Find and retrieve proteomics datasets from MassIVE and ProteomeXchange. Search by species, keyword, or accession; retrieve detailed metadata (instruments, publications, species, PTMs studied). Use for locating public proteomics datasets to reanalyze, comparing instrument/protocol coverage across studies, and pre-download dataset evaluation.
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Find and retrieve proteomics datasets from MassIVE and ProteomeXchange. Search by species, keyword, or accession; retrieve detailed metadata (instruments, publications, species, PTMs studied). Use for locating public proteomics datasets to reanalyze, comparing instrument/protocol coverage across studies, and pre-download dataset evaluation.
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Proteomics Data Retrieval
Find and retrieve metadata for publicly available proteomics datasets from MassIVE and ProteomeXchange
repositories. Supports searching by species, keyword, or accession, and returns detailed dataset metadata
including instruments, publications, species, and post-translational modifications.
When to Use This Skill
Triggers:
"Find proteomics datasets for [organism/disease/protein]"
"Search MassIVE for [keyword]"
"Get details for PXD000001" or "Look up MSV000079514"
"What public mass spectrometry datasets exist for [topic]?"
"Find MS datasets with [PTM type] data"
"List recent human proteomics datasets"
Use Cases:
Dataset Discovery: Search repositories for proteomics experiments related to a research topic
Accession Lookup: Get full metadata for a known dataset accession (PXD or MSV)
Species-Filtered Search: Find all datasets for a specific organism
Cross-Repository Search: Query both MassIVE and ProteomeXchange for comprehensive coverage
Experimental Context: Find published datasets to validate or complement in-house results
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
KEY PRINCIPLES
ProteomeXchange is the aggregator -- it indexes datasets from PRIDE, MassIVE, PeptideAtlas, jPOST, and iProX
MassIVE has richer metadata -- includes summaries, keywords, modifications, and contacts
Search both repositories -- ProteomeXchange for breadth, MassIVE for detail
Species uses NCBI taxonomy IDs -- human = 9606, mouse = 10090, rat = 10116
Accession formats: PXD (ProteomeXchange), MSV (MassIVE) -- both accepted by MassIVE_get_dataset
LOOK UP DON'T GUESS -- Never assume which datasets exist, their accessions, or their instrument types. Always search and retrieve metadata to confirm.
Domain Reasoning: Dataset Quality Assessment
Dataset quality depends on instrument, sample preparation, and quantification method. TMT/iTRAQ (isobaric labeling) datasets have ratio compression and co-isolation interference biases that differ from label-free quantification (LFQ). DIA datasets require different analysis pipelines than DDA. Check the original publication for methods before reusing data in a meta-analysis or cross-study comparison. Instrument resolution (Orbitrap > ion trap) and acquisition mode (DIA > DDA for completeness) directly affect how many proteins are quantified and at what confidence.
Core Repositories Integrated
Repository
Coverage
Strengths
MassIVE
10,000+ datasets
Rich metadata (summaries, keywords, modifications, contacts), species filtering by taxonomy ID
Try individual terms instead of multi-word queries
Common Species Taxonomy IDs
Species
Taxonomy ID
Human
9606
Mouse
10090
Rat
10116
Zebrafish
7955
Fruit fly
7227
C. elegans
6239
S. cerevisiae
559292
A. thaliana
3702
E. coli
562
Interpretation Framework
Quality Indicator
Good
Acceptable
Caution
Instrument
Orbitrap Exploris/Eclipse, timsTOF
Q Exactive, TripleTOF 6600
Older LTQ, ion trap only
Publication
Peer-reviewed with PubMed ID
Preprint or DOI only
No associated publication
Metadata completeness
Species + instrument + PTMs + summary
Species + instrument only
Title only, no annotations
Interpreting dataset search results:
Datasets with both MassIVE and ProteomeXchange accessions generally have richer metadata; MassIVE provides summaries and keywords while ProteomeXchange provides file counts -- cross-reference both for a complete picture.
Instrument type determines data quality ceiling: high-resolution instruments (Orbitrap, timsTOF) produce higher mass accuracy and more reliable quantification than older ion trap platforms.
A dataset lacking a peer-reviewed publication may still be valuable, but its experimental design and processing pipeline cannot be independently verified -- weight such datasets lower in meta-analyses.
Synthesis questions to address in the report:
Do multiple independent datasets for the same organism/condition show consistent protein identifications, or do discrepancies suggest batch effects?
Is the instrument platform appropriate for the analysis type (e.g., DIA requires high-resolution; TMT requires MS3 or calibrated MS2)?
Are the reported PTM types and species consistent with the user's research question, or is additional filtering needed?
Limitations
MassIVE: No keyword/text search -- only species-based filtering via species parameter
ProteomeXchange: Limited metadata in search results (no summaries or keywords); get details via Dataverse_get_dataset
No full-text search: Cannot search within dataset descriptions or abstracts across repositories
No download: These tools retrieve metadata only, not raw data files
Rate limits: Both APIs may throttle under heavy load; keep page_size/limit reasonable
Coverage: ProteomeXchange is the most comprehensive but may lag behind individual repositories for very recent submissions
Integration with Other Skills
Skill
Relationship
tooluniverse-proteomics-analysis
Use retrieved datasets as input for MS data analysis
tooluniverse-protein-modification-analysis
Find PTM-specific datasets to complement iPTMnet annotations
tooluniverse-multi-omics-integration
Discover proteomics datasets for cross-omics integration
