protocols.io Integration

Overview

protocols.io is the leading protocol repository for life sciences with 90,000+ open-access experimental protocols covering molecular biology, cell biology, bioinformatics, clinical research, and lab automation. The REST API provides programmatic access to protocol search, full protocol retrieval (steps, reagents, materials, equipment), protocol versioning, workspace management, and protocol publishing. Public protocols are freely accessible; authentication (OAuth2 token) is required for private protocols or creating/editing.

When to Use

• Searching for validated wet-lab protocols by keyword, technique, or journal article DOI
• Retrieving the full step-by-step content of a protocol (reagents, timing, volumes, notes) for automation or analysis
• Finding protocols associated with a specific reagent, kit, or instrument
• Building lab automation workflows by extracting protocol steps and reagent lists programmatically
• Verifying protocol versions and citing the correct DOI for methods sections
• Discovering community-validated protocols as alternatives to proprietary methods
• Use alongside

  opentrons-integration

  or

  benchling-integration

  to implement downloaded protocols in automated workflows

Prerequisites

• Python packages:

  requests

  ,

  pandas

• Data requirements: protocol keywords, DOIs, or protocols.io protocol IDs
• Environment: internet connection; public protocols: no auth needed; private: OAuth2 token from https://www.protocols.io/developers
• Rate limits: 10 requests/second for public API; unauthenticated requests allowed for public protocols

pip install requests pandas
# For private protocol access or publishing:
# Register at https://www.protocols.io/developers to obtain an API token

Quick Start

import requests

BASE = "https://www.protocols.io/api/v4"
# For public protocols, no token needed (but add for higher rate limits)
HEADERS = {"Authorization": "Bearer YOUR_TOKEN_HERE"}  # Optional for public

# Search for CRISPR protocols
r = requests.get(f"{BASE}/protocols",
                 params={"q": "CRISPR guide RNA design", "order_field": "views",
                         "page_size": 5},
                 headers=HEADERS)
r.raise_for_status()
data = r.json()
print(f"Total CRISPR protocols: {data['pagination']['total_results']}")
for p in data["items"][:3]:
    print(f"\n  {p['title']}")
    print(f"  DOI: {p.get('doi')} | Views: {p.get('stats', {}).get('number_of_views')}")
    print(f"  Authors: {', '.join(a['name'] for a in p.get('creators', [])[:3])}")

Core API

Query 1: Protocol Search

Search the protocols.io public library by keyword, technique, or full-text.

import requests, pandas as pd

BASE = "https://www.protocols.io/api/v4"

def search_protocols(query, page_size=20, order_field="relevance", category_id=None):
    params = {"q": query, "page_size": page_size, "order_field": order_field}
    if category_id:
        params["filter[categories_ids][]"] = category_id
    r = requests.get(f"{BASE}/protocols", params=params)
    r.raise_for_status()
    return r.json()

data = search_protocols("RNA extraction tissue", page_size=10, order_field="views")
total = data["pagination"]["total_results"]
print(f"RNA extraction protocols: {total}")

rows = []
for p in data["items"][:10]:
    rows.append({
        "id": p.get("id"),
        "title": p.get("title"),
        "doi": p.get("doi"),
        "views": p.get("stats", {}).get("number_of_views", 0),
        "created": p.get("created_on"),
        "category": p.get("categories", [{}])[0].get("name", "n/a"),
    })
df = pd.DataFrame(rows).sort_values("views", ascending=False)
print(df.to_string(index=False))

# Search with category filter (get category IDs from /categories endpoint)
data_pcr = search_protocols("qPCR primer design", order_field="views")
print(f"\nqPCR protocols: {data_pcr['pagination']['total_results']}")
for p in data_pcr["items"][:3]:
    print(f"  {p['title'][:70]} (DOI: {p.get('doi', 'n/a')})")

Query 2: Retrieve Full Protocol Content

Fetch the complete protocol with steps, reagents, materials, and equipment.

import requests

BASE = "https://www.protocols.io/api/v4"

def get_protocol(protocol_id):
    r = requests.get(f"{BASE}/protocols/{protocol_id}")
    r.raise_for_status()
    return r.json()

# Retrieve protocol by ID (from search results or DOI lookup)
protocol_id = 45979  # Example: a public protocol
data = get_protocol(protocol_id)
protocol = data.get("payload", data)  # Handle API response structure

print(f"Title: {protocol.get('title')}")
print(f"DOI: {protocol.get('doi')}")
print(f"Authors: {', '.join(a['name'] for a in protocol.get('creators', []))}")
print(f"Steps: {len(protocol.get('steps', []))}")
print(f"Materials: {len(protocol.get('materials', []))}")
print(f"Abstract: {protocol.get('description', '')[:200]}")

# Parse protocol steps
protocol_steps = protocol.get("steps", [])
for i, step in enumerate(protocol_steps[:5], 1):
    step_desc = step.get("description", "")
    duration = step.get("duration", {})
    print(f"\nStep {i}: {step_desc[:120]}")
    if duration:
        print(f"  Duration: {duration.get('duration')} {duration.get('unit_label', '')}")

Query 3: Retrieve Protocol by DOI

Fetch a protocol using its DOI for precise citation-based retrieval.

import requests, json

BASE = "https://www.protocols.io/api/v4"

def get_protocol_by_doi(doi):
    """Retrieve protocol using its DOI."""
    # URL-encode the DOI for the query
    r = requests.get(f"{BASE}/protocols",
                     params={"q": doi, "page_size": 5})
    r.raise_for_status()
    items = r.json()["items"]
    for item in items:
        if item.get("doi") == doi:
            return item
    return None

doi = "10.17504/protocols.io.bvb3n2qn"  # Example protocols.io DOI
protocol = get_protocol_by_doi(doi)
if protocol:
    print(f"Found: {protocol['title']}")
    print(f"  ID: {protocol['id']}")
    print(f"  Version: {protocol.get('version_id')}")

Query 4: Extract Reagents and Materials

Parse out the materials list from a retrieved protocol.

import requests, pandas as pd

BASE = "https://www.protocols.io/api/v4"

def get_reagents(protocol_id):
    r = requests.get(f"{BASE}/protocols/{protocol_id}")
    r.raise_for_status()
    data = r.json()
    protocol = data.get("payload", data)
    return protocol.get("materials", [])

# Get reagents list
materials = get_reagents(45979)  # Example protocol ID
print(f"Materials ({len(materials)} items):")
rows = []
for m in materials[:10]:
    rows.append({
        "name": m.get("name"),
        "quantity": m.get("quantity"),
        "unit": m.get("unit", {}).get("name", ""),
        "supplier": m.get("supplier", {}).get("name", ""),
        "catalog": m.get("sku"),
    })
df = pd.DataFrame(rows)
print(df.to_string(index=False))

Query 5: Browse Protocol Categories

List available protocol categories for targeted searches.

import requests, pandas as pd

BASE = "https://www.protocols.io/api/v4"

r = requests.get(f"{BASE}/categories")
r.raise_for_status()
data = r.json()
categories = data.get("items", data.get("payload", []))

print(f"protocols.io categories: {len(categories)}")
df = pd.DataFrame(categories)[["id", "name"]].head(20)
print(df.to_string(index=False))

Query 6: List Protocol Versions

Retrieve version history for a protocol to track updates.

import requests

BASE = "https://www.protocols.io/api/v4"

def get_protocol_versions(protocol_id):
    r = requests.get(f"{BASE}/protocols/{protocol_id}")
    r.raise_for_status()
    protocol = r.json().get("payload", r.json())
    return {
        "title": protocol.get("title"),
        "version": protocol.get("version_id"),
        "published": protocol.get("published_on"),
        "doi": protocol.get("doi"),
        "parent_doi": protocol.get("parent_publication", {}).get("doi"),
    }

info = get_protocol_versions(45979)
for k, v in info.items():
    print(f"  {k}: {v}")

Key Concepts

Protocol DOIs and Versioning

Each published protocols.io protocol has a citable DOI (format:

10.17504/protocols.io.XXXXX

API Authentication

Public protocols are accessible without authentication. OAuth2 Bearer tokens are needed for: private protocols, workspace management, protocol creation/editing, and user-specific queries. Obtain tokens at https://www.protocols.io/developers.

Common Workflows

Workflow 1: Protocol Discovery and Comparison

Goal: Search for protocols matching a technique, compare them, and select the best one for adaptation.

import requests, pandas as pd

BASE = "https://www.protocols.io/api/v4"

def search_and_rank(query, top_n=20):
    """Search protocols and return ranked by views + forks."""
    r = requests.get(f"{BASE}/protocols",
                     params={"q": query, "page_size": top_n, "order_field": "views"})
    r.raise_for_status()
    data = r.json()

    rows = []
    for p in data["items"]:
        stats = p.get("stats", {})
        rows.append({
            "id": p.get("id"),
            "title": p.get("title"),
            "doi": p.get("doi"),
            "views": stats.get("number_of_views", 0),
            "forks": stats.get("number_of_forks", 0),
            "steps": p.get("number_of_steps"),
            "created": p.get("created_on")[:10] if p.get("created_on") else "n/a",
            "category": p.get("categories", [{}])[0].get("name", "n/a"),
        })

    df = pd.DataFrame(rows)
    df["popularity_score"] = df["views"] * 0.7 + df["forks"] * 0.3 * 100
    return df.sort_values("popularity_score", ascending=False)

# Compare western blotting protocols
df = search_and_rank("western blot protein detection", top_n=15)
df.to_csv("western_blot_protocols.csv", index=False)
print("Top western blot protocols:")
print(df[["title", "views", "forks", "steps"]].head(8).to_string(index=False))

Workflow 2: Protocol Step Extraction for Automation

Goal: Extract protocol steps, timing, and reagent volumes for downstream automation scripting.

import requests, pandas as pd

BASE = "https://www.protocols.io/api/v4"

def extract_protocol_steps(protocol_id):
    """Extract structured step data from a protocol."""
    r = requests.get(f"{BASE}/protocols/{protocol_id}")
    r.raise_for_status()
    protocol = r.json().get("payload", r.json())

    steps = []
    for i, step in enumerate(protocol.get("steps", []), 1):
        duration = step.get("duration", {})
        steps.append({
            "step_number": i,
            "description": step.get("description", ""),
            "duration_value": duration.get("duration"),
            "duration_unit": duration.get("unit_label", ""),
            "temperature": step.get("temperature", {}).get("value"),
            "temp_unit": step.get("temperature", {}).get("unit_label", ""),
        })

    materials = [{
        "name": m.get("name"),
        "quantity": m.get("quantity"),
        "unit": m.get("unit", {}).get("name", ""),
    } for m in protocol.get("materials", [])]

    return {
        "title": protocol.get("title"),
        "doi": protocol.get("doi"),
        "steps": pd.DataFrame(steps),
        "materials": pd.DataFrame(materials),
    }

result = extract_protocol_steps(45979)
print(f"Protocol: {result['title']}")
print(f"\nSteps ({len(result['steps'])}):")
print(result["steps"][["step_number", "description", "duration_value", "duration_unit"]].head(5).to_string(index=False))
print(f"\nMaterials ({len(result['materials'])}):")
print(result["materials"].head(5).to_string(index=False))

# Export for automation
result["steps"].to_csv("protocol_steps.csv", index=False)
result["materials"].to_csv("protocol_materials.csv", index=False)

Key Parameters

q

order_field

"relevance"

"relevance"

"views"

"date"

"activity"

page_size

10

1

50

page_id

1

filter[categories_ids][]

Best Practices

1. Sort by views for quality: Use

   order_field=views

   when searching for well-validated protocols, as highly-viewed protocols have been tested by many groups.

2. Always cite the specific DOI: protocols.io DOIs are versioned; cite the exact version DOI (not just the protocol title) in methods sections so readers can reproduce your exact protocol.

3. Check license before use: All public protocols.io protocols are CC-BY 4.0 by default. Commercial use requires checking individual protocol licenses.

4. Extract materials list for reagent ordering: The materials API returns catalog numbers and supplier names, enabling direct procurement list generation.

5. Store protocol ID + DOI for reproducibility: Record both the integer ID (for API access) and the DOI (for stable citation) when selecting protocols for a project.

Common Recipes

Recipe: Search by Reagent Name

When to use: Find protocols that use a specific commercial kit or reagent.

import requests

r = requests.get("https://www.protocols.io/api/v4/protocols",
                 params={"q": "RNeasy Mini Kit RNA extraction", "page_size": 5,
                         "order_field": "views"})
data = r.json()
print(f"Protocols using RNeasy: {data['pagination']['total_results']}")
for p in data["items"][:3]:
    print(f"  {p['title'][:70]} ({p.get('doi', 'n/a')})")

Recipe: Get Protocol Citation for Methods Section

When to use: Generate a citation string for a methods section.

import requests

protocol_id = 45979
r = requests.get(f"https://www.protocols.io/api/v4/protocols/{protocol_id}")
p = r.json().get("payload", r.json())
authors = "; ".join(a["name"] for a in p.get("creators", [])[:3])
print(f"Citation: {authors} ({p.get('created_on', '')[:4]}). ")
print(f"{p.get('title')}. protocols.io. https://doi.org/{p.get('doi')}")

Recipe: Find Most-Forked Protocols

When to use: Identify widely-adapted protocols (high forks = adapted by many labs).

import requests, pandas as pd

r = requests.get("https://www.protocols.io/api/v4/protocols",
                 params={"q": "ChIP-seq chromatin", "page_size": 20})
data = r.json()
df = pd.DataFrame([{
    "title": p["title"][:60],
    "forks": p.get("stats", {}).get("number_of_forks", 0),
    "views": p.get("stats", {}).get("number_of_views", 0),
} for p in data["items"]])
print(df.sort_values("forks", ascending=False).head(5).to_string(index=False))

Troubleshooting

items

HTTP 401

description

re.sub(r'<[^>]+>', '', text)

materials

if item.get("doi") == doi

time.sleep(0.15)

Related Skills

• opentrons-integration

  — Execute protocols on Opentrons liquid handling robots using steps extracted via this skill

• benchling-integration

  — Store retrieved protocols in Benchling ELN with reagent tracking

• scientific-manuscript-writing

  — Reference protocols correctly in methods sections using protocols.io DOIs

References

• protocols.io API documentation — Official REST API reference
• protocols.io platform — Public protocol library and search
• protocols.io citation guide — How to cite protocols.io in publications
• Teytelman et al. (2016) Nature Methods — protocols.io platform description paper