Table 3.
R packages for MS/MS data.
| Functionalities | Package | Reference | Repos |
|---|---|---|---|
| MS/MS and Libraries | |||
| Tools for processing raw data to database ready cleaned spectra with metadata. | RMassBank | [110] | BioC |
| From RT-m/z pairs (or m/z alone) creates MS/MS experiment files with non-overlapping subsets of the targets. Bruker, Agilent and Waters supported. | MetShot | [111] | GitHub |
| Creating MS libraries from LC-MS data using xcms/CAMERA packages. A multi-modular annotation function including X-Rank spectral scoring matches experimental data against the generated MS library. | MatchWeiz | [107] | GitHub |
| Assess precursor contribution to fragment spectrum acquired or anticipated isolation windows using “precursor purity” for both LC-MS(/MS) and DI-MS(/MS) data. Spectral matching against a SQLite database of library spectra. | msPurity | [112] | BioC |
| Automated quantification of metabolites by targeting mass spectral/retention time libraries into full scan-acquired GC-MS chromatograms. | baitmet | [113] | CRAN |
| MS/MS spectra similarity and unsupervised statistical methods. Workflow from raw data to visualisations and is interfaceable with xcms. | CluMSID | [114] | BioC |
| Import of spectra from different file formats such as NIST msp, mgf (mascot generic format), and library (Bruker) to MSnbase objects. | MSnio | GitHub | |
| Multi-purpose mass spectrometry package. Contains many different functions e.g., isotope pattern calculation, spectrum similarity, chromatogram plotting, reading of msp files and peptide related functions. | OrgMassSpecR | CRAN | |
| Annotation of LC-MS data based on a database of fragments. | MetaboList | [115] | CRAN |
| In Silico Fragmentation | |||
| In silico fragmentation of candidate structures. | MetFragR | [116] | GitHub |
| SOLUTIONS for High ReSOLUTION Mass Spectrometry including several functions to interact with MetFrag, developed during the SOLUTIONS project (www.solutions-project.eu). | ReSOLUTION | [116] | GitHub |
| Uses MetFrag and adds substructure prediction using the isotopic pattern. Can be trained on a custom dataset. | CCC | [117] | GitHub |
| Retention Time Correction | |||
| Retention time prediction based on compound structure descriptors. Five different machine learning algorithms are available to build models. Plotting available to explore chemical space and model quality assessment. | Retip | GitHub | |