The most widespread model for PMF databases is Codd's relational database, since they enable very efficient storage and retrieval of data. Relational databases use tables to store data and Structured Query Language (SQL) to access the data. More recent considerations do also include NoSQL databases and Cloud-based solutions, however, the community is divided as to whether significant performance increases will be achieved by abandoning SQL in general. A widely accepted flagship database is MASCOT, a database offered by Matrix Science. MASCOT is a relational database, which contains information on proteins coming from a variety of organisms, ranging from hepatitis C virus to Homo sapiens. The data is comprised of two main streams, one being multiple experimental datasets from different laboratories, whereas rigorous and thorough examination and curation of the data is performed regularily. The other stream of data is much more scalable, since it involves the in silico-digestion of proteins that are generated by leveraging modern bioinformatics and in silico transcriptomics, where fully sequenced genomes, such as the popular genome assembly GRCh38 are transcribed and translated into proteins. The proteins are then digested by computational models of different endoproteases and the peptides are assigned their respective masses. The MASCOT server can be accessed online, where 20 different proteases can be selected, as well as a diverse variety of post-translational modifications and missed cleavages. Then, the simple list of MS peaks is to be submitted to MASCOT, and a database search can be initiated. If some matches are registered (with respective tolerance), an intricate scoring algorithm will curate the possible proteins based on multiple statistical models, where each protein gets assigned a score, which evaluates the significance and thus propability of the matched peptides coming from the corresponding protein (Figures ).