patRoon aims to provide comprehensive mass spectrometry based non-target analysis (NTA) workflows for environmental
analysis. The name is derived from a Dutch word that means pattern and may also be an acronym for hyPhenated mAss
specTROmetry nOn-target aNalysis.
December 2021 patRoon 2.0 is now available. This major new release adds functionality to automatically screen and
identify transformation products, process positive and negative ionization MS data simultaneously and combine the
results, new algorithms for feature and adduct detection, interactive data curation and more. Please see the Project
NEWS for details.
Mass spectrometry based non-target analysis is used to screen large numbers of chemicals simultaneously. For this purpose, high resolution mass spectrometry instruments are used which are typically coupled (or hyphenated) with chromatography (e.g. LC or GC). The size and complexity of resulting data makes manual processing impractical. Many software tools were/are developed to facilitate a more automated approach. However, these tools are generally not optimized for environmental workflows and/or only implement parts of the functionality required.
patRoon combines established software tools with novel functionality in order to provide comprehensive NTA workflows.
The different algorithms are provided through a consistent interface, which removes the need to know all the details of
each individual software tool and performing tedious data conversions during the workflow. The table below outlines the
major functionality of patRoon.
| Functionality | Description | Algorithms |
|---|---|---|
| Raw data pre-treatment | MS format conversion (e.g. vendor to mzML) and calibration. |
ProteoWizard, OpenMS, DataAnalysis |
| Feature extraction | Finding features and grouping them across analyses. | XCMS, OpenMS, enviPick, DataAnalysis, KPIC2, SIRIUS, SAFD |
| Suspect screening | Finding features with suspected presence by MS and chromatographic data. Estimation of identification confidence levels. | Native |
| MS data extraction | Automatic extraction and averaging of feature MS(/MS) peak lists. | Native, mzR, DataAnalysis |
| Formula annotation | Automatic calculation of formula candidates for features. | GenForm, SIRIUS, DataAnalysis |
| Compound annotation | Automatic (in silico) compound annotation of features. | MetFrag, SIRIUS, Native |
| Componentization & adduct annotation | Grouping of related features based on chemistry (e.g. isotopes, adducts and homologs), hierarchical clustering or MS/MS similarity into components. Using adduct and isotope annotations for prioritizing features and improving formula/compound annotations. | RAMClustR, CAMERA, nontarget R package, OpenMS, cliqueMS, Native |
| Combining algorithms | Combine data from different algorithms (e.g. features, annotations) and generate a consensus. | Native |
| Sets workflows | Simultaneous processing and combining +/- MS ionization data | Native |
| Transformation product (TP) screening | Automatic screening of TPs using library/in-silico data, MS similarities and classifications. Tools to improve compound TP annotation. | BioTransformer, PubChemLite, Native |
| Reporting | Automatic reporting in CSV, PDF and (interactive) HTML formats. An example HTML report can be viewed here. | Native |
| Data clean-up & prioritization | Filters for blanks, replicates, intensity thresholds, neutral losses, annotation scores, identification levels and many more. | Native |
| Data curation | Several graphical interactive tools and functions to inspect and remove unwanted data. | Native |
The workflow of non-target analysis typically depends on the aims and requirements of the study and the instrumentation
and methodology used for sample analysis. For this reason, patRoon does not enforce a certain workflow. Instead, most
workflow steps are optional, fully configurable and algorithms can easily be mixed or even combined.
patRoonis implemented as an R package, which allows easy interfacing with the many otherRbased MS tools and other data processing functionality fromR.- Fully open-source (GPLv3).
- Developed on Windows, Linux and macOS
- S4 classes and generics are used to implement a consistent interface to all supported algorithms.
- Continuous integration is used for automated unit testing, automatically updating the Website and documentation and maintaining a miniCRAN repository and Docker image to simplify installation (see the handbook for more details).
- Supports all major instrument vendor input formats (through usage of ProteoWizard and DataAnalysis).
- Optimizations
data.tableis used internally as a generally much more efficient alternative todata.frame.- The processx and future
Rpackages are used for parallelization. - Results from workflow steps are cached within a SQLite database to avoid repeated computations.
- Code for loading MS and EIC data, MS similarity calculations and others were implemented in
C++to reduce computational times.
- The RDCOMClient
Rpackage is used to interface with Bruker DataAnalysis algorithms. - The Shiny
Rpackage was used to implement several GUI tools.
patRoon itself can be installed as any other R package, however, some additional installation steps are needed to
install its dependencies. Alternatively, R Studio based Docker images are available to easily deploy a
complete patRoon environment. Please see the installation section in the handbook for more
information.
For a very quick start:
library(patRoon)
newProject()The newProject() function will pop-up a dialog screen (requires R Studio), which will allow you to quickly
select the analyses and common workflow options to subsequently generate a template R processing script.
However, for a better guide to get started it is recommended to read the tutorial. Afterwards the handbook is a
recommended read if you want to know more about advanced usage of patRoon. Finally, the reference outlines all the
details of the patRoon package.
When you use patRoon please cite its publications:
Rick Helmus, Thomas L. ter Laak, Annemarie P. van Wezel, Pim de Voogt and Emma L. Schymanski. patRoon: open source software platform for environmental mass spectrometry based non-target screening. Journal of Cheminformatics 13, 1 (2021)
Rick Helmus, Bas van de Velde, Andrea M. Brunner, Thomas L. ter Laak, Annemarie P. van Wezel and Emma L. Schymanski. patRoon 2.0: Improved non-target analysis workflows including automated transformation product screening. In preparation
patRoon builds on many open-source software tools and open data sources. Therefore, it is important to also cite their
work when using these algorithms via patRoon.
For bug reports, code contributions (pull requests), questions, suggestions and general feedback please use the GitHub page.