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Base Editing Analysis Usage Guide

Overview

This guide covers analyzing base editing and prime editing outcomes from CRISPR experiments.

Prerequisites

# CRISPResso2
pip install CRISPResso2

# Or via conda
conda install -c bioconda crispresso2

Quick Start

Tell your AI agent what you want to do:

  • "Analyze my base editing experiment for C-to-T conversion efficiency"
  • "Quantify bystander edits in my ABE data"
  • "Compare editing efficiency across multiple guides"
  • "Assess prime editing outcomes from my amplicon sequencing"

Example Prompts

Base Editing

"Analyze my CBE experiment. The target is a C-to-T conversion at position 6 in the editing window"

"Calculate the editing efficiency and bystander rate for my adenine base editor samples"

Prime Editing

"Quantify prime editing outcomes including correct edits, partial edits, and indels"

"Compare PE2 vs PE3 efficiency from my amplicon data"

Quality Assessment

"Check if my base editing has acceptable indel rates (should be <5%)"

"Identify which samples have high bystander editing"

What the Agent Will Do

  1. Set up CRISPResso2 with base editor parameters
  2. Configure expected conversions (C->T or A->G)
  3. Run analysis on amplicon sequencing data
  4. Extract editing efficiency and bystander metrics
  5. Generate summary statistics and plots

Tips

  • Ensure amplicon covers the entire editing window
  • Use high-quality paired-end reads for best results
  • Include unedited control samples for comparison
  • Low indel rates (<5%) indicate clean base editing
  • Consider strand when interpreting conversion types