Life Sciences Connector

Purpose

Connect to scientific databases (PubMed, Benchling) for protocol queries and biological data analysis with Biopython integration.

When to Use

• Biology research tasks
• Protocol searches
• Scientific data handling
• Sequence analysis
• Lab data integration
• HIPAA-compliant workflows

Core Instructions

PubMed Query

from Bio import Entrez
Entrez.email = "your.email@example.com"

def search_pubmed(term, retmax=5):
    """Search PubMed for articles"""
    handle = Entrez.esearch(db="pubmed", term=term, retmax=retmax)
    record = Entrez.read(handle)
    return record['IdList']

def fetch_article(pmid):
    """Fetch article details"""
    handle = Entrez.efetch(db="pubmed", id=pmid, rettype="xml")
    return Entrez.read(handle)

# Usage
results = search_pubmed("CRISPR protocol")
for pmid in results:
    article = fetch_article(pmid)
    print(article['Title'])

Sequence Analysis

from Bio import SeqIO
from Bio.Align import PairwiseAligner

# Parse FASTA
sequences = list(SeqIO.parse("sequences.fasta", "fasta"))

# Align sequences
aligner = PairwiseAligner()
alignments = aligner.align(sequences[0].seq, sequences[1].seq)
print(f"Alignment score: {alignments[0].score}")

HIPAA Compliance

def anonymize_patient_data(data):
    """
    Anonymize patient information (HIPAA)
    """
    # Remove PHI (Protected Health Information)
    phi_fields = [
        'name', 'address', 'phone', 'email',
        'ssn', 'medical_record_number'
    ]

    anonymized = data.copy()
    for field in phi_fields:
        if field in anonymized:
            anonymized[field] = hash_or_remove(field, data[field])

    return anonymized

Guidelines

• Accuracy: Cross-validate sources
• Privacy: Anonymize patient data (HIPAA)
• Citations: Always cite sources
• Verification: Cross-check protocols

Dependencies

• Python 3.8+
• biopython
• requests
• PubMed Entrez API access

Version

v1.0.0 (2025-10-23)