GeenaR: A Web Tool for Reproducible MALDI-TOF Analysis

Eugenio Del Prete, Angelo Facchiano, Aldo Profumo, Claudia Angelini, Paolo Romano

Research output: Contribution to journalArticlepeer-review


Mass spectrometry is a widely applied technology with a strong impact in the proteomics field. MALDI-TOF is a combined technology in mass spectrometry with many applications in characterizing biological samples from different sources, such as the identification of cancer biomarkers, the detection of food frauds, the identification of doping substances in athletes’ fluids, and so on. The massive quantity of data, in the form of mass spectra, are often biased and altered by different sources of noise. Therefore, extracting the most relevant features that characterize the samples is often challenging and requires combining several computational methods. Here, we present GeenaR, a novel web tool that provides a complete workflow for pre-processing, analyzing, visualizing, and comparing MALDI-TOF mass spectra. GeenaR is user-friendly, provides many different functionalities for the analysis of the mass spectra, and supports reproducible research since it produces a human-readable report that contains function parameters, results, and the code used for processing the mass spectra. First, we illustrate the features available in GeenaR. Then, we describe its internal structure. Finally, we prove its capabilities in analyzing oncological datasets by presenting two case studies related to ovarian cancer and colorectal cancer. GeenaR is available at

Original languageEnglish
Pages (from-to)635814
JournalFrontiers in Genetics
Publication statusPublished - Mar 29 2021


  • cancer analysis
  • mass spectrometry
  • proteomics
  • reproducible research
  • web tool

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Genetics(clinical)


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