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('fileCategory',{}).get('value','?'):<6}] {f['fileName']}  "
          f"{f.get('fileSizeBytes',0)/1e6:.2f} MB")

Module 5: SDRF File — /files/sdrf/{projectAccession}

PRIDE projects that follow the modern submission standard include an SDRF (Sample-Data Relationship Format) TSV that maps each MS run to its biological sample, treatment, label, fraction, etc. Pull it once, parse it as a TSV.

import requests, pandas as pd, io

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

def get_sdrf(accession):
    """Fetch the SDRF sample-to-run mapping for a project (404 if not provided)."""
    r = requests.get(f"{PRIDE}/files/sdrf/{accession}", timeout=30)
    if r.status_code == 404:
        return None
    r.raise_for_status()
    return pd.read_csv(io.StringIO(r.text), sep="\t")

# Many older projects have no SDRF — newer ones typically do
sdrf = get_sdrf("PXD000001")
if sdrf is None or sdrf.empty:
    print("No SDRF available for this project")
else:
    print(f"SDRF rows: {len(sdrf)}  cols: {len(sdrf.columns)}")
    print(f"First columns: {list(sdrf.columns)[:8]}")

Module 6: Protein → Project Mapping — /proteins/{accession}

PRIDE v3's protein endpoint returns only the list of project accessions that contain identifications for the given UniProt accession. It does not return PSM counts, peptide counts, or sequence coverage — those are not exposed at the API surface in v3. For depth metrics you must download a project's RESULT files and parse them locally.

import requests

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

def get_protein_projects(uniprot_acc):
    """Return the list of PRIDE project accessions that mention this UniProt accession.
    No PSM/peptide/coverage counts are available at this endpoint."""
    r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30)
    if r.status_code == 404:
        return None
    r.raise_for_status()
    data = r.json()
    return data.get("projects", [])

tp53 = get_protein_projects("P04637")
print(f"TP53 (P04637) is reported in {len(tp53)} PRIDE projects")
print(f"First 8: {tp53[:8]}")

unknown = get_protein_projects("Q99999")
print(f"\nQ99999 (no real protein): "
      f"{'no PRIDE evidence' if not unknown else f'{len(unknown)} projects'}")

Module 7: Discovery Helpers — Similar Projects, Autocomplete

/projects/{accession}/similarProjects returns projects with related metadata signatures (organism, instrument, experiment type, tags). /search/autocomplete?keyword=... returns project titles starting with the prefix — useful to suggest searches.

import requests, pandas as pd

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

# Find similar projects to one of interest
similar = requests.get(f"{PRIDE}/projects/PXD004131/similarProjects",
                       params={"pageSize": 5}, timeout=30).json()
print(f"Similar to PXD004131: {len(similar)} projects")
for p in similar[:5]:
    print(f"  {p['accession']}  {(p.get('title') or '')[:70]}")

# Autocomplete suggestions for a project-title prefix
suggestions = requests.get(f"{PRIDE}/search/autocomplete",
                           params={"keyword": "tp53"}, timeout=30).json()
print(f"\nAutocomplete for 'tp53': {len(suggestions)} suggestions")
for s in suggestions[:5]:
    print(f"  {s}")

Module 8: Repository-Wide Counts — /projects/count, /files/count

Get total counts across the repository — useful for status displays and sanity checks. Both endpoints return a plain integer body (no JSON object wrapper).

import requests

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

n_projects = int(requests.get(f"{PRIDE}/projects/count", timeout=30).text)
n_files    = int(requests.get(f"{PRIDE}/files/count", timeout=30).text)
print(f"PRIDE Archive current scale:")
print(f"  Projects: {n_projects:,}")
print(f"  Files:    {n_files:,}")

Key Concepts

Plain JSON Arrays, No HAL Envelope

PRIDE v3 list endpoints return plain JSON arrays — for example /search/projects returns [{...}, {...}, ...] directly. There is no _embedded.compactprojects, no page.totalElements/totalPages, no _links.next.href. Older PRIDE v2 clients that parsed data["_embedded"]["compactprojects"] will silently return empty against the current API. To paginate, walk page=0, 1, 2, ... until you get an empty array (or a partial page shorter than pageSize).

What v3 Removed

The endpoint families below no longer exist in v3 (and v2 is now an alias for v3 internally — error messages from /v2/peptides literally report path: "/pride/ws/archive/v3/peptides"):

Removed endpoint	Status in v3	Replacement
GET /peptides?projectAccessions=X	404	None — download project's RESULT files and parse
GET /psms?projectAccessions=X	404	None — download RESULT files
GET /proteins?proteinAccession=X (query-param style)	404	GET /proteins/{accession} (path-param)
HAL+JSON _embedded/page wrapper	Gone	Plain JSON array
/projects?keyword=...&organisms=...&tissues=... filters	Silently ignored	/search/projects?keyword=...&filter=field==value

Filter Syntax on /search/projects

The filter query parameter takes a comma-separated list of field==value constraints. Field names use the _facet suffix (the underlying Solr-style field). Discover valid field names and values via /facet/projects before constructing the filter:

# Valid filter forms
"organisms_facet==Homo sapiens (human)"
"instruments_facet==Q Exactive"
"diseases_facet==Prostate adenocarcinoma"
"softwares_facet==MaxQuant"

# Combine with commas
filter="organisms_facet==Homo sapiens (human),instruments_facet==Orbitrap Fusion Lumos"

File Categories

Each file in a project carries a fileCategory CV-param. The .value is a category code; the .name is the human-readable label:

value code	Description	Common formats
RAW	Unprocessed instrument output	.raw (Thermo), .d (Bruker/Agilent), .wiff (Sciex)
PEAK	Centroided / deconvoluted spectra	.mzML, .mzXML, .mgf
RESULT	Identification results	.mzid, .mzTab, MaxQuant txt, PRIDE XML
FASTA	Protein sequence database used in search	.fasta
OTHER	Supplementary / scripts / tables	.txt, .xlsx, .csv

For reanalysis pipelines, RESULT is the cheapest entry point — pre-identified peptides without re-searching spectra. PEAK lets you re-search with a different engine. RAW is only needed for full vendor-format reprocessing.

Accession Formats

PRIDE project accessions follow ProteomeXchange format PXD######. These are stable across PRIDE, MassIVE, jPOST, and iProX. File accessions inside PRIDE are SHA-256-style hashes (e.g., 5bda360133398f66021c8889e01dce921cb51300c7269e1f2b0f20368ab20af6) — opaque identifiers; use fileName for human-readable filenames.

Common Workflows

Workflow 1: Faceted Discovery — From Disease Keyword to Filtered Project List

Goal: Start from a disease keyword, see which instruments and softwares are common in matching datasets via facet counts, then pull a filtered project list using one of the top values.

import requests, pandas as pd

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

# 1) Inspect facet counts to learn which filter values dominate
facets = requests.get(f"{PRIDE}/facet/projects",
                      params={"keyword": disease_kw, "facetPageSize": 10},
                      timeout=30).json()

top_instr = sorted(facets.get("instruments", {}).items(), key=lambda kv: -kv[1])[:5]
top_org = sorted(facets.get("organisms", {}).items(), key=lambda kv: -kv[1])[:3]
print(f"Top instruments for '{disease_kw}':")
for k, v in top_instr: print(f"  {k:<35} {v}")
print(f"Top organisms:")
for k, v in top_org: print(f"  {k:<35} {v}")

# 2) Build a filtered search using one top instrument
target_instr = top_instr[0][0]
projects = requests.get(f"{PRIDE}/search/projects",
                        params={"keyword": disease_kw,
                                "filter": f"organisms_facet==Homo sapiens (human),instruments_facet=={target_instr}",
                                "pageSize": 50,
                                "sortFields": "submission_date",
                                "sortDirection": "DESC"},
                        timeout=30).json()

df = pd.DataFrame([{
    "accession": p["accession"],
    "title": (p.get("title") or "")[:70],
    "submission_date": p.get("submissionDate"),
    "tissues": ", ".join(p.get("organismsPart", []))[:40],
    "submitter": (p.get("submitters") or [""])[0] if p.get("submitters") else "",
} for p in projects])
print(f"\nFiltered projects: {len(df)}  (target instrument: {target_instr})")
print(df.head(10).to_string(index=False))
df.to_csv(f"{disease_kw.replace(' ', '_')}_{target_instr.replace(' ', '_')}_projects.csv",
          index=False)

Workflow 2: File Download Manifest for One Project

Goal: Pull the file list for a project, filter to the categories you actually want (RAW + RESULT), and emit an aria2c-ready URL list for parallel FTP download.

import requests, pandas as pd
from pathlib import Path

PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3"
accession = "PXD004131"
keep_categories = {"RAW", "RESULT"}
output_dir = Path(f"/data/pride/{accession}")

files = requests.get(f"{PRIDE}/projects/{accession}/files/all", timeout=120).json()

manifest = []
for f in files:
    cat = (f.get("fileCategory") or {}).get("value")
    if cat not in keep_categories:
        continue
    ftp = next((loc["value"] for loc in f.get("publicFileLocations", [])
                if loc.get("name") == "FTP Protocol"), None)
    if not ftp:
        continue
    manifest.append({
        "file_name": f["fileName"],
        "category": cat,
        "size_mb": round((f.get("fileSizeBytes") or 0) / 1e6, 2),
        "ftp": ftp,
    })

mdf = pd.DataFrame(manifest).sort_values(["category", "file_name"])
print(f"{accession}: keeping {len(mdf)}/{len(files)} files "
      f"({mdf['size_mb'].sum():.0f} MB total)")
print(mdf.groupby("category")[["size_mb"]].sum().round(0))

# aria2c -i pride_dl.list -d /data/pride/PXD004131 -x 8 -j 4
with open("pride_dl.list", "w") as fh:
    fh.write("\n".join(mdf["ftp"]))
print(f"\nWrote pride_dl.list with {len(mdf)} URLs (use aria2c -i)")

Workflow 3: Protein Cross-Project Occurrence

Goal: For a candidate protein panel (e.g., from a differential-expression analysis), look up how many PRIDE projects mention each one and shortlist the most-evidenced proteins. Note: this is a project-count signal only — there are no PSM/peptide counts at the API surface in v3, so a high project count is breadth, not depth.

import requests, time, pandas as pd, matplotlib.pyplot as plt

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

candidates = {
    "P04637": "TP53", "P38398": "BRCA1", "P31749": "AKT1",
    "P40763": "STAT3", "O15530": "PDPK1", "P10275": "AR",
}

rows = []
for acc, sym in candidates.items():
    r = requests.get(f"{PRIDE}/proteins/{acc}", timeout=30)
    projs = r.json().get("projects", []) if r.status_code == 200 else []
    rows.append({"uniprot": acc, "symbol": sym, "n_projects": len(projs)})
    time.sleep(0.3)

df = pd.DataFrame(rows).sort_values("n_projects", ascending=False)
print(df.to_string(index=False))

fig, ax = plt.subplots(figsize=(8, 3.5))
bars = ax.bar(df["symbol"], df["n_projects"], color="#3182BD")
ax.bar_label(bars, fmt="%d", fontsize=9, padding=2)
ax.set_ylabel("# PRIDE projects mentioning the protein")
ax.set_title("PRIDE project-level occurrence — candidate panel")
plt.tight_layout()
plt.savefig("pride_protein_occurrence.png", dpi=150, bbox_inches="tight")
print("Saved pride_protein_occurrence.png")

Key Parameters

Parameter	Endpoint	Default	Range / Options	Effect
keyword	/search/projects, /facet/projects, /search/autocomplete	—	free-text string	Full-text search across title, description, tags
filter	/search/projects	—	field_facet==value, field_facet==value	Server-side filter using facet field names
pageSize	/search/projects, /projects, /projects/{acc}/files, /projects/{acc}/similarProjects	100	positive integer	Results per page
page	same as above	0	0-indexed integer	Page number (no metadata returned — walk pages until empty)
sortFields	/search/projects	submission_date	comma-separated field names	Sort key(s)
sortDirection	/search/projects	DESC	ASC or DESC	Sort order
facetPageSize	/facet/projects	20	positive integer	Values returned per facet group
dateGap	/search/projects, /facet/projects	—	e.g. +1MONTH, +1YEAR	Date-range aggregation granularity
(path) accession	/projects/{acc}, /projects/{acc}/files, /projects/{acc}/similarProjects, /proteins/{acc}	required	PXD###### or UniProt acc	Identifies the resource

Best Practices

1. Use /search/projects for searching, not /projects. Plain /projects is a paginated listing endpoint and silently ignores keyword / organism / disease filters. Filtering only works through /search/projects with the filter=field_facet==value syntax.

2. Discover filter values via /facet/projects before filtering. Facet field values must match exactly (e.g., organisms_facet==Homo sapiens (human), parentheses and all). The facet endpoint tells you which values exist and how many projects each has — saves a lot of trial-and-error.

3. Don't try to query peptide- or PSM-level data over the API. Those endpoints were removed in v3. Download the project's RESULT files and parse them locally with pyteomics, pyOpenMS, or a search-engine reader (MaxQuant, ProteomeDiscoverer, etc.).

4. Prefer FTP URLs for bulk file downloads. Each file record carries both FTP Protocol and Aspera Protocol URLs. FTP is more universally supported; pair it with aria2c -x 8 -j 4 for parallel chunks. Use Aspera only if you have an Aspera client and need >100 Mbit transfer speeds.

5. Watch the field name fileSizeBytes. The current v3 field is fileSizeBytes, not fileSize (old v2 docs may say fileSize). Sizes are in bytes — divide by 1e6 for MB, 1e9 for GB.

6. Filter file downloads by fileCategory.value. A project can have hundreds of files spanning RAW (GB-scale) and OTHER (KB-scale). Always filter to the categories you actually need before queueing downloads — otherwise you'll easily download tens of gigabytes of vendor RAW files when you only wanted the identification tables.

7. Pagination has no metadata — walk until empty. Unlike old PRIDE v2, the v3 API doesn't return totalElements/totalPages. Iterate page=0, 1, 2, ... and stop when a page returns an empty array, or when its length is less than pageSize.

Common Recipes

Recipe: Quick Project File Summary

import requests

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

def project_file_summary(accession):
    files = requests.get(f"{PRIDE}/projects/{accession}/files/all", timeout=60).json()
    by_cat = {}
    for f in files:
        cat = (f.get("fileCategory") or {}).get("value", "OTHER")
        by_cat.setdefault(cat, [0, 0])
        by_cat[cat][0] += 1
        by_cat[cat][1] += (f.get("fileSizeBytes") or 0) / 1e6
    print(f"\n{accession} file summary:")
    for cat, (n, mb) in sorted(by_cat.items()):
        print(f"  {cat:<8} {n:>4} file(s)   {mb:>10.1f} MB")
    total_mb = sum(mb for _, mb in by_cat.values())
    total_n  = sum(n  for n, _  in by_cat.values())
    print(f"  {'TOTAL':<8} {total_n:>4} file(s)   {total_mb:>10.1f} MB")

project_file_summary("PXD000001")

Recipe: Check If a Protein Has Any PRIDE Evidence

import requests

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

def pride_evidence(uniprot_acc):
    """Return (has_evidence, n_projects). PRIDE v3 only exposes project list, no PSM counts."""
    r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30)
    if r.status_code != 200:
        return False, 0
    projs = r.json().get("projects", [])
    return bool(projs), len(projs)

for acc in ["P04637", "Q99999"]:
    has, n = pride_evidence(acc)
    print(f"{acc}: evidence={has}  projects={n}")

Recipe: Recent Submissions for a Keyword (sorted by date)

import requests, pandas as pd

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

r = requests.get(f"{PRIDE}/search/projects",
                 params={"keyword": "single-cell proteomics",
                         "sortFields": "submission_date",
                         "sortDirection": "DESC",
                         "pageSize": 15},
                 timeout=30)
recent = r.json()
df = pd.DataFrame([{
    "submission_date": p.get("submissionDate"),
    "accession": p["accession"],
    "title": (p.get("title") or "")[:80],
} for p in recent]).sort_values("submission_date", ascending=False)
print(df.to_string(index=False))

Recipe: Suggest-as-You-Type via Autocomplete

import requests

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

for prefix in ["alzheimer", "single cell", "brca"]:
    s = requests.get(f"{PRIDE}/search/autocomplete",
                     params={"keyword": prefix}, timeout=30).json()
    print(f"\n'{prefix}' → {len(s)} suggestions:")
    for sug in s[:3]:
        print(f"  · {sug[:80]}")

Troubleshooting

Problem	Cause	Solution
Parsing returns empty list even when r.json() has data	Code is doing data["_embedded"]["compactprojects"] — old HAL+JSON wrapper that v3 no longer returns	Parse the response directly as a list: projects = r.json()
HTTP 404 on /peptides, /psms, or /proteins?proteinAccession=X	These endpoints were removed in v3	For peptide/PSM data, download the project's RESULT files and parse locally. For protein lookup, use /proteins/{accession} (path param)
/projects?keyword=cancer returns the same 100 results as /projects with no keyword	The /projects endpoint only accepts pageSize / page — keyword and other filters are silently ignored	Use /search/projects?keyword=...&filter=... instead
/projects/{acc}/files shows file size 0	Reading fileSize instead of fileSizeBytes	The v3 field is fileSizeBytes (bytes); compute MB via fileSizeBytes / 1e6
Filter has no effect	Facet value doesn't exactly match a real value	Call /facet/projects?keyword=... first to enumerate valid values (Homo sapiens (human), not Homo sapiens)
pageSize beyond the actual result set returns an empty array	Normal pagination behavior	Stop iterating when the returned array length is < pageSize, or when it is empty
findAllOrganismsCount returns HTTP 406 Not Acceptable	The endpoint requires a non-JSON Accept header	Skip this endpoint — facet counts via /facet/projects cover the same need
HTTP 429 or ConnectionError on bursts	Shared EBI infrastructure	Add time.sleep(0.3) in loops; retry on 5xx with exponential backoff

Related Skills

• uniprot-protein-database — UniProt sequences, Swiss-Prot annotations, ID mapping; pair with PRIDE protein lookups to enrich each UniProt accession with sequence and functional information
• interpro-database — Protein domain architecture (Pfam, SMART, PANTHER) for proteins reported in PRIDE
• pdb-database — Resolved 3D structures for proteins with PRIDE evidence
• pyteomics (off-skill Python library) — Parse mzIdentML / mzML / mzTab files downloaded from /projects/{accession}/files; the path for spectrum- and PSM-level analysis now that the REST API no longer exposes those

References

• PRIDE Archive v3 REST API root — base URL
• PRIDE v3 OpenAPI/Swagger spec — authoritative endpoint inventory
• PRIDE Archive web portal — interactive dataset browser
• Perez-Riverol et al., Nucleic Acids Research 2022 — PRIDE 2022 update describing repository and architecture
• ProteomeXchange Consortium — standard accession system shared across PRIDE, MassIVE, jPOST, iProX
• SDRF-Proteomics specification — format used by /files/sdrf/{projectAccession}