SpecQuality: A Tool for Reliable Spectral Quality Assessment in Proteomics and Proteogenomics

Area of Science:

• Proteomics and Proteogenomics
• Computational Biology
• Biomarker Discovery

Background:

• Proteogenomics integrates genomics and mass spectrometry (MS) data for biological insights.
• High-throughput MS data presents computational challenges, leading to unassigned spectra and potential false positives.
• Accurate assessment of MS/MS spectral quality is crucial for reliable peptide identification.

Purpose of the Study:

• To develop and evaluate SpecQuality, an accessible tool for MS/MS spectral quality assessment in proteogenomics.
• To implement a machine learning model for predicting spectral quality scores (SQS) based on spectral features.
• To enhance the reliability of peptide spectrum matches (PSMs) and reduce false-positive identifications in proteogenomic analyses.

Main Methods:

• Evaluation of ten spectral features for MS/MS quality assessment.
• Development of a machine learning model utilizing top features to predict spectral quality scores (SQS).
• Application and validation of SpecQuality on large-scale proteogenomic datasets, including Alzheimer's disease data.

Main Results:

• SpecQuality successfully predicted MS/MS spectral quality, highlighting high-quality spectra in proteogenomic searches.
• High spectral quality was associated with the identification of novel, variant, and modified peptides.
• The tool demonstrated effectiveness in mitigating false positives when compared to manually curated variant identifications.
• SpecQuality successfully highlighted high-quality spectra in Alzheimer's disease datasets, comprising ~2.7 million spectra.

Conclusions:

• SpecQuality provides a scalable and accessible approach to spectral prioritization, enhancing data integrity in proteomics and proteogenomics.
• The tool facilitates rapid prioritization of high-quality PSMs, reducing false positives and improving the reliability of results.
• SpecQuality is an open-source, versatile tool that can be integrated into various proteomics and bioinformatics pipelines.