majiayu000 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)