Academic Journal
MARLOWE: taxonomic characterization of unknown samples for forensics using de novo peptide identification.
| Τίτλος: | MARLOWE: taxonomic characterization of unknown samples for forensics using de novo peptide identification. |
|---|---|
| Συγγραφείς: | Jenson SC; Chemical and Biological Signatures Group, Pacific Northwest National Laboratory, Richland, WA, 99352, USA., Chu F; Chemical and Biological Signatures Group, Pacific Northwest National Laboratory, Richland, WA, 99352, USA. fanny.chu@pnnl.gov., Alves G; Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20854, USA., Ogurtsov AY; Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20854, USA., Barente AS; Chemical and Biological Signatures Group, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.; Department of Genome Sciences, University of Washington, Seattle, WA, 98194, USA., Crockett DL; Applied Decisions Systems and Analytics, Group, Pacific Northwest National Laboratory, Richland, WA, 99352, USA., Lamar NC; Applied Statistics and Computational Modeling Group, Pacific Northwest National Laboratory, Richland, WA, 99352, USA., Merkley ED; Chemical and Biological Signatures Group, Pacific Northwest National Laboratory, Richland, WA, 99352, USA., Yu YK; Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20854, USA., Jarman KH; Chemical and Biological Signatures Group, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.; Karius Inc., 975 Island Dr., Suite 101, Redwood City, CA, 94065, USA. |
| Πηγή: | Scientific reports [Sci Rep] 2026 Aug 20; Vol. 16 (1). Date of Electronic Publication: 2026 Aug 20. |
| Τύπος έκδοσης: | Journal Article; Research Support, U.S. Gov't, Non-P.H.S. |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| Ιατρικοί όροι (MeSH): | Peptides*/analysis , Proteomics*/methods , Forensic Sciences*/methods , Computational Biology*/methods , Software*, Bacillus cereus/classification ; Algorithms ; Biodiversity |
| Περίληψη: | 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. Except for the constraints of this underlying broad sequence database, 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 MARLOWE's 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 MARLOWE's 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. (© 2026. Battelle Memorial Institute. Parts of this work were authored by US Federal Government authors and are not under copyright protection in the US; foreign copyright protection may apply.) |
| Competing Interests: | Declarations. Competing interests: The authors declare no competing interests. |
| Σχόλια: | Update of: bioRxiv. 2025 Jun 02:2024.09.30.615220. doi: 10.1101/2024.09.30.615220.. (PMID: 40501933) |
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| Grant Information: | HSHQPM16X00216 U.S. Department of Homeland Security |
| Contributed Indexing: | Keywords: De novo sequence tags; Forensic proteomics; Mass spectrometry; Metaproteomics; Strong peptides |
| Substance Nomenclature: | 0 (Peptides) |
| Entry Date(s): | Date Created: 20260820 Date Completed: 20260820 Latest Revision: 20260825 |
| Update Code: | 20260825 |
| PubMed Central ID: | PMC13494011 |
| DOI: | 10.1038/s41598-026-50102-3 |
| PMID: | 42624859 |
| Βάση Δεδομένων: | MEDLINE |
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