proteomics

Differential protein abundance analysis requires a protein or peptide result table and sample metadata. The skill processes MS-derived data through QC, normalization, and comparative statistics to identify differentially abundant proteins across sample groups.

Yes, the skill supports DDA, DIA, and PTM-enriched workflows. It applies assay-specific QC and normalization across peptide- and protein-level outputs, enabling comparative analysis regardless of the mass spectrometry acquisition method.

Proteomics QC requires a protein or peptide result table, sample metadata, and an assay context indicating DDA, DIA, or PTM-enriched. The skill then performs normalization and quality control to produce reliable protein-level results.

The skill addresses missingness and inconsistent quantification through end-to-end QC and normalization. It processes MS-derived data to deliver reliable protein-level results, mitigating common quality issues that hinder interpretation of mass spectrometry proteomics data.

The skill exports protein tables, QC summaries, and publication-ready figures. These structured outputs follow a folder layout and include differential abundance results alongside comparative statistics from the mass spectrometry proteomics analysis.

Yes, PTM-enriched workflows are explicitly supported alongside DDA and DIA. The skill applies quality control, normalization, and differential abundance analysis to PTM-enriched data at both peptide and protein levels.