In the last two decades, computational tools for mass spectrometry-based proteomics data analysis have evolved from a few stand-alone software solutions serving specific goals, such as the identification of amino acid sequences based on mass spectrometry spectra, to large-scale complex pipelines integrating multiple computer programs to solve a collection of problems. This software evolution has been mostly driven by the appearance of novel technologies that allowed the community to tackle complex biological problems, such as the identification of proteins that are differentially expressed in two samples under different conditions. The achievement of such objectives requires a large suite of programs to analyze the intricate mass spectrometry data. Our laboratory addresses complex proteomics questions by producing and using algorithms and software packages. Our current computational pipeline includes, among other things, tools for mass spectrometry raw data processing, peptide and protein identification and quantification, post-translational modification analysis, and protein functional enrichment analysis. In this paper, we describe a suite of software packages we have developed to process mass spectrometry-based proteomics data and we highlight some of the new features of previously published programs as well as tools currently under development. Graphical Abstract ᅟ.
Keywords: Algorithms; Bioinformatics; Computational biology; Database; Functional enrichment analysis; Peptide identification; Protein identification; Proteomics; Quantitative proteomics; Statistics.