---

name: "pride-database" description: "Search the PRIDE Archive v3 REST API for proteomics datasets: discover projects by keyword + faceted filters (organism, instrument, disease, software), fetch project metadata, list and download RAW/PEAK/RESULT/FASTA files (with FTP/Aspera URLs), look up which projects mention a UniProt accession, and find similar projects. PRIDE v3 no longer exposes peptide/PSM-level identification endpoints — for spectrum-level data download the project's RESULT files. Use uniprot-protein-database for protein sequences; interpro-database for domain architecture." license: "Apache-2.0"

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()
    return r.json()

p = get_project("PXD004131")
print(f"Accession    : {p['accession']}")
print(f"Title        : {p['title'][:80]}")
print(f"Submission   : {p.get('submissionDate')}")
print(f"Publication  : {p.get('publicationDate')}")
print(f"DOI          : {p.get('doi')}")
print(f"License      : {p.get('license')}")
print(f"Type         : {p.get('submissionType')}")
print(f"Organisms    : {[o['name'] for o in p.get('organisms', [])]}")
print(f"Instruments  : {[i['name'] for i in p.get('instruments', [])]}")
print(f"Experiment   : {[e['name'] for e in p.get('experimentTypes', [])]}")
print(f"PIs          : {[pi.get('name') for pi in p.get('labPIs', [])]}")
print(f"References   : {[r.get('doi') for r in p.get('references', [])[:3]]}")

Module 4: Project Files — /projects/{accession}/files + /files/all

List the files associated with a project. Use the paginated endpoint for large projects; /files/all returns every file in one shot. Each file record carries fileCategory.value (one of RAW, PEAK, RESULT, FASTA, OTHER), fileSizeBytes (note the Bytes suffix — not fileSize), and a list of publicFileLocations each labeled FTP Protocol or Aspera Protocol.

import requests, pandas as pd

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

def get_project_files(accession, file_type=None, page_size=100):
    """Walk paginated /files for a project. Optionally filter by category code
    (RAW, PEAK, RESULT, FASTA, OTHER). Returns a DataFrame."""
    rows, page = [], 0
    while True:
        r = requests.get(f"{PRIDE}/projects/{accession}/files",
                         params={"pageSize": page_size, "page": page},
                         timeout=30)
        r.raise_for_status()
        batch = r.json()
        if not batch:
            break
        for f in batch:
            cat = f.get("fileCategory") or {}
            ftp = next((loc["value"] for loc in f.get("publicFileLocations", [])
                        if loc.get("name") == "FTP Protocol"), "")
            asp = next((loc["value"] for loc in f.get("publicFileLocations", [])
                        if loc.get("name") == "Aspera Protocol"), "")
            rows.append({
                "file_name": f.get("fileName"),
                "category": cat.get("value"),     # RAW/PEAK/RESULT/FASTA/OTHER
                "size_mb": round((f.get("fileSizeBytes") or 0) / 1e6, 2),
                "ftp_url": ftp,
                "aspera_url": asp,
                "downloads": f.get("totalDownloads"),
            })
        if len(batch) < page_size:
            break
        page += 1

    df = pd.DataFrame(rows)
    if file_type:
        df = df[df["category"] == file_type]
    return df

files_df = get_project_files("PXD004131")
print(f"Total files: {len(files_df)}")
print(files_df.groupby("category")["size_mb"].agg(["count", "sum"]).round(1).to_string())

raw_only = files_df[files_df["category"] == "RAW"]
print(f"\nRAW files: {len(raw_only)}; combined {raw_only['size_mb'].sum():.0f} MB")
print(raw_only[["file_name", "size_mb", "downloads"]].head(5).to_string(index=False))

# /files/all returns every file in one response — convenient for small projects
files = requests.get(f"{PRIDE}/projects/PXD000001/files/all", timeout=60).json()
print(f"PXD000001 files (all): {len(files)}")
for f in files[:4]:
    print(f"  [{f.get('f