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MARLOWE: An Untargeted Proteomics, Statistical Approach to Taxonomic Classification for Forensics
Fanny Chu1, Sarah C Jenson1, Anthony S Barente1,2
1Chemical & Biological Signatures Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.
Journal of Proteome Research
|February 3, 2025
Summary
MARLOWE software aids in identifying the source of unknown protein samples using de novo peptide identification. This tool accurately characterizes forensically relevant samples, demonstrating broad utility in taxonomic profiling.
Area of Science:
- Proteomics
- Bioinformatics
- Forensic Science
Background:
- Traditional proteomics focuses on known sample origins.
- Attributing sample origin is crucial in environmental, clinical, archaeological, and forensic proteomics.
- Existing tools often rely on database searches and organism-specific peptides.
Purpose of the Study:
- To evaluate the utility of the MARLOWE software for taxonomic characterization of unknown samples.
- To assess MARLOWE's performance on forensically relevant samples using publicly available proteomics data.
Main Methods:
- Utilized MARLOWE software for taxonomic characterization based on de novo peptide identification.
- Employed signal-erosion-resistant strong peptides, which are shared and taxonomy-dependent.
- Analyzed publicly available proteomics data from forensically relevant samples.
Main Results:
- MARLOWE successfully characterized samples, providing ranked lists of potential contributors and taxonomic scores.
- Correct characterization rates ranged from 44% to 100%, influenced by sample type and data quality.
- Demonstrated accurate identification of true contributors, close relatives, and differentiation of microbial species.
Conclusions:
- MARLOWE effectively provides taxonomic insights into diverse sample types without prior assumptions.
- The software shows significant potential for applications in forensic science.
- MARLOWE's approach is broadly applicable to bioanalytical studies requiring proteomic taxonomic characterization.