MARLOWE: Taxonomic Characterization of Unknown Samples for Forensics Using De Novo Peptide Identification

preprint · bioRxiv · 2025

preprint · bioRxiv · 2025. Sarah C. Jenson et al. We present a computational tool, MARLOWE, for source organism characterization of unknown, forensic…
Date 2025-06-02
Type preprint
Venue bioRxiv
Publisher Cold Spring Harbor Laboratory
Contribution downstream-application
DOI 10.1101/2024.09.30.615220
Citations (OpenAlex) 4

Abstract

We present a computational tool, MARLOWE, for source organism characterization of unknown, forensic biological samples. The intent of MARLOWE is to address a gap in applying proteomics data analysis to forensic applications. MARLOWE produces a list of potential source organisms given confident peptide tags derived from de novo peptide sequencing and a statistical approach to assign peptides to organisms in a probabilistic manner, based on a broad sequence database. In this way, the algorithm assumes no a priori knowledge of potential sources, and the probabilistic way peptides are taxonomically assigned and then scored enables results to be unbiased (within the constraints of the sequence database). In a proof-of-concept study, we examined MARLOWEs performance on two datasets, the Biodiversity dataset and the Bacillus cereus superspecies dataset. Not only did MARLOWE demonstrate successful characterization to true contributors in single source and binary mixtures in the Biodiversity dataset, but also provided sufficient specificity to distinguish species within a bacterial superspecies group. We also compared MARLOWEs results to those of MiCId, a leading microbial identification/characterization tool based on proteomics database search. Comparison of the two tools using 225 mass spectrometry data files yielded comparable performance, with slightly higher accuracy and specificity for MiCId. At the species level, MARLOWE achieved a specificity of 91.4% at 5% FDR. These results suggest that MARLOWE is suitable for candidate- or lead-generation identification of single-organism and binary samples that can generate forensic leads and aid in selecting appropriate follow-on analyses in a forensic context.

Authors

  1. Sarah C. Jenson · Pacific Northwest National Laboratory
  2. Fanny Chu · Pacific Northwest National Laboratory
  3. Gelio Alves · National Center for Biotechnology Information, National Institutes of Health
  4. Aleksey Y. Ogurtsov · National Institutes of Health
  5. Anthony S. Barente · Pacific Northwest National Laboratory, University of Washington
  6. Dustin L. Crockett · Pacific Northwest National Laboratory
  7. Natalie C. Heller-Lamar · Pacific Northwest National Laboratory
  8. Eric D. Merkley · Pacific Northwest National Laboratory
  9. Yi-Kuo Yu · National Center for Biotechnology Information, National Institutes of Health
  10. Kristin H. Jarman · Karius Inc., Pacific Northwest National Laboratory

Methods and tools

  • MARLOWE: Computational tool that takes de novo-sequenced peptides from mass spectra of an unknown biological sample and returns a ranked, probabilistic list of source organisms, aimed at forensic biothreat and biodiversity casework.

Data used

  • Identification of type strain-specific diagnostic peptides of the Bacillus cereus group (as deposited) · PXD003669
  • PNNL Biodiversity Library - Microbial Section (as deposited) · MSV000079053
  • PXD001860 (as deposited) · PXD001860

Cites (11)

Cited by (2)

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