/bio-crispr-screens-crispresso-editing

name: bio-crispr-screens-crispresso-editing
description: CRISPResso2 for analyzing CRISPR gene editing outcomes. Quantifies indels, HDR efficiency, and generates comprehensive editing reports. Use when analyzing amplicon sequencing data from CRISPR editing experiments to assess editing efficiency.
tool_type: cli
primary_tool: CRISPResso2

Version Compatibility

Reference examples tested with: CRISPResso2 2.2+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: `pip show <package>` then `help(module.function)` to check signatures
• CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

CRISPResso2 Editing Analysis

**"Quantify CRISPR editing from my amplicon data"** → Analyze amplicon sequencing to measure indel frequencies, HDR efficiency, and frameshift rates from CRISPR gene editing experiments.

• CLI: `CRISPResso --fastq_r1 reads.fq --amplicon_seq ATGC --guide_seq GUIDE`

Basic Analysis

**Goal:** Quantify CRISPR editing outcomes from amplicon sequencing of a single target site.

**Approach:** Align amplicon reads against the reference and guide sequences with CRISPResso, which reports indel frequencies, allele tables, and editing efficiency plots.

# Analyze single amplicon
CRISPResso \
    --fastq_r1 sample_R1.fastq.gz \
    --fastq_r2 sample_R2.fastq.gz \
    --amplicon_seq AATGTCCCCCAATGGGAAGTTCATCTGGCACTGCCCACAGGTGAGGAGGTCATGATCCCCTTCTGGAGCTCCCAACGGGCCGTGGTCTGGTTCATCATCTGTAAGAATGGCTTCAAGAGGCTCGGCTGTGGTT \
    --guide_seq CTGCCCACAGGTGAGGAGGT \
    --output_folder crispresso_output \
    --name sample1

# Output includes:
# - Editing efficiency statistics
# - Indel distribution
# - Allele frequency plots

With HDR Template

# Analyze HDR editing
CRISPResso \
    --fastq_r1 hdr_sample_R1.fastq.gz \
    --fastq_r2 hdr_sample_R2.fastq.gz \
    --amplicon_seq AATGTCCCCCAATGGGAAGTTCATCTGGCACTGCCCACAGGTGAGGAGGTCATGATCCCCTTCTGGAGCTCCCAACGGGCCGTGGTCTGGTTCATCATCTGTAAGAATGGCTTCAAGAGGCTCGGCTGTGGTT \
    --guide_seq CTGCCCACAGGTGAGGAGGT \
    --expected_hdr_amplicon_seq AATGTCCCCCAATGGGAAGTTCATCTGGCACTGCCCACAGGTGAGGAGGTCATGATCCCCTTCTGGAGCTCCCAACGGGCCGTGGTCTGGTTCATCATCTGTAAGAATGGCTTCAAGATGCTCGGCTGTGGTT \
    --output_folder hdr_output \
    --name hdr_sample

Batch Analysis

**Goal:** Process multiple CRISPR editing samples in a single run.

**Approach:** Define a batch file listing sample names, FASTQ paths, amplicon sequences, and guide sequences, then run CRISPRessoBatch for parallel multi-sample analysis.

# Create batch file (tab-separated)
# batch.txt:
# name    fastq_r1    fastq_r2    amplicon_seq    guide_seq
# sample1 s1_R1.fq.gz s1_R2.fq.gz AMPLICON1       GUIDE1
# sample2 s2_R1.fq.gz s2_R2.fq.gz AMPLICON2       GUIDE2

CRISPRessoBatch \
    --batch_settings batch.txt \
    --output_folder batch_output \
    --n_processes 8

Pool Analysis (Multiple Guides)

# Analyze pooled amplicons
CRISPRessoPooled \
    --fastq_r1 pooled_R1.fastq.gz \
    --fastq_r2 pooled_R2.fastq.gz \
    --amplicon_file amplicons.txt \
    --output_folder pooled_output \
    --n_processes 8

# amplicons.txt format:
# amplicon_name    amplicon_seq    guide_seq

WGS Analysis

# Analyze off-target editing from WGS
CRISPRessoWGS \
    --bam aligned.bam \
    --reference genome.fa \
    --regions_file targets.bed \
    --output_folder wgs_output

Parse Results in Python

**Goal:** Extract editing metrics from CRISPResso output for downstream analysis or reporting.

**Approach:** Load the mapping statistics and quantification files from the CRISPResso output directory, and parse the compressed allele frequency table for allele-level detail.

import pandas as pd
import json

# Load mapping statistics
with open('crispresso_output/CRISPResso_mapping_statistics.txt') as f:
    stats = {}
    for line in f:
        key, value = line.strip().split('\t')
        stats[key] = value

print(f"Reads aligned: {stats['READS_ALIGNED']}")
print(f"Reads aligned %: {stats['READS_ALIGNED_PERCENTAGE']}")

# Load quantification
quant = pd.read_csv('crispresso_output/CRISPResso_quantification_of_editing_frequency.txt', sep='\t')
print(quant)

# Load allele frequency
alleles = pd.read_csv('crispresso_output/Alleles_frequency_table.zip', compression='zip', sep='\t')
print(f"Unique alleles: {len(alleles)}")
print(alleles.head(10))

Key Output Files

CRISPResso_output/
├── CRISPResso_mapping_statistics.txt    # Read mapping stats
├── CRISPResso_quantification_of_editing_frequency.txt  # Summary
├── Alleles_frequency_table.zip          # All allele sequences
├── CRISPResso_RUNNING_LOG.txt           # Analysis log
├── Indel_histogram.png                  # Indel size distribution
├── Insertion_deletion_substitution.png  # Edit type pie chart
├── Alleles_frequency_table.png          # Top allele bar plot
└── CRISPResso2_info.json               # Machine-readable summary

Quantify Specific Outcomes

# Define expected outcomes
CRISPResso \
    --fastq_r1 sample_R1.fastq.gz \
    --amplicon_seq AMPLICON \
    --guide_seq GUIDE \
    --coding_seq CODING_REGION \
    --quantification_window_size 5 \
    --quantification_window_center -3 \
    --output_folder output

Base Editing Analysis

# For base editors (CBE/ABE)
CRISPResso \
    --fastq_r1 base_edit_R1.fastq.gz \
    --amplicon_seq AMPLICON \
    --guide_seq GUIDE \
    --base_editor_output \
    --conversion_nuc_from C \
    --conversion_nuc_to T \
    --output_folder base_edit_output

Prime Editing Analysis

# For prime editing
CRISPResso \
    --fastq_r1 prime_edit_R1.fastq.gz \
    --amplicon_seq AMPLICON \
    --guide_seq GUIDE \
    --prime_editing_pegRNA_spacer_seq SPACER \
    --prime_editing_pegRNA_extension_seq EX