sdrf-fix

name	sdrf:fix
description	Use when the user has an SDRF file with known errors and wants them fixed automatically. Triggers on requests to fix, correct, or repair SDRF errors.
user-invocable	true
argument-hint	[file path or paste SDRF content]

SDRF Auto-Fix Workflow

You are fixing known common errors in an SDRF file. Apply fixes systematically.

Step 0: Check parse_sdrf availability

Verify that parse_sdrf is available (run parse_sdrf --version or which parse_sdrf). If it is not installed:

• Inform the user that re-validation after fixes will need to be done manually
• Suggest /sdrf:setup or conda env create -f environment.yml && conda activate sdrf-skills (or pip install -r requirements.txt)
• Continue with the fixes; the user can validate later once dependencies are installed

Common Error Patterns and Their Fixes

1. UNIMOD Accession Swaps (45% of all errors)

Wrong	Correct	Modification
UNIMOD:21 for Acetyl	UNIMOD:1	Acetyl
UNIMOD:1 for Phospho	UNIMOD:21	Phospho
UNIMOD:34 for Oxidation	UNIMOD:35	Oxidation
UNIMOD:35 for Methyl	UNIMOD:34	Methyl

Fix: Parse NT= field, look up correct UNIMOD accession, replace AC= field.

2. Missing Ontology Prefix (30%)

Wrong	Correct
0000305	EFO:0000305
9606	NCBITaxon:9606
0002107	UBERON:0002107

Fix: Detect bare numbers, infer ontology from column type, add prefix.

3. Case Normalization (25%)

Wrong	Correct
Male	male
Female	female
Homo Sapiens	Homo sapiens
Not Available	not available

Fix: Lowercase sex values and reserved words. Organism names follow binomial rules (capital genus, lowercase species).

4. Python/Programming Artifacts (15%)

Wrong	Correct
['breast cancer']	breast cancer
nan	not available
None	not available
""	(empty or not available)

Fix: Strip brackets, quotes; replace nan/None with reserved words.

5. Reserved Word Standardization

Wrong	Correct
N/A	not applicable
NA	not available
n/a	not applicable
unknown	not available
null	not available
-	not available

Fix: Replace with correct SDRF reserved words.

6. DIA/DDA Terminology

Wrong	Correct
data-dependent acquisition	Data-Dependent Acquisition
data-independent	Data-Independent Acquisition
DDA	Data-Dependent Acquisition
DIA	Data-Independent Acquisition

Fix: Use the ontology-standard full name.

7. Age Format

Wrong	Correct
58 years	58Y
58	58Y
6 months	6M
14 days	14D
58yo	58Y

Fix: Extract number, map unit to Y/M/D suffix.

8. Modification Parameter Format

Wrong	Correct
Carbamidomethyl (C)	NT=Carbamidomethyl;AC=UNIMOD:4;TA=C;MT=Fixed
Oxidation (M)	NT=Oxidation;AC=UNIMOD:35;TA=M;MT=Variable
NT=Acetyl;AC=UNIMOD:1;TA=Protein N-term	NT=Acetyl;AC=UNIMOD:1;PP=Protein N-term;MT=Variable

Fix: Parse free-text mods, construct proper NT/AC/TA/MT format.

9. Trailing Whitespace

Fix: Trim all cell values and column names.

10. Instrument Format

Wrong	Correct
Q Exactive	AC=MS:1001911;NT=Q Exactive
Orbitrap Fusion Lumos	AC=MS:1002732;NT=Orbitrap Fusion Lumos

Fix: If missing AC= format, search OLS MS ontology and construct proper format.

mcp OLS → searchClasses(query="<instrument>", ontologyId="ms")

Fix Procedure

1. Parse the SDRF into a structured table
2. Scan every cell for each error pattern above
3. Apply fixes — for ontology-dependent fixes, verify via OLS before changing
4. Log changes — track every change made (row, column, old value, new value, reason)
5. Present changelog to user before outputting the fixed SDRF
6. Output the corrected SDRF as a TSV code block

Changelog Format

Changes Applied:
  Row 3, comment[modification parameters]:
    OLD: NT=Acetyl;AC=UNIMOD:21;TA=Protein N-term;MT=Variable
    NEW: NT=Acetyl;AC=UNIMOD:1;PP=Protein N-term;MT=Variable
    FIX: UNIMOD:21 is Phospho, not Acetyl. Correct accession is UNIMOD:1. Also TA→PP for position.

  Row 5, characteristics[sex]:
    OLD: Male
    NEW: male
    FIX: Sex values must be lowercase per SDRF specification.

  All rows, comment[data file]:
    FIX: Trimmed trailing whitespace from 12 values.

Summary: 15 fixes applied (3 UNIMOD corrections, 5 case fixes, 7 whitespace trims)

Step After Fixes: Re-Validate with sdrf-pipelines

After applying all fixes, always run programmatic validation before presenting results to the user.

1. Update spec to latest version

git submodule update --remote --recursive

2. Run sdrf-pipelines validation

Save the fixed SDRF to a file and validate with the detected templates:

parse_sdrf validate-sdrf \
  --sdrf_file fixed.sdrf.tsv \
  --template <template1> \
  --template <template2>

Detect templates from comment[sdrf template] columns in the SDRF. If parse_sdrf is not installed, tell the user: pip install sdrf-pipelines

3. Interpret results

1. If validation passes → present the changelog + fixed SDRF to the user
2. If validation finds new errors → fix them and re-run until clean
3. Verify fixed UNIMOD accessions match the NT= modification names
4. Read spec/sdrf-proteomics/TERMS.tsv and check allow_not_available/allow_not_applicable fields
5. Count: total fixes applied, remaining issues not auto-fixable

Present the re-validation summary alongside the changelog.

If all errors are fixed and the SDRF is for a ProteomeXchange dataset (PXD accession), suggest contributing the corrected annotation via /sdrf:contribute {PXD} to the sdrf-annotated-datasets community repository.

When NOT to Auto-Fix

• Values that might be intentionally different (ask the user)
• Ontology terms where the "correct" version is ambiguous
• Missing columns (suggest but don't add without user approval)
• Factor values (design decisions — always ask)
• Cell line names (need Cellosaurus verification)
• Organism names that might be intentional (e.g., hybrid organisms)