12Most clinical drugs are based on microbial natural products, with compound classes including 13 polyketides (PKS), non-ribosomal peptides (NRPS), fluoroquinones and ribosomally synthesized and 14 post-translationally modified peptides (RiPPs). While variants of biosynthetic gene clusters (BGCs) for 15 known classes of natural products are easy to identify in genome sequences, BGCs for new 16 compound classes escape attention. In particular, evidence is accumulating that for RiPPs, subclasses 17 known thus far may only represent the tip of an iceberg. Here, we present decRiPPter (Data-driven 18Exploratory Class-independent RiPP TrackER), a RiPP genome mining algorithm aimed at the 19 discovery of novel RiPP classes. DecRiPPter combines a Support Vector Machine (SVM) that identifies 20candidate RiPP precursors with pan-genomic analyses to identify which of these are encoded within 21 operon-like structures that are part of the accessory genome of a genus. Subsequently, it prioritizes 22 such regions based on the presence of new enzymology and based on patterns of gene cluster and 23 precursor peptide conservation across species. We then applied decRiPPter to mine 1,295 24Streptomyces genomes, which led to the identification of 42 new candidate RiPP families that could 25 not be found by existing programs. One of these was studied further and elucidated as a novel 26 subfamily of lanthipeptides, designated Class V. Two previously unidentified modifying enzymes are 27proposed to create the hallmark lanthionine bridges. Taken together, our work highlights how novel 28 natural product families can be discovered by methods going beyond sequence similarity searches to 29 integrate multiple pathway discovery criteria. 30 31 Code and data availability 32The source code of DecRiPPter is freely available online at https://github.com/Alexamk/decRiPPter. 33Results of the data analysis are available online at 34