Related to the issues in metaproteomics, forensic samples present unique challenges due to their unknown origin, complicating the identification of the appropriate protein sequence database-similar to studying an organism with an unsequenced genome in microbial communities. Jenson et al. developed MARLOWE , a computational tool that addresses this by characterizing source organisms in unknown forensic samples. It uses PEAKS, Novor, or Casanovo for de novo sequencing, performs peptide tag extraction and filtering by peptide strength (as defined by Jarman et al. previously ), assigns tags to taxonomic sources, adjusts counts for tags shared between organisms, and finally scores source organisms using non-negative least squares regression. Tested on biodiversity and Bacillus cereus datasets, MARLOWE successfully identified true contributors in single-source and binary mixtures and distinguished species within a bacterial group, demonstrating its potential for generating forensic leads and aiding follow-up analyses.