for a single feature.
MetMashR integrates and harmonises metabolite annotations from multiple sources. It imports annotation outputs into a common structure and provides modular tools for cleaning, standardising, filtering, prioritising and combining annotations, as well as enriching them with information from resources such as PubChem, ChEBI, KEGG and LIPID MAPS. These steps can be combined into reproducible workflows that can be reused and applied consistently across analyses. MetMashR was developed primarily for LC–MS annotation workflows, but its modular framework can also be extended to other analytical platforms.
Documentation
Bioconductor
GitHub
MetMashR workflow.To install the release version from Bioconductor:
if (!require("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("MetMashR")
To install the development version from GitHub:
if (!require("remotes", quietly = TRUE)) {
install.packages("remotes")
}
remotes::install_github("computational-metabolomics/MetMashR")
This example uses LipidSearch annotations that are included with the package. The workflow imports the annotations, keeps only the higher-confidence ones (grades A and B), then combines duplicate annotations of the same lipid into a single record.
library(MetMashR)
# annotation source: a LipidSearch output file
AT <- ls_source(
source = system.file(
"extdata/MTox/LS/MTox_2023_HILIC_POS.txt",
package = "MetMashR"
)
)
# workflow
WF <-
# step 1: import the annotations
import_source() +
# step 2: keep annotations with grade A or B
filter_labels(
column_name = "Grade",
labels = c("A", "B"),
mode = "include"
) +
# step 3: combine records for the same lipid, counting how many there were
combine_records(
group_by = "LipidName",
default_fcn = fuse_unique(separator = "; "),
fcns = list(count = count_records())
)
# apply the workflow to the annotation source
WF <- model_apply(WF, AT)
# the processed annotations
predicted(WF)
head(predicted(WF)$data[, c("LipidName", "Grade", "count")])
# the annotations after any individual step, e.g. after filtering
predicted(WF[2])
The Using MetMashR vignette describes these and many other workflow steps in detail.
Gavin Rhys Lloyd University of Birmingham g.r.lloyd@bham.ac.uk
For bug reports and feature requests, please use the GitHub issue tracker.
To obtain the recommended citation for MetMashR in R, run:
citation("MetMashR")
MetMashR is distributed under the GNU General Public License version 3 (GPL-3).