Summary: ADViSELipidomics is a novel Shiny app for preprocessing, analyzing and visualizing lipidomics data. Ithandles the outputs from LipidSearch and LIQUID for lipid identification and quantification and the data fromthe Metabolomics Workbench. ADViSELipidomics extracts information by parsing lipid species (using LIPID MAPSclassification) and, together with information available on the samples, performs several exploratory and statisticalanalyses. When the experiment includes internal lipid standards, ADViSELipidomics can normalize the data matrix,providing normalized concentration values per lipids and samples. Moreover, it identifies differentially abundantlipids in simple and complex experimental designs, dealing with batch effect correction. Finally, ADViSELipidomicshas a user-friendly graphical user interface and supports an extensive series of interactive graphics.

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 http://proteomics.hsanmartino.it/geenar/.

Mass spectrometry is a set of technologies with many applications in characterizing biological samples. Due to the huge quantity of data, often biased and contaminated by different source of errors, and the amount of results that is possible to extract, an easy-to-learn and complete workflow is essential. GeenaR is a robust web tool for pre-processing, analysing, visualizing and comparing a set of MALDI-ToF mass spectra. It combines PHP, Perl and R languages and allows different levels of control over the parameters, in order to adapt the work to the needs and expertise of the users.