The main objective of RefMet is to provide a standardized reference nomenclature for both discrete metabolite structures and metabolite species identified by spectroscopic techniques in metabolomics experiments. The wide range of available analytical methods coupled with the even wider range of metabolite databases and in-house libraries has unfortunately led to a pervasive problem wherein the same metabolite species may be reported by many different names. This nomenclature issue represents a significant barrier for comparative analysis of metabolomics data across studies generated by different institutions and/or platforms. We have used data from over 3,000 MS and NMR studies on the Metabolomics Workbench to develop a highly curated analytical chemistry-centric list of common names for metabolite structures and isobaric species. This nomenclature is also linked to a metabolite classification system. A name-conversion user interface is provided where users can submit a list of metabolite names and map them to the corresponding RefMet names. RefMet has the ability to greatly increase the data-sharing potential of metabolomics experiments and facilitate "meta-analysis" and systems biology objectives for the majority of commonly encountered metabolite species.
These R-based tools enable users to map a list of metabolite names (from a text file or a data frame column) to RefMet nomenclature in batch mode, gain access to RefMet metadata for metabolites and perform plotting and saving functions.
Reference: RefMet: a reference nomenclature for metabolomics (Nature Methods, 2020)
To install the current version:
# For Windows:
devtools::install_github("metabolomicsworkbench/RefMet")
# If permission issues prevent installation, lib argument can be specified similar to as explained
# below for the Linux platform.
# For Linux: The use may not have write permission in the standard install location,
# such as, /usr/lib64/R/library, so, one can install it in the user area. The lib argument
# of install_github specifies the library location, e.g., user can specify a subfolder
# in their account where they have write-privilege. In the lib location below, please ensure
# that the subfolder .local/R exists in the user account, else, create it using the Linux
# command mkdir.
USER_HOME=Sys.getenv("HOME");
devtools::install_github("metabolomicsworkbench/RefMet", lib = paste0(USER_HOME, "/.local/R"))
Load RefMet in R: For linux, first add paste0(USER_HOME, "/.local/R") to library path.
.libPaths( c( .libPaths(), paste0(USER_HOME, "/.local/R") )); # Linux only
library(RefMet)#Use a text file of metabolite names as input
infile <- system.file("extdata", "met_list.txt", package = "RefMet")
RefMet_mapped <- refmet_map(infile)
head(RefMet_mapped[,1:4])
Input.name Standardized.name Formula Exact.mass
1 2'-Deoxyuridine Deoxyuridine C9H12N2O5 228.0746
2 4-hydroxyphenylpyruvate 4-Hydroxyphenylpyruvic acid C9H8O4 180.0423
3 acetylcarnitine CAR 2:0 C9H17NO4 203.1158
4 Acylcarnitine (C10:0) [M+H]+ CAR 10:0 C17H33NO4 315.241
5 Adenosine triphosphate ATP C10H16N5O13P3 506.9958
6 Asn Asparagine C4H8N2O3 132.0535
#Use a data frame column containing metabolite names as input
DF <- read.table(infile, header = TRUE, quote = "", sep="\t");
RefMet_mapped <- refmet_map_df(DF[,1])
head(RefMet_mapped[,c(2,5,6,7)])
Standardized.name Super.class Main.class Sub.class
1 Deoxyuridine Nucleic acids Pyrimidines Pyrimidine deoxyribonucleosides
2 4-Hydroxyphenylpyruvic acid Organic acids Phenylpropanoids Cinnamic acids
3 CAR 2:0 Fatty Acyls Fatty esters Acyl carnitines
4 CAR 10:0 Fatty Acyls Fatty esters Acyl carnitines
5 ATP Nucleic acids Purines Purine rNTP
6 Asparagine Organic acids Amino acids and peptides Amino acids
#Plot RefMet main class distribution of metabolite list as a pie chart
#Options are:'Super class','Main class','Sub class'
classplot('Main class',RefMet_mapped)
#Calculate m/z values for an adduct of interest using the RefMet neutral exact mass column in the data frame
#created with refmet_map or refmet_map_df
#The data frame must contain the neutral mass column labeled 'Exact.mass'
RefMet_mapped_with_adduct_col<- adduct_calc('[M+H]+',RefMet_mapped)
head(RefMet_mapped_with_adduct_col[,c(2,4,8)])
Standardized.name Exact.mass [M+H]+
1 Deoxyuridine 228.0746 229.0819
2 4-Hydroxyphenylpyruvic acid 180.0423 181.0496
3 CAR 2:0 203.1158 204.1231
4 CAR 10:0 315.2410 316.2483
5 ATP 506.9958 508.0031
6 Asparagine 132.0535 133.0608
#Fetch metadata for a RefMet name
metadata<- refmet_metadata("Tyrosine")
head(metadata[,1:8])
analysis study refmet_name kegg_id inchi_key pubchem_cid super_class main_class
1 AN002455 ST001479 Tyrosine C00082 OUYCCCASQSFEME-QMMMGPOBSA-N 6057 Organic acids Amino acids and peptides
2 AN004625 ST002832 Tyrosine C00082 OUYCCCASQSFEME-QMMMGPOBSA-N 6057 Organic acids Amino acids and peptides
3 AN004375 ST002484 Tyrosine C00082 OUYCCCASQSFEME-QMMMGPOBSA-N 6057 Organic acids Amino acids and peptides
4 AN001592 ST000972 Tyrosine C00082 OUYCCCASQSFEME-QMMMGPOBSA-N 6057 Organic acids Amino acids and peptides
5 AN003632 ST002223 Tyrosine C00082 OUYCCCASQSFEME-QMMMGPOBSA-N 6057 Organic acids Amino acids and peptides
6 AN004626 ST002832 Tyrosine C00082 OUYCCCASQSFEME-QMMMGPOBSA-N 6057 Organic acids Amino acids and peptides
head(metadata[,9:15])
sub_class species sample disease analysis_type polarity chromatography_type
1 Amino acids Mouse Brain Leigh syndrome LCMS POSITIVE Reversed phase
2 Amino acids Bacteroides uniformis Bacterial cells LCMS POSITIVE HILIC
3 Amino acids Human Blood Autism GCMS POSITIVE GC
4 Amino acids Baboon Blood GCMS POSITIVE GC
5 Amino acids Mouse Kidney Cancer LCMS UNSPECIFIED HILIC
6 Amino acids Blautia producta Bacterial cells LCMS NEGATIVE HILIC
#Plot a bargraph of species distribution of Tyrosine in NMDR studies (Top 10 species)
#Options are 'Species','Sample source','Disease association','Analysis type','MS polarity','Chromatography'
# Specify width(e.g. 15) and height(e.g. 8) of plot
metadata<- refmet_metadata("Tyrosine")
metplot('Species', metadata, 15,8)
#Print species distribution of Tyrosine in NMDR studies to a file. (all species)
#Options are 'Species','Sample source','Disease association','Analysis type','MS polarity','Chromatography'
metadata<- refmet_metadata("Tyrosine")
metprint('Species', metadata)
#Species distribution in NMDR studies is printed to a file called 'Species.txt' in the working directoryEoin Fahy, University of California San Diego (efahy@ucsd.edu)