DiagnoMass

DiagnoMass identifies discriminative spectral clusters from shotgun proteomic datasets and classifies proteomic profiles to enable discovery of peptides and proteins associated with biological conditions.

Key Features:

• Spectral Clustering: Groups similar MS/MS spectra into clusters to reveal recurrent spectral patterns within complex proteomic data.
• Discriminative Cluster Shortlisting: Shortlists spectral clusters that demonstrate significant discrimination between different biological states or conditions.
• Integration with Proteomic Tools: Integrates cluster outputs with peptide-spectrum matching, de novo sequencing, open searches, and hybrid methods to aid peptide and protein identification.
• Proteomic Profile Classification: Classifies entire proteomic profiles based on discriminative clusters to contextualize molecular differences and support biomarker discovery.

Scientific Applications:

• Biomarker discovery: Identifies candidate peptides and proteins by detecting discriminative spectral clusters associated with specific conditions.
• Bacterial proteome analysis: Applied to proteomic data from Escherichia coli, Salmonella, and Shigella to identify high-quality discriminative spectral clusters not found by widely adopted proteomic tools.
• Characterization of unidentified spectra: Enables analysis of spectral clusters that remain unassigned by peptide-spectrum matching, de novo sequencing, open searches, and hybrid methods.

Methodology:

Groups similar spectra into clusters, applies a shortlisting procedure to select discriminative clusters, and integrates cluster outputs with peptide-spectrum matching, de novo sequencing, open searches, and hybrid methods.