SeekNSee: A Rule-Based Framework for Prioritizing Proteotypic Peptides

SeekNSee, a locally run R/Shiny framework, generates proteotypic peptide candidates for human proteins with 733,864 candidates and preserves rule-level decision traces.

• Why it matters: Users often need reproducible peptide-candidate screening without prior experimental data, programming skills, or reliable external repositories, creating a gap in transparent, auditable selection workflows.
• What they did: SeekNSee performs enzyme-aware in silico digestion using nine configurations, then applies 12 configurable ignore/apply/block rules with independent numerical scoring and candidate-specific rule traces.
• The result: In proteome-wide processing of all 20,416 reviewed human proteins, it produced 581,026 unique peptide sequences; automated validation used 198 passing assertions and benchmarking linked score categories to peptide observation and analytical measurements.

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