PeptideWitch

PeptideWitch transforms low-stringency peptide-to-spectrum matching-based label-free shotgun proteomics identifications into high-stringency spectral-count-based datasets for quantitative comparison between control and treated samples.

Key Features:

• Data Input and Processing: Accepts low-stringency protein identification lists derived from peptide-to-spectrum matching search engines corresponding to control and treated samples.
• High-Stringency Data Output: Produces high-stringency outputs by filtering and consolidating identifications to retain reliable protein entries for quantitation.
• Spectral Counting and Normalization: Normalizes spectral abundance factors and sums spectral counts to support quantitative analyses.
• Inner Join Filtering: Applies inner joins to refine datasets so that only proteins present across compared samples are retained for analysis.
• Data Quality Metrics: Generates comprehensive data quality metrics to assess dataset integrity and robustness.
• Statistical Analyses and Graphical Representations: Provides statistical tools and graphical representations to visualize differences between sample proteomes.
• Implementation and Platform: Implemented in Python and builds upon the Scrappy software platform.

Scientific Applications:

• Label-free quantitative proteomics: Facilitates comparison of protein expression levels across biological samples or experimental conditions using spectral counts.
• Differential protein identification: Supports identification of significant proteomic changes for investigations into disease mechanisms and drug responses.

Methodology:

The methodology integrates spectral counting data from control and treated samples, normalizes spectral abundance factors and sums spectral counts, and applies inner joins to retain high-confidence protein identifications.