Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has transformed microbiology by enabling rapid and cost-effective pathogen identification. However, differentiating between closely related species remains challenging. Current analytical approaches, such as database-driven approaches, still underutilize the information contained in mass spectra. In response to this challenge, we developed MSclassifR, an R package designed to facilitate the construction of data analysis pipelines for accurate mass spectra classification using machine learning (ML) techniques. MSclassifR provides end-to-end pipelines tailored for microbiological diagnostics, covering preprocessing, mass-to-charge ( m / z ) selection, and classification of mass spectra. One of the main strengths of the package is its m / z selection method based on random forest (RF) variable importance, which improves identification accuracy by focusing on the most informative spectral features. We assessed classification pipelines constructed using MSclassifR through rigorous experiments conducted on diverse datasets that included bacterial species or subspecies and virulent/avirulent phenotypes, illustrating the package’s versatility across diverse applications. Moreover, the pipelines achieved high accuracy when applied to SARS-CoV-2 nasal swab mass spectra acquired using various MALDI-TOF instruments. Comparisons of multiple pipelines revealed that RF-based pipelines achieved the best performances on various-sized datasets. The MSclassifR package offers microbiologists an open-source solution that leverages ML to enhance MALDI-TOF MS’s diagnostic capabilities. It is available for download from the Comprehensive R Archive Network ( https://cran.r-project.org/web/packages/MSclassifR/ ).

MOTIVATION: We present a high-performance software integrating shotgun with top-down proteomic data. The tool can deal with multiple experiments and search engines. Enable rapid and easy visualization, manual validation and comparison of the identified proteoform sequences including the post-translational modification characterization. RESULTS: We demonstrate the effectiveness of our approach on a large-scale Escherichia coli dataset; ProteoCombiner unambiguously shortlisted proteoforms among those identified by the multiple search engines. AVAILABILITY AND IMPLEMENTATION: ProteoCombiner, a demonstration video and user tutorial are freely available at https://proteocombiner.pasteur.fr, for academic use; all data are thus available from the ProteomeXchange consortium (identifier PXD017618). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments. DAPAR contains procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate. ProStaR is a graphical user interface that allows friendly access to the DAPAR functionalities through a web browser. AVAILABILITY AND IMPLEMENTATION: DAPAR and ProStaR are implemented in the R language and are available on the website of the Bioconductor project (http://www.bioconductor.org/). A complete tutorial and a toy dataset are accompanying the packages. CONTACT: [email protected], [email protected], [email protected].