Spectral Cruncher: A Visualization Tool Integrating Manual Curation, Ion-Intensity Prediction, and De Novo Tag...

Area of Science:

• Proteomics
• Computational Biology
• Bioinformatics

Background:

• Manual curation of proteomic tandem mass spectra is time-consuming.
• Bridging manual curation and computational analysis is crucial for efficient proteomics research.
• Existing computational tools may lack instrument-specific accuracy and interactive visualization.

Purpose of the Study:

• To introduce Spectral Cruncher, an interactive extension for the PatternLab for Proteomics platform.
• To integrate de novo sequence tag extraction, automated spectral annotation, targeted tag search, and a fragment-ion intensity predictor (SpecFormer).
• To provide instrument-specific visualization and accurate intensity prediction for proteomic data.

Main Methods:

• Integration of de novo sequence tag extraction and automated spectral annotation.
• Implementation of a customized transformer-based fragment-ion intensity predictor (SpecFormer).
• Training SpecFormer on multiple datasets (Q-Exactive+, Astral bulk, Astral single-cell) for instrument-specific models.

Main Results:

• SpecFormer achieved high predictive performance with average cosine similarities of ~0.98 (Q-Exactive+ bulk), ~0.91 (Astral bulk), and ~0.87 (Astral single-cell).
• Accurate intensity prediction was achieved even with sparse fragmentation and low signal-to-noise ratios.
• The integrated tools facilitate interrogation of ambiguous spectra and validation of peptide identifications.

Conclusions:

• Spectral Cruncher and SpecFormer offer a unified graphical environment for interactive proteomic data analysis.
• The tools improve the accuracy and efficiency of peptide identification and spectral interpretation.
• Freely available tools lower technical barriers, promoting expert-driven workflows and learning in proteomics.