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Use when the user wants to choose an analysis pipeline, check SDRF compatibility with a pipeline, or understand how to go from SDRF to analysis.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
| name | sdrf:convert |
| description | Use when the user wants to choose an analysis pipeline, check SDRF compatibility with a pipeline, or understand how to go from SDRF to analysis. |
| user-invocable | true |
| argument-hint | [pipeline name or experiment description] |
You are helping the user choose and configure an analysis pipeline from their SDRF.
sdrf-pipelines can convert SDRF to these formats:
| Pipeline | Command | Best For |
|---|---|---|
| OpenMS | convert-openms | Flexible workflows, custom pipelines |
| MaxQuant | convert-maxquant | DDA label-free, TMT, SILAC (desktop) |
| DIA-NN | convert-diann | DIA/SWATH, PlexDIA (fast, scalable) |
| MSstats | convert-msstats | Statistical analysis (downstream of search) |
| NormalyzerDE | convert-normalyzerde | Normalization and differential expression |
| quantms | Nextflow pipeline | Cloud/HPC, complete workflow (uses SDRF natively) |
Is it DIA data?
├── YES → DIA-NN (fastest, best DIA performance)
│ Also consider: quantms with DIA module
│
└── NO (DDA) →
├── Label-free?
│ ├── Small study (<50 samples) → MaxQuant
│ ├── Large study (>50 samples) → quantms (scalable)
│ └── Custom workflow needed → OpenMS
│
├── TMT/iTRAQ?
│ ├── Standard TMT → MaxQuant or quantms
│ ├── TMT + phospho → MaxQuant (PTM scoring)
│ └── Large TMT cohort → quantms
│
└── SILAC?
└── MaxQuant (best SILAC support)
For statistical analysis (after search):
→ MSstats (gold standard for proteomics statistics)
→ NormalyzerDE (normalization comparison)
For cloud/HPC processing:
→ quantms (Nextflow, reads SDRF directly, no conversion needed)
Before recommending, verify SDRF compatibility:
The sdrf-pipelines Python package provides the parse_sdrf CLI tool.
Install: pip install sdrf-pipelines
# For MaxQuant:
parse_sdrf convert-maxquant --sdrf file.sdrf.tsv --fastafilepath proteins.fasta
# For OpenMS:
parse_sdrf convert-openms --sdrf file.sdrf.tsv --onetable
# For DIA-NN:
parse_sdrf convert-diann --sdrf file.sdrf.tsv
# For MSstats (from OpenMS output):
parse_sdrf convert-msstats --sdrf file.sdrf.tsv --openswathtomsstats
# For NormalyzerDE:
parse_sdrf convert-normalyzerde --sdrf file.sdrf.tsv
# For quantms (Nextflow — reads SDRF directly, no conversion needed):
nextflow run bigbio/quantms --input file.sdrf.tsv --fasta proteins.fasta
Note: quantms reads SDRF natively via its own Nextflow modules. The SDRF is the
pipeline input — no parse_sdrf conversion step is needed.
Independently falsify and review a created, modified, or proposed SDRF against its specification, ontology terms, source evidence, repository files, and experimental design. Use after SDRF annotation or repair, before contribution or completion, when a review gate reports a pending artifact, or whenever an isolated reviewer must distrust the producer's self-assessment and issue a hash-bound verdict.
Create or improve an SDRF through a producer-reviewer workflow with deterministic validation, an evidence manifest, fresh-context adversarial review, repair, and mandatory re-review. Use when an SDRF must be independently verified before completion or contribution, when the user requests adversarial review, or when automated review hooks require a passing hash-bound receipt.
Use when the user wants SDRF annotation to run as an autonomous retained-improvement loop over one dataset, a manifest, or a dataset class such as all PRIDE cell line or crosslinking datasets.
Use when the user has a completed SDRF annotation for a ProteomeXchange dataset and wants to contribute it back to the community via a PR to sdrf-annotated-datasets.
Use when the user wants a comprehensive quality review of an SDRF file, a PR review of an SDRF submission, or a quality score assessment.
Use before sdrf:autoresearch when the user needs to screen or shortlist proteomics studies from PRIDE, MassIVE, ProteomeXchange accessions, or a manifest using detailed user-defined inclusion/exclusion criteria; extract study-level metadata from repository records and publications; and write an evidence-backed TSV for downstream annotation, review, or meta-analysis.