Mass-Spectra Prediction Using the FIORA Model.

{fioRa} 0.3.7

FIORA is an in silico fragmentation algorithm designed to predict tandem mass spectra (MS/MS) with high accuracy. Using a graph neural network, FIORA models bond cleavages, fragment intensities, and estimates retention times (RT) and collision cross sections (CCS).

The original model and prediction algorithm is implemented in Python and can be found at its GitHub Repo. The full description of the algorithm is published in a Nature Communications article.

The R package {fioRa} provides a wrapper for FIORA, to either run the Python script using the R function run_script() or start a GUI (Shiny-App) using the R function run_app().

Installation

You can install the development version of {fioRa} from GitHub.

install.packages("devtools")
devtools::install_github("janlisec/fioRa")

Before first use you need to set up a python installation and a conda environment fiora which can be achieved from within R with the help of the reticulate package and a convenience function.

fioRa::install_fiora()
#> No valid 'default_path' provided, using reticulate::miniconda_path 'C:/Users/jlisec/AppData/Local/r-miniconda'.
#> $os
#> [1] "Windows"
#> 
#> $python
#> [1] "C:\\Users\\jlisec\\AppData\\Local\\r-miniconda\\envs\\fiora\\python.exe"
#> 
#> $script
#> [1] "C:\\Users\\jlisec\\AppData\\Local\\r-miniconda\\envs\\fiora\\Scripts\\fiora-predict"

Run

Now, you can launch the application as a Shiny-App.

fioRa::run_app()

Alternatively, you can use the exported function run_script() to work in the R command line directly. This will accept R styled input parameters, generate an appropriate temporary FIORA input file, process it, and return an R styled list including the predicted MS/MS spectrum.

x <- data.frame(
  Name = "Example_0",
  SMILES = "CC1=CC(=O)OC2=CC(OS(O)(=O)=O)=CC=C12",
  Precursor_type = "[M-H]-",
  CE = 17,
  Instrument_type = "HCD"
)
fioRa::run_script(x = x, annotation = TRUE)
#> No valid 'default_path' provided, using reticulate::miniconda_path 'C:/Users/jlisec/AppData/Local/r-miniconda'.
#> $Example_0
#> $Example_0$TITLE
#> [1] "Example_0"
#> 
#> $Example_0$SMILES
#> [1] "CC1=CC(=O)OC2=CC(OS(O)(=O)=O)=CC=C12"
#> 
#> $Example_0$FORMULA
#> [1] "C10H8O6S"
#> 
#> $Example_0$PRECURSOR_MZ
#> [1] "254.99688252391005"
#> 
#> $Example_0$PRECURSORTYPE
#> [1] "[M-H]-"
#> 
#> $Example_0$COLLISIONENERGY
#> [1] "17.0"
#> 
#> $Example_0$INSTRUMENTTYPE
#> [1] "HCD"
#> 
#> $Example_0$COMMENT
#> [1] "\"In silico generated spectrum by FIORA OS v1.0.0\""
#> 
#> $Example_0$spec
#>          mz         int                           SMILES  adduct  formula
#> 1  78.94844 0.013833499                      O=[SH](=O)O [M-3H]-    H2O3S
#> 2  79.95681 0.002825602                      O=[SH](=O)O [M-2H]-    H2O3S
#> 3 175.03897 0.899893463           Cc1cc(=O)oc2cc(O)ccc12  [M-H]-  C10H8O3
#> 4 254.99688 0.102497801 Cc1cc(=O)oc2cc(OS(=O)(=O)O)ccc12  [M-H]- C10H8O6S

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You are reading the doc about version 0.3.7 compiled on 2026-01-23 13:28:55.965608.