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Peer-Reviewed Publication
Sci Rep2026;16(1)August 20, 2026Journal Article

MARLOWE: taxonomic characterization of unknown samples for forensics using de novo peptide identification.

Sarah C Jenson1, Fanny Chu2, Gelio Alves3, Aleksey Y Ogurtsov3, Anthony S Barente1,4, Dustin L Crockett5, Natalie C Lamar6, Eric D Merkley1, Yi-Kuo Yu3, Kristin H Jarman1,7
1Chemical and Biological Signatures Group, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
2Chemical and Biological Signatures Group, Pacific Northwest National Laboratory, Richland, WA, 99352, USA. fanny.chu@pnnl.gov.
3Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20854, USA.
4Department of Genome Sciences, University of Washington, Seattle, WA, 98194, USA.
5Applied Decisions Systems and Analytics, Group, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
6Applied Statistics and Computational Modeling Group, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
7Karius Inc., 975 Island Dr., Suite 101, Redwood City, CA, 94065, USA.
PubMed DOI

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 org…

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