Chapter 3 Software Options for the Analysis of MS-Proteomic Data Avinash Yadav, Federica Marini, Alessandro Cuomo, and Tiziana Bonaldi Abstract Mass spectrometry (MS)-based proteomics is currently the most successful approach to measure and compare peptides and proteins in a large variety of biological samples. Modern mass spectrometers, equipped with high-resolution analyzers, provide large amounts of data output. This is the case of shotgun/bottom-up proteomics, which consists in the enzymatic digestion of protein into peptides that are then measured by MS-instruments through a data dependent acquisition (DDA) mode. Dedicated bioinformatic tools and platforms have been developed to face the increasing size and complexity of raw MS data that need to be processed and interpreted for large-scale protein identification and quantification. This chapter illustrates the most popular bioinformatics solution for the analysis of shotgun MS-proteomics data. A general description will be provided on the data preprocessing options and the different search engines available, including practical suggestions on how to optimize the parameters for peptide search, based on hands-on experience. Keywords Mass spectrometry, Shotgun proteomics, Software, Algorithms, Protein identification, Protein quantification, Databases 1 Introduction Mass spectrometry (MS)-based proteomics is a high-throughput approach to achieve reliable and robust identification and quantifi- cation of thousands of proteins from a single biological sample. The most popular MS-proteomic workflow uses a bottom-up approach, whereby proteins are digested into peptides, separated by reversed phase liquid-chromatography (RP-LC), and transferred into the MS analyzer through an electrospray ionization (ESI) source. The MS instrument separates intact peptide ions (precursors) by their mass-to-charge ratio that is recorded in an MS1 spectrum [1]; peptide ions are then isolated and dissociated into structurally informative fragments, which are recorded in the MS2 (MS/MS) Daniela Cecconi (ed.), Proteomics Data Analysis, Methods in Molecular Biology, vol. 2361, https://doi.org/10.1007/978-1-0716-1641-3_3, © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2021 Avinash Yadav and Federica Marini contributed equally to this work. 35