CRISPR TP53 Guide Design: End-to-End Workflow — Evidence Lab

Overview

TP53 is the most frequently mutated gene in human cancers. Designing effective CRISPR guide RNAs to knock out TP53 requires balancing on-target efficiency with minimizing off-target effects. In this episode, we walk through the complete Hordago pipeline for CRISPR guide design.

Step 1: Define the Target

We start by specifying the gene, genome assembly, and cell line context:

hordago crispr design --gene TP53 --cell-line A549

The pipeline automatically:

Resolves TP53 to its canonical transcript (ENST00000269305)
Identifies all exons in the coding sequence
Scans for PAM sites (NGG) within exonic regions

Step 2: On-Target Scoring

Each candidate guide RNA is scored using CRISPRon, which predicts cutting efficiency based on sequence features and chromatin accessibility:

import pandas as pd

guides = pd.read_csv("guides.tsv", sep="\t")
print(guides[["guide_id", "sequence", "on_target_score"]].head())

guide_id	sequence	on_target_score
TP53-g1	GCAGCCTTTGTGAACCAACA	0.92
TP53-g2	TGGTTCTCACTTGGTGGAAG	0.89
TP53-g3	AGCAGGTCTGTTCCAAGGGA	0.87

Step 3: Off-Target Analysis

Cas-OFFinder scans the entire genome for potential off-target sites, allowing up to 3 mismatches:

cas-offinder input.txt G output.txt

Results show TP53-g3 has the fewest off-target hits, while TP53-g1 has the highest on-target score — a classic tradeoff.

TP53-g1: 12 off-target sites (max 3 mismatches)
TP53-g2: 18 off-target sites
TP53-g3: 5 off-target sites

Step 4: Final Ranking

The pipeline combines on-target and off-target scores into a composite ranking:

RESULTS: 3 guides ranked
  TP53-g1  GCAGCCTTTGTGAACCAACA  on=0.92  off=0.02  rank=1
  TP53-g3  AGCAGGTCTGTTCCAAGGGA  on=0.87  off=0.01  rank=2
  TP53-g2  TGGTTCTCACTTGGTGGAAG  on=0.89  off=0.04  rank=3

Before & After

Before: Manual workflow

3+ hours of manual tool switching
No reproducibility guarantee
Results scattered across browser tabs

After: Hordago pipeline

4.2 seconds end-to-end
Full provenance manifest
Ranked output with composite scoring

Key Takeaways

Automated pipelines eliminate human error in multi-tool workflows
Provenance tracking ensures every result can be reproduced
Composite scoring surfaces the best guide by balancing efficiency vs. safety
The pipeline’s cell-line context (A549) factors in chromatin state, which improves on-target predictions