sdrf:autoresearch

SDRF Autoresearch Protocol

This workflow is a domain-specific autonomous loop for SDRF annotation. It is intended to function with minimal user supervision once the target and optimization goal are clear.

Use it when the user asks for:

• annotation of all datasets in a category
• repeated refine → validate → fix loops
• maximum metadata completion without blind guessing
• autonomous SDRF improvement until no more retained gains are possible

This is a protocol skill, not a dedicated runner script. Execute the loop by following the steps below and by calling the existing sdrf:* skills in order.

Console Triggers

Claude-style examples:

/sdrf:autoresearch target="all PRIDE cell line datasets"
/sdrf:autoresearch target="all sandbox crosslinking datasets" profile="crosslinking"
/sdrf:autoresearch target="manifest:data/cell_line_manifest.tsv" objective="maximize_valid_field_coverage"

Codex-style examples:

$sdrf-autoresearch target="all PRIDE cell line datasets"
$sdrf-autoresearch target="accessions:PXD001234,PXD005678" profile="clinical"
$sdrf-autoresearch target="all sandbox crosslinking datasets" objective="crosslinking_assay_completion" write="sandbox"

Step 1: Parse the Request into a Loop Config

Normalize the user request into these fields:

• target

  • What dataset set to operate on
  • Examples:
    • all PRIDE cell line datasets
    • all sandbox crosslinking datasets
    • manifest:data/cell_line_manifest.tsv
    • accessions:PXD001234,PXD005678

• profile

  • Domain preset that biases which templates, columns, and evidence sources matter most
  • Supported defaults:
    • general-proteomics
    • cell-line
    • crosslinking
    • clinical
    • immunopeptidomics

• objective

  • The optimization target for retained improvements
  • Supported defaults:
    • maximize_valid_field_coverage
    • minimize_unknowns
    • crosslinking_assay_completion
    • cell_line_sample_completion
    • clinical_sample_completion

• focus_fields

  • Optional list of fields to prioritize
  • Examples:
    • cell line,disease,organism part,treatment
    • cross-linker,crosslink enrichment method,collision energy

• evidence

  • Which sources are allowed
  • Defaults to pride,files,europepmc
  • Common values:
    • pride,files
    • pride,files,europepmc
    • manuscript-first

• stop

  • When the loop should stop
  • Supported defaults:
    • 3_no_improve_rounds
    • coverage>=0.95
    • only_low_confidence_candidates_left

• write

  • Where edits should land
  • Supported values:
    • sandbox
    • branch
    • report-only

If the user does not specify these explicitly, infer them conservatively and state the inferred config before the loop begins.

Step 2: Build the Dataset Target Set

Interpret target into a concrete dataset set:

• accessions:...

  • Use those accessions directly

• manifest:...

  • Read the manifest TSV/CSV and use those datasets

• all PRIDE <category> datasets

  • Use PRIDE MCP + local manifests to discover datasets matching the category
  • Examples:
    • cell line
    • crosslinking
    • human clinical
    • immunopeptidomics

• all sandbox <category> datasets

  • Use local sandbox inventory first

Always resolve the target into a manifest-like working list before annotation begins.

Plasma Campaign Heuristic

For blood plasma or plasma biomarker campaigns:

1. Audit the local project collection first

   • enumerate existing SDRFs under the relevant project tree
   • separate true plasma SDRFs from blood-cell / tissue / platelet / serum-only SDRFs
   • extract the current disease coverage before searching PRIDE

2. Treat discovery as two layers

   • new_dataset: accession not yet present in the local plasma collection
   • upgrade_existing_sdrf: accession already present locally, but disease wording, ontology, or field coverage should be improved

3. Default to human plasma unless the user explicitly says otherwise

   • treat Homo sapiens as the default species scope for biomarker-style plasma campaigns
   • use PRIDE organisms first to confirm species
   • if PRIDE species is missing or generic, confirm human-only status from the manuscript title, abstract, methods, or supplementary text
   • keep non-human or mixed-species plasma projects as audit rows, not as automatically promoted annotation targets

4. When querying PRIDE, do not trust plasma hits blindly

   • expand disease names before PRIDE search:
     • start with lexical OLS disease lookup in MONDO, DOID, EFO, and NCIT
     • add useful exact labels and synonyms
     • for broad disease requests like kidney tumor, use OLS embedding search to surface likely subtype names such as renal-cancer variants
     • keep useful child-term labels when the study is still clearly in scope, for example myositis -> dermatomyositis, sarcoma -> Ewing sarcoma, myeloma -> multiple myeloma, or alcohol-related liver disease -> alcoholic hepatitis
     • for influenza-like campaigns, acceptable query widening can include influenza A, IAV, H1N1, flu, and, if the user explicitly allows it, broader viral pneumonia plus serum
     • then query PRIDE and Europe PMC with the expanded disease family, not only the original user wording
     • record whether a promoted hit is an exact, child_term, related, or surrogate disease match so downstream ranking does not overstate coverage
   • annotate the candidate workflow class during discovery:
     • use PRIDE experimentTypes for acquisition style such as Data-independent acquisition, Data-dependent acquisition, Gel-based experiment, or Bottom-up proteomics
     • use PRIDE quantificationMethods first for labeling style such as TMT, iTRAQ, label-free quantification, Dimethyl Labeling, or NSAF
     • if quantificationMethods is empty, inspect sampleProcessingProtocol, dataProcessingProtocol, and keywords for explicit terms like TMT16plex, iTRAQ, label-free quantification, LFQ, MaxQuant, DIA, SWATH, or Spectronaut
     • store separate acquisition_mode and quant_mode columns so campaigns can prioritize DIA-LFQ, DDA-TMT, DDA-LFQ, and related workflows explicitly
   • distinguish blood plasma / plasma proteome / plasma extracellular vesicles from false positives such as plasma cells or plasma membrane
   • for the current plasma-dataset campaigns, keep only accessions hosted by PRIDE, MassIVE, jPOST, or iProX; treat PanoramaPublic discoveries as audit-only until the campaign policy changes
   • prefer candidates where both the disease and the blood-plasma context are explicit in the title or description
   • for automatic shortlist generation, only promote accessions when plasma context is positive or ambiguous and the disease context is explicit
   • keep disease-only hits with missing plasma context as low-confidence discovery rows, not as prioritized plasma projects
   • do not promote datasets that lack usable raw or acquisition files; keep them as audit-only rows even if the disease and matrix look promising
   • after shortlist generation, run a manuscript-backed plasma review and classify each accession as:
     • confirmed_plasma: explicit plasma evidence in title, abstract, methods, results, or supplementary text
     • mixed_includes_plasma: plasma is explicit, but other sample matrices are also part of the study
     • likely_non_plasma: manuscript shows a different primary matrix such as CSF, urine, platelet releasate, BALF, or cell-line material
     • unclear: insufficient manuscript evidence; do not prioritize automatically
   • prefer confirmed_plasma first, then mixed_includes_plasma if mixed-matrix studies are acceptable for the campaign

5. Search Europe PMC independently when publication links may be missing from PRIDE

   • run disease-focused Europe PMC queries even when PRIDE does not already point to a publication
   • include PXD, PRIDE, or ProteomeXchange in the literature query to favor papers that reference a proteomics dataset
   • when a paper is open access, inspect the full text and extract explicit PXD... / MSV... mentions
   • use the literature-side accession hits to cross-link back into PRIDE and recover datasets that are weakly linked or unlinked in the archive record

6. Prioritize candidates in this order

   • missing disease coverage with explicit blood-plasma context
   • related-only local coverage where a disease-specific plasma accession exists
   • already covered diseases only when the existing local SDRF is clearly incomplete

Step 3: Choose the Scoring Objective

Do not optimize for raw field count alone. Optimize for valid, evidence-supported, template-compliant completion.

Default scoring dimensions:

1. Required-field completion
2. Recommended-field completion
3. Objective-specific completion
4. Validation success
5. Ontology quality
6. Evidence support
7. Placeholder penalty

Recommended derived objectives:

maximize_valid_field_coverage

Use when the user wants the most complete valid SDRF possible.

Reward:

• required columns present and filled
• recommended columns present and filled
• validated ontology terms
• technical metadata recovered from files or manuscript evidence

Penalize:

• not available, not applicable, unknown
• invalid ontology terms
• unsupported guesses

minimize_unknowns

Use when the main problem is placeholders and unresolved values.

Reward:

• replacing unknown and unknown crosslinker
• resolving generic metadata into validated terms

Penalize:

• replacements that are not evidence-backed

crosslinking_assay_completion

Use for XL-MS datasets. Focus strongly on:

• comment[cross-linker]
• comment[crosslink enrichment method]
• characteristics[enrichment process]
• characteristics[crosslink distance]
• comment[crosslinker concentration]
• characteristics[crosslinking reaction time]
• characteristics[crosslinking temperature]
• comment[quenching reagent]
• comment[collision energy]

cell_line_sample_completion

Use for cell line datasets. Focus strongly on:

• characteristics[cell line]
• characteristics[cell type]
• characteristics[disease]
• characteristics[organism part]
• characteristics[treatment]
• characteristics[sex]
• characteristics[age]

For human or clinical campaigns, also treat demographic evidence conservatively:

• promote characteristics[developmental stage] when the cohort is clearly adult, pediatric, fetal, juvenile, and so on, even if the paper reports only cohort-level age summaries
• do not auto-fill per-sample characteristics[age], characteristics[sex], or characteristics[ethnicity] unless the manuscript or supplementary tables map them to individual source samples
• if only cohort summaries exist, record the evidence in notes rather than forcing those values into every SDRF row

Step 4: Run the Annotation Loop

For each dataset:

1. Discover evidence

   • PRIDE metadata
   • PRIDE files
   • manuscript from Europe PMC when available
   • supplementary and tables for sample metadata
   • methods for technical metadata

   For PRIDE file discovery, prefer the complete Archive REST endpoint when you need exact file coverage or file counts:

   GET https://www.ebi.ac.uk/pride/ws/archive/v3/projects/PXD######/files/all
   

   Use this to avoid partial paged counts when auditing raw files, checking whether a candidate accession is tractable, or comparing SDRF comment[data file] values against the archive. If this endpoint returns 0 files for a valid PXD hosted through PanoramaPublic, MassIVE, iProX, or jPOST, treat that as archive endpoint empty for external repository rather than no dataset. Keep the accession in play and note the repository-backed limitation in the ranking output.

2. Run /sdrf:annotate

   • draft or extend the SDRF using the selected templates

3. Run /sdrf:terms

   • normalize ontology-backed fields
   • use lexical OLS first
   • use embeddings for fuzzy manuscript-derived mentions
   • use ZOOMA as slower fallback when useful

4. Run /sdrf:techrefine

   • refine technical MS metadata when raw files or techsdrf evidence are available

5. Run /sdrf:validate

   • validate template structure, reserved words, and ontology-backed fields
   • keep validation concurrency bounded: default to serial, and never run more than 2 parse_sdrf jobs at once
   • if sdrf-techrefine, raw-file conversion, or other heavy analysis is active, validate only 1 dataset at a time

6. Run /sdrf:fix

   • apply safe corrections to known SDRF error patterns

7. Run /sdrf:improve

   • rescore completeness, specificity, consistency, standards, and design

8. Keep or discard

   • keep only if the score improves and validation does not regress
   • discard any refinement that adds unsupported metadata

Step 5: Repeat Until the Stop Rule Fires

Repeat the full loop until one of these is true:

• no retained improvement for 3 rounds
• objective score crosses the configured threshold
• only low-confidence or unsupported candidates remain
• the remaining work is clearly manual-only

Never keep looping just to replace placeholders with guesses.

Validation Resource Guard

Autonomous annotation must not compromise the machine.

Use these defaults:

• validation_mode=serial unless there is a demonstrated need for limited parallel validation
• max_validation_jobs=2
• max_validation_jobs=1 whenever raw-file analysis, file conversion, or large manuscript processing is already active
• validate changed datasets first
• use representative smoke checks before full collection sweeps

If validation hangs or the system becomes resource-constrained, reduce the number of concurrent validators before continuing.

Step 6: Refinement Heuristics by Evidence Source

Prioritize evidence as follows:

Sample metadata

Best sources:

• PRIDE sample description
• manuscript Methods
• manuscript Results
• supplementary sample tables
• figure and table captions

Technical metadata

Best sources:

• raw-file analysis
• PRIDE protocols
• manuscript Methods
• search-parameter tables

File mapping and replicate logic

Best sources:

• PRIDE file list
• SDRF row structure
• file names

Step 7: Output Requirements

At the end of the run, report:

• resolved target set
• inferred or explicit config
• retained vs discarded rounds
• final score or stopping condition
• files changed
• remaining unresolved high-value gaps

If write=report-only, produce the same retained-improvement report without writing SDRFs.

Example Configs

Cell lines

/sdrf:autoresearch target="all PRIDE cell line datasets" profile="cell-line" objective="maximize_valid_field_coverage" focus_fields="cell line,disease,organism part,treatment" evidence="pride,files,europepmc" stop="3_no_improve_rounds" write="sandbox"

Crosslinking

/sdrf:autoresearch target="all sandbox crosslinking datasets" profile="crosslinking" objective="crosslinking_assay_completion" focus_fields="cross-linker,crosslink enrichment method,collision energy,crosslinking reaction time" evidence="pride,files,europepmc" stop="only_low_confidence_candidates_left" write="sandbox"

Report-only review

/sdrf:autoresearch target="manifest:data/review_set.tsv" objective="minimize_unknowns" write="report-only"