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While reliable identification of post-translational modifications (PTMs) is key to understanding various cellular regulatory processes, adequate configuration of existing tools remains a challenging task. In this context, it becomes crucial to carefully assess the accuracy of identification of expected modifications. We discuss methods to quantify the false discovery rate from a search and illustrate how several features can be used to distinguish valid modifications from search artifacts. In addition, we describe two open-source unrestrictive PTM search algorithms, MS-Alignment and Spectral Networks, that search for all types of PTMs at once in blind mode (i.e. without knowing in advance which PTMs to search for). While MS-Alignment capitalizes on spectral alignment to identify modified variants of peptides in a database, Spectral Networks directly align unidentified spectra to discover PTMs and highly-modified peptides. InsPecT, MS-Alignment and Spectral Networks are freely available as open-source packages and web services at http://proteomics.ucsd.edu.