DiagnoTop
DiagnoTop identifies and differentiates bacterial pathogens from top-down proteomics (TDP) data by detecting discriminative proteoform spectral clusters without relying on database searches.
Key Features:
- Database-free identification: Operates without database searches to enable analysis of poorly annotated microbial proteomes.
- Top-down proteomics input: Processes top-down proteomics (TDP) mass spectra and TDP datasets, including enterobacterial TDP datasets.
- Proteoform-focused analysis: Detects species-unique proteoforms and uses them as discriminative signals.
- Discriminative spectral cluster detection: Identifies, lists, and shortlists discriminative spectral clusters and high-quality spectra.
- Differentiation of closely related enterobacteria: Resolves species-level ambiguity among Escherichia coli, Shigella, and Salmonella using discriminative clusters.
- Diagnostic and biomarker potential: Enhances diagnostic power and supports biomarker discovery from TDP-derived proteoform signatures.
Scientific Applications:
- Clinical pathogen identification: Confirming bacterial infections to inform antimicrobial therapy decisions.
- Differentiation of enterobacterial pathogens: Resolving taxonomic ambiguity among Escherichia coli, Shigella, and Salmonella in mass-spectrometry-based analyses.
- Biomarker discovery: Identifying species-specific proteoform signatures from top-down proteomics data.
- Analysis of poorly annotated proteomes: Enabling proteomic studies where reference databases are incomplete or lacking annotations.
Methodology:
Analyzes top-down proteomics (TDP) mass spectra to detect, list, and shortlist discriminative spectral clusters and proteoforms without performing database searches.
Topics
Details
- Tool Type:
- desktop application, workflow
- Added:
- 9/8/2021
- Last Updated:
- 9/13/2021
Operations
Publications
Borges Lima D, Dupré M, Mariano Santos MD, Carvalho PC, Chamot-Rooke J. DiagnoTop: A Computational Pipeline for Discriminating Bacterial Pathogens without Database Search. Journal of the American Society for Mass Spectrometry. 2021;32(6):1295-1299. doi:10.1021/jasms.1c00014. PMID:33856212.
PMID: 33856212
Funding: - Agence Nationale de la Recherche: ANR-15-CE18-0021
- H2020 Research Infrastructures: 823839
- H2020 Health: 773830