Deterministic Retrieval with gget & AI

Before You Begin: Install and Test Retrieval Separately

Separate data retrieval from AI interpretation. First prove that gget returns the expected record with a reproducible command. Only then provide that saved output to an AI assistant for explanation.

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip gget
python -c "import gget; print('gget import: OK')"
mkdir -p retrieval-results

Pin the package version after the first successful run, record the query and date, and save the raw response. Do not ask the model to invent a value when the retrieval command fails; fix the query or report the missing result explicitly.

Success check: a second run with the same query produces a saved response whose source, identifier, and retrieval date are recorded.

Introducing gget for Reliable Data Retrieval

gget is a free, open-source command-line tool and Python package that efficiently queries large biological databases (Ensembl, UniProt, NCBI). By instructing your AI coding assistant to use gget, you ensure the data it relies on is 100% accurate.

Step 1: Installing and Verifying gget

Install gget in the same environment where your AI agent executes code. Pin the version to ensure reproducibility:

pip install gget==0.28.6

# Verify the installation and check available modules
gget --help
# Expected output: list of subcommands (info, seq, search, enrichr, archs4, ...)

gget Module Reference

The gget package provides multiple modules, each querying a different biological database:

Step 2: Retrieving Gene Information Programmatically

The key to this workflow is using gget as a Python library to retrieve verified data before any AI-generated analysis begins:

import gget
import json

# Retrieve gene information from Ensembl
result = gget.info("FOXP3", species="human")
print(result.to_json(indent=2))

# Expected output includes:
# - ensembl_id: "ENSG00000049768"
# - uniprot_id: "Q9BZS1"
# - chromosome: "X"
# - biotype: "protein_coding"
# - description: "forkhead box P3"

# Save the verified result for reproducibility
result.to_csv("retrieval-results/foxp3_info.csv", index=False)
print(f"Retrieved Ensembl ID: {result.iloc[0]['ensembl_id']}")

Why this matters: If you ask ChatGPT or Copilot "What is the Ensembl ID for FOXP3?", it may return an outdated ID or confuse it with a pseudogene. The gget call queries the live Ensembl API and returns the current, authoritative record.

Step 3: Retrieving Sequences for Downstream Analysis

Use gget seq to download FASTA sequences directly from Ensembl:

import gget

# Retrieve the protein sequence for TP53
fasta = gget.seq("ENSG00000141510", translate=True)
print(fasta[:200])  # Print first 200 characters

# Save to file for alignment or structural prediction
with open("retrieval-results/tp53_protein.fasta", "w") as f:
    f.write(fasta)

# Calculate basic sequence statistics
from collections import Counter
seq = fasta.split("\n", 1)[1].replace("\n", "")
aa_counts = Counter(seq)
print(f"Sequence length: {len(seq)} amino acids")
print(f"Most common residues: {aa_counts.most_common(5)}")

Step 4: Combining gget with Gene Set Enrichment

A common workflow is to take a list of differentially expressed genes and run enrichment analysis:

import gget

# Run enrichment analysis on a gene list from your DEG analysis
deg_genes = ["FOXP3", "IL2RA", "CTLA4", "IKZF2", "TIGIT"]

enrichment = gget.enrichr(
    genes=deg_genes,
    database="GO_Biological_Process_2023",
    species="human"
)

# Display top 5 enriched pathways
print(enrichment[["Term", "Adjusted P-value"]].head())
enrichment.to_csv("retrieval-results/treg_enrichment.csv", index=False)

Common Pitfalls and Troubleshooting

• Species mismatch: Always specify species="human" or species="mouse" explicitly. The default species may change between gget versions.
• Gene symbol ambiguity: Some symbols map to multiple genes (e.g., "ACE" vs "ACE2"). Use Ensembl IDs when precision is critical.
• API rate limits: If querying hundreds of genes in a loop, add a time.sleep(0.5) delay between calls to avoid being rate-limited by Ensembl.
• Offline environments: gget requires internet access. On air-gapped HPC systems, download results on a login node and transfer the CSV files to compute nodes.

Conclusion

Deterministic retrieval bridges the gap between the creative problem-solving of LLMs and the absolute precision required in bioinformatics. By providing tools like gget to your AI agents, you eliminate data hallucinations and build reproducible workflows.