pride-database

PRIDE Database

Overview

The PRIDE Archive (ProteomicsIDEntifications database) at EMBL-EBI is the world's largest public mass-spectrometry proteomics repository — 39,000+ projects and 3.4M+ deposited files as of 2026. Programmatic access is via a JSON REST API at https://www.ebi.ac.uk/pride/ws/archive/v3/. No authentication is required. The OpenAPI/Swagger spec is at https://www.ebi.ac.uk/pride/ws/archive/v3/v3/api-docs. PRIDE v3 returns plain JSON arrays for list endpoints (no HAL+JSON _embedded envelope) and intentionally does not expose per-peptide or per-PSM identification endpoints — for spectrum-level identifications, download the project's RESULT files (mzIdentML, MaxQuant txt, etc.) and parse them locally.

When to Use

• Finding published proteomics datasets by free-text keyword and facet filters (organism, tissue, disease, instrument, software, PTM) for meta-analysis or benchmarking
• Downloading raw mass-spectrometry data (RAW, mzML, MGF) or pre-processed identifications (RESULT files) from a specific PRIDE project accession
• Looking up which PRIDE projects mention a specific UniProt protein accession (project-level occurrence map only — no PSM/coverage counts at the API surface)
• Finding similar projects to one of interest for reanalysis or cross-study comparison
• Fetching SDRF (Sample-Data Relationship Format) files for projects so you can model the sample-to-MS-run mapping programmatically
• Discovering valid filter values via faceted search before constructing a structured query
• For protein sequences, Swiss-Prot annotations, and ID mapping use uniprot-protein-database
• For protein domain and family classification use interpro-database — PRIDE only reports project-level occurrence, not domain-level features
• PRIDE v3 has no /peptides, /psms, or /proteins?proteinAccession= endpoints — if you need peptide- or PSM-level data, download the RESULT files from /projects/{accession}/files and parse them with pyteomics or a search-engine-specific reader

Prerequisites

• Python packages: requests, pandas, matplotlib
• Data requirements: a PRIDE project accession (PXD###### format) or a search keyword, optionally a UniProt accession for protein-occurrence lookup
• Environment: internet connection; no API key required
• Rate limits: not formally published; keep bursts under ~5 requests/second and add time.sleep(0.3) in loops

pip install requests pandas matplotlib

Quick Start

import requests

PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3"

# 1) Free-text search for cancer proteomics projects
projects = requests.get(f"{PRIDE}/search/projects",
                        params={"keyword": "prostate cancer", "pageSize": 5},
                        timeout=30).json()
print(f"Top {len(projects)} projects:")
for p in projects[:3]:
    instr = ", ".join(p.get("instruments", []))[:50]
    print(f"  {p['accession']}  {(p['title'] or '')[:70]}  [{instr}]")

# 2) Drill into one project
acc = projects[0]["accession"]
proj = requests.get(f"{PRIDE}/projects/{acc}", timeout=30).json()
print(f"\n{proj['accession']}: {proj['title'][:70]}")
print(f"  Submitted: {proj.get('submissionDate')}  DOI: {proj.get('doi')}")
print(f"  Organisms: {[o['name'] for o in proj.get('organisms', [])]}")
print(f"  Instruments: {[i['name'] for i in proj.get('instruments', [])]}")

# 3) List files and total size
files = requests.get(f"{PRIDE}/projects/{acc}/files/all", timeout=60).json()
total_mb = sum(f.get("fileSizeBytes", 0) for f in files) / 1e6
print(f"\n  {len(files)} files, {total_mb:.0f} MB total")

Core API

Module 1: Project Search — /search/projects

Free-text search with optional facet-based filtering, pagination, and sorting. Returns a plain JSON array of project records — there is no HAL+JSON _embedded/page wrapper.

import requests, pandas as pd

PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3"

def search_projects(keyword=None, organism=None, instrument=None,
                    disease=None, software=None,
                    page_size=25, page=0, sort_field="submission_date",
                    sort_direction="DESC"):
    """Search PRIDE v3 for projects.
    Filter syntax (for the `filter` arg) is `field==value, field==value` using `_facet` field names
    that are discoverable via /facet/projects."""
    filters = []
    if organism:   filters.append(f"organisms_facet=={organism}")
    if instrument: filters.append(f"instruments_facet=={instrument}")
    if disease:    filters.append(f"diseases_facet=={disease}")
    if software:   filters.append(f"softwares_facet=={software}")

    params = {"pageSize": page_size, "page": page,
              "sortFields": sort_field, "sortDirection": sort_direction}
    if keyword: params["keyword"] = keyword
    if filters: params["filter"] = ",".join(filters)

    r = requests.get(f"{PRIDE}/search/projects", params=params, timeout=30)
    r.raise_for_status()
    return r.json()   # plain list[dict]

projects = search_projects(keyword="cancer", organism="Homo sapiens (human)",
                           instrument="Q Exactive", page_size=5)
df = pd.DataFrame([{
    "accession": p["accession"],
    "title": (p.get("title") or "")[:70],
    "submission_date": p.get("submissionDate"),
    "diseases": ", ".join(p.get("diseases", []))[:60],
    "instruments": ", ".join(p.get("instruments", []))[:50],
} for p in projects])
print(df.to_string(index=False))

# Paginate through all matches for a keyword. The API doesn't return total counts inline;
# walk pages until the next one is empty.
def search_all_projects(keyword, page_size=100, max_pages=20):
    all_records, page = [], 0
    while page < max_pages:
        batch = search_projects(keyword=keyword, page_size=page_size, page=page)
        if not batch:
            break
        all_records.extend(batch)
        if len(batch) < page_size:
            break    # last page
        page += 1
    return all_records

results = search_all_projects("phosphoproteomics", page_size=100, max_pages=3)
print(f"Phosphoproteomics projects collected (max 300): {len(results)}")

Module 2: Faceted Filter Discovery — /facet/projects

Before constructing a filtered search, query the facet endpoint to see which instrument / organism / disease / software values actually exist for a given keyword, along with their counts. The response is a dict of facet groups, each mapping {value: count}.

import requests, pandas as pd

PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3"

def get_facets(keyword=None, facet_page_size=20):
    """Return facet counts for projects matching `keyword`. Keys are facet groups
    (instruments, organisms, diseases, softwares, experimentTypes, ...); values are
    dicts of {value: count}."""
    params = {"facetPageSize": facet_page_size}
    if keyword: params["keyword"] = keyword
    r = requests.get(f"{PRIDE}/facet/projects", params=params, timeout=30)
    r.raise_for_status()
    return r.json()

facets = get_facets(keyword="cancer", facet_page_size=10)
print(f"Facet groups: {list(facets.keys())}")
print(f"\nTop instruments for 'cancer':")
for instr, n in sorted(facets.get("instruments", {}).items(), key=lambda kv: -kv[1])[:8]:
    print(f"  {instr:<35} {n}")
print(f"\nTop diseases:")
for d, n in sorted(facets.get("diseases", {}).items(), key=lambda kv: -kv[1])[:6]:
    print(f"  {d:<55} {n}")

Module 3: Project Detail — /projects/{accession}

Full metadata for a single project: submitters, labPIs, instruments, organisms (CV-coded), diseases, experiment types, references, DOI, submission/publication dates. Lists are CvParam-style objects with accession, cvLabel, name, optionally value.

import requests

PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3"

def get_project(accession):
    r = requests.get(f"{PRIDE}/projects/{accession}", timeout=30)
    r.raise_for_status()
    r