RHermes
RHermes is a broad-scoped targeted metabolomics software designed to
analyse LC-MS and LC-MS/MS data to identify compounds in biological or
environmental samples.
The RHermes workflow works with both Orbitrap and q-TOF instrument
data and comes with an easy to use GUI that will guide you every step of
the way.
You are in control of your metabolites: whether it’s natural products,
biomedical or enviormental samples, RHermes has you covered. By
restricting the formula database, you can focus on just the compounds
you are interested in and achieve greater metabolome coverage depth.
Have you ever wished you could just see the metabolites in your
data? With RHermes you can do that and much more. Say goodbye to
manually calculating m/z’s and plotting XIC of different adducts: with
our GUI you are just one click away from a metabolite-centric plot.
For more info, check out the documentation here and the Nature Methods article here
System requirements
The recommended system requirements are:
- At least 8-16 GB of RAM
- An internet connection to perform KEGG queries
Installation
You can download the development version from GitHub with:
if(!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("RogerGinBer/RHermes")Setup
RHermes can perform almost all its functions after installation, but
the SOI Blank Substraction step requires a valid keras and
tensorflow installation (which rely on Python).
Option 1: Default installation
For most users, both keras and tensorflow can be configured with:
reticulate::install_miniconda()
keras::install_keras()
tensorflow::install_tensorflow()After which you can check the following:
tensorflow::tf_config()
model <- keras::load_model_hdf5(system.file("extdata", "ImprovedModel.h5",
package = "RHermes"))
is(model, "python.builtin.object") #Gives TRUE if the loading is successful.If both commands don’t give any error (the “Your CPU supports …” warning is fine) the installation has been successful. If it fails (which can happen in some users with previous Python installations, try Option 2).
Option 2: Manual installation
First install Miniconda:
reticulate::install_miniconda()Now find the Miniconda Prompt in your computer. Instead of relying on the default r-reticulate environment, type the following to create a new environment:
conda create -n newenv python=3.6 tensorflow keras
When finished, type in R:
reticulate::use_condaenv("newenv", required = TRUE)
tensorflow::tf_config()
model <- keras::load_model_hdf5(system.file("extdata", "ImprovedModel.h5",
package = "RHermes"))Everything should run smoothly. If not, try manually installing Anaconda
from their website and letting reticulate know where to find the
environment.
Also check out Keras and Tensorflow R tutorials.
Analyzing LC-MS data with RHermes
Once installed, you can use RHermes programmatically like this:
library(RHermes)
#Generate a Exp object
example <- RHermesExp()
#Set your formula and adduct database
example <- setDB(example, db = "hmdb")
#Process your MS1 files
example <- processMS1(example,
system.file("extdata", "MS1TestData.mzML",
package = "RHermes"))
#Generate SOIs
example <- findSOI(example, getSOIpar(), 1)
#Generate an IL (Inclusion List)
example <- generateIL(example, 1, ILParam())With the generated inclusion list, you can export it and run a Parallel Reaction Monitoring (PRM) MS2 experiment to reveal coeluting isomers or use any other MS2 mode you see fit.
Or start the interactive GUI typing:
RHermesGUI()In the GUI you will find abundant help pages to guide you along the
processing
Please check the User Guide vignette for more detailed info and real examples.
Bug reporting
Suggestions and bug reports are more than welcome at: https://github.com/RogerGinBer/RHermes/issues
Citation
Please cite this package as:
Giné, R., Capellades, J., Badia, J.M. et al. HERMES: a molecular-formula-oriented method to target the metabolome. Nat Methods 18, 1370–1376 (2021). https://doi.org/10.1038/s41592-021-01307-z