| name | sdrf:brainstorm |
| description | Use when the user wants to plan what metadata to capture for a new experiment, or discuss experimental design and SDRF strategy before creating the file. |
| user-invocable | true |
| argument-hint | [experiment description] |
SDRF Brainstorming Workflow
You are helping the user plan their SDRF annotation BEFORE creating the file.
This is a collaborative thinking session, not a file generation task.
Step 1: Understand the Experiment
Ask the user about (if not already provided):
- What organism? (human, mouse, rat, plant, microbiome, etc.)
- What technology? (DDA, DIA, TMT, SILAC, label-free, Olink, SomaScan)
- What is being compared? (disease vs control, treatment vs untreated, time course)
- What tissue/sample type? (tissue biopsy, cell line, plasma, FFPE, etc.)
- How many samples per group?
- Is there fractionation? (high-pH RP, SAX, gel bands)
- Is there a publication or PXD to reference?
Step 2: Recommend Templates
Use the sdrf:templates decision tree to select the right combination.
Reference the 5 template layers:
- Technology (required):
ms-proteomics or affinity-proteomics
- Organism (recommended):
human, vertebrates, invertebrates, or plants
- Experiment type (if applicable):
dia-acquisition, cell-lines, single-cell, immunopeptidomics, crosslinking
- Clinical/Domain (if applicable):
clinical-metadata, oncology-metadata
- Metaproteomics (special):
metaproteomics + child (human-gut, soil, water)
Read spec/sdrf-proteomics/sdrf-templates/templates.yaml to confirm template names and current versions.
Present the recommendation:
Your experiment: [description]
Recommended templates:
1. ms-proteomics (required — mass spectrometry experiment)
2. human (organism is Homo sapiens)
3. clinical-metadata (patient samples with treatment data)
4. oncology-metadata (cancer study — adds tumor staging)
This combination requires these columns: [read from TERMS.tsv, filter by template names in usage]
And recommends these additional columns: [read from template YAMLs for recommended columns]
Read spec/sdrf-proteomics/TERMS.tsv and filter by the selected template names to list the columns.
Step 3: Search for Similar Experiments
Find reference datasets to learn from:
Search PRIDE for similar experiments:
mcp PRIDE → search_extensive(query="<keywords>")
Search publications for standard experimental designs:
mcp PubMed → search_articles(query="<keywords> AND proteomics")
Search bioRxiv for recent preprints:
mcp bioRxiv → search_preprints(category="biochemistry" or "cell biology", recent_days=180)
Present findings:
Similar datasets found:
- PXD012345: TMT phosphoproteomics of breast cancer (24 samples, 12 fractions)
- PXD023456: Label-free DIA of liver cancer tissue (30 patients)
Common design patterns in this field:
- Typical sample size: 10-30 per group
- Common labels: TMT, label-free DIA, SILAC
- Standard fractionation: 12-24 high-pH RP fractions
- Most include: age, sex, disease staging
Step 4: Recommend Metadata Columns
Present a complete column plan organized by importance:
Must Have (required by templates)
Columns required by the selected template combination.
For each: explain what it is, what ontology to use, and give examples.
Should Have (recommended for this experiment type)
Columns that 70%+ of similar experiments include.
For each: explain why it adds value.
Nice to Have (optional but valuable)
Columns that would increase reusability and findability.
For each: explain the benefit.
Factor Values
Discuss what the experimental comparison is:
- What variable is being tested?
- Are there multiple factors? (e.g., disease × treatment)
- What will the statistical comparison be?
Step 5: Discuss Design Considerations
Raise potential issues proactively:
Batch Effects
- Will all conditions be processed together or separately?
- Are label channels balanced across conditions?
- Is the instrument assignment confounded with the experimental variable?
Replication
- How many biological replicates per condition?
- Are technical replicates needed?
- Is the sample size adequate for statistical testing?
Labeling Strategy (if TMT/iTRAQ)
- How will samples be assigned to channels?
- Will there be a pooled reference channel?
- How many TMT sets are needed?
Fractionation
- How many fractions per sample?
- What method? (high-pH RP, SAX, SCX, gel)
- How does this affect the total number of SDRF rows?
Step 6: Calculate SDRF Dimensions
Help the user understand the scale:
Your SDRF will have:
Rows: [samples] × [fractions] × [label channels] × [technical replicates]
Example: 20 patients × 12 fractions × 1 (label-free) × 1 replicate = 240 rows
Example: 20 patients × 12 fractions × 10 (TMT10plex) × 1 replicate = 2,400 rows
Columns: ~15 required + ~8 recommended + factor values = ~25 columns
Step 7: Summarize the Plan
Create a clear annotation plan the user can follow:
## SDRF Annotation Plan for [experiment]
Templates: ms-proteomics + human + oncology-metadata
Rows: ~240 (20 patients × 12 fractions)
Columns: 26
Required metadata to collect:
- Patient demographics: age, sex (from clinical records)
- Diagnosis: specific cancer subtype (from pathology)
- Tumor staging: TNM stage, grade (from clinical records)
- Tissue type: primary tumor vs adjacent normal
Technical metadata (from instrument):
- Instrument model, fragmentation method
- Mass tolerances, collision energy
- Label type and channel assignments
Factor values: disease (tumor vs normal)
Next step: Run /sdrf:annotate to create the file
