Detected vs Inferred Proteins per Sample
Precursor Evidence Heatmap (Top 50 Most Variable)
Cumulative Detection Curve
Sample Clustering by Detection Pattern (Jaccard Distance)
Precursor Count per Protein (per Sample)
Per-Group Replicate Statistics
Contaminant Protein Analysis
Per-Sample Contaminant Intensity
Top Contaminant Proteins
Contaminant Expression Heatmap (Top 20)
Data Explorer
Export for ClaudeAbundance Profiles (Quartile Analysis)
Variable Proteins (Quartile Range >= 2)
Sample-Sample Scatter
AI-Powered Analysis Summary
How it works: Click the button below to generate a comprehensive AI-powered analysis across all comparisons in your experiment.
The AI will identify key DE proteins per comparison, cross-comparison biomarkers, and provide biological insights on high-confidence candidates.
Configure Comparison
report_log.txt
or the SLURM
.out
file from each DIA-NN search.
Only the command line and summary stats are read.
AI-Powered Comparison Analysis
Generate an AI narrative summary or export data for external analysis.
MOFA2 Factor Decomposition
Treats Run A and Run B as two views of the same samples and decomposes joint variance into shared and run-specific factors.
Note: QC Stats (with Groups) + Top 800 Expression Data are sent to AI.
Load existing DDA/de novo results from HPC
Manuscript Summary Statistics (Table 1)
Species Resolution
Delta = (best species identity %) minus (second-best species identity %). Peptides right of the dashed line (delta > 15%) are species-diagnostic and can be used for species identification. Peptides left of the line are conserved across species.
Top Proteins by De Novo Peptide Count
Taxonomic Coverage
Identity of each peptide across the top species, grouped by source protein. Reveals patterns like conserved vs species-specific protein regions.
Show full peptide-species identity matrix
Peptide Length Distribution
Charge State Distribution
Post-Translational Modifications
Modification analysis from de novo sequences. In paleoproteomics, high N-deamidation with low Q-deamidation indicates genuine ancient protein (time-dependent asparagine degradation).
Select a near-match peptide from the table below, then click Show Alignment to visualize mismatches with per-residue confidence. Green = genuine variant (AA score > 0.95), Red = possible sequencing error (AA score < 0.70).
This view cross-references BLAST mismatches with Casanovo's per-residue amino acid confidence scores to distinguish species-specific markers from sequencing artifacts.
Target-decoy approach: reversed peptide sequences are BLASTed against the same SwissProt database. At each identity threshold, FDR = reversed_hits / forward_hits. This is the best available FDR proxy for de novo sequencing without a database.
Cross-species comparison of de novo peptides. Requires BLAST results from multiple samples (e.g., feather samples from different bird species).
Per-Protein Cross-Sample Comparison
Protein family classification of BLAST hits. Groups proteins into biological families (keratins, collagens, histones, etc.) for interpretation.
Protein sequence coverage maps for top 20 proteins by peptide count. Green = confirmed (100% identity), Orange = near-match (90-99%), Red = distant (<90%).
Peptide-Protein Mapping Details
Submit De Novo Sequencing Job
Run de novo sequencing on raw files. Cascadia for DIA data, Casanovo for DDA data.
Export Complete Analysis
Download everything needed to reproduce and share this analysis. Includes all data files, DIA-NN search parameters, and session state.
What's included (click to expand)
- expression_matrix.csv -- Normalized protein intensities (DPC-Quant, complete, no missing values)
- diann_pg_matrix.tsv -- DIA-NN protein-level matrix with real missing values (0 = not detected, ~200 KB)
- data_quality_summary.csv -- Per-sample protein counts, % detected, contaminant counts
- detection_matrix.csv -- Per-protein precursor detection counts per sample
- quartile_profiles.csv -- Intensity quartile assignments per sample
- variable_proteins.csv -- Proteins with inconsistent abundance across samples
- sample_metadata.csv -- Sample groups and identifiers
- contaminant_summary.csv -- Contaminant protein statistics
- search_info.md -- Full DIA-NN search parameters and job metadata
- session.rds -- Complete session state (reload in DE-LIMP)
- methods.txt -- Pipeline parameters, normalization, app version
- reproducibility_log.R -- R code log to reproduce every step
- PROMPT.md -- AI analysis prompt with biological questions
DE Results Table
Quick export of the DE results for the selected comparison. Includes gene symbols, logFC, P.Value, adj.P.Val, and per-sample expression values. One CSV file — no search parameters or session data.
Export Results CSVCV Analysis
Coefficient of variation for significant proteins. Includes per-group CV and average CV values. One CSV file.
Export CV Analysis CSVFull DIA-NN Output
The complete DIA-NN search output (report.parquet, precursor matrices, spectral libraries, logs) is stored on the HPC cluster. These files can be large (100 MB+) and are not included in the analysis export.
- Action name - what you did (e.g., 'Run Pipeline')
- Timestamp - when you did it
- R code - how to reproduce it
Copy this entire code block to reproduce your analysis in a fresh R session.
DE-LIMP
Differential Expression — LIMPA Pipeline
Explore video tutorials, training courses, and methodology citations.