Oktoberfest

Oktoberfest generates in silico spectral libraries and rescoring data for proteomics by applying machine learning and deep learning models (e.g., Prosit) to predict peptide properties.

Key Features:

• Search Engine Agnosticism: Compatible with outputs from different peptide-spectrum matching search engines to enable independent downstream rescoring.
• Peptide Property Prediction: Integrates online peptide property predictions using ML/DL models such as Prosit to obtain predicted fragment intensities and properties.
• Spectral Library Generation: Produces in silico reference spectral libraries based on predicted peptide properties for use in library-based proteomic analyses.
• Enhanced Rescoring Capabilities: Applies predicted peptide properties to rescore search engine results and can reproduce or enhance previously published rescoring analyses.

Scientific Applications:

• Data-Independent Acquisition (DIA) Analysis: Supports generation of accurate reference libraries for DIA workflows to improve peptide detection and quantification.
• Rescoring Search Engine Results: Refines peptide-spectrum match scores to increase reliability of protein identification and quantification.

Methodology:

Uses ML/DL models (e.g., Prosit) to predict peptide properties that are used to generate in silico spectral libraries and to rescore search engine results.