DiagnoProt

DiagnoProt identifies and prioritizes unannotated mass spectra from mass spectrometry-based proteomics to enable discovery of discriminative tandem mass spectra associated with biological conditions.

Key Features:

• Integrated Computational Environment: Clusters millions of mass spectra to manage large-scale high-throughput proteomics datasets.
• Machine Learning Integration: Employs machine learning algorithms to shortlist high-quality unidentified tandem mass spectra that are discriminative of different biological conditions.
• High-Quality Spectrum Shortlisting: Filters and prioritizes spectra by quality and relevance, demonstrated by shortlisting 4,366 high-quality unidentified tandem mass spectra discriminative of Aspergillus types.

Scientific Applications:

• Biomarker Discovery: Enables identification of novel molecular entities or biomarkers from previously unannotated spectra.
• Disease and Pathogen Studies: Supports targeted investigations into disease mechanisms and pathogen differentiation by focusing on spectra indicative of specific biological states.
• Microbiology, Pathology, and Systems Biology: Facilitates studies in microbiology, pathology, and systems biology through detection of spectra discriminative of organismal or condition-specific variation.

Methodology:

Clustering of mass spectra followed by machine learning-driven analysis to identify spectra with high discriminative power.