Bio Crispr Screens Base Editing Analysis

Approach: Run CRISPResso with --base_editor_output and the expected edited amplicon sequence to measure target base conversion, bystander edits, and indel frequencies.

# Analyze base editing with expected outcome
CRISPResso --fastq_r1 reads.fq.gz \
    --amplicon_seq ATGCGATCGATCGATCGATCGATCG \
    --guide_seq TCGATCGATCGATCGAT \
    --expected_hdr_amplicon_seq ATGCGATCGATCGTTCGATCGATCG \
    --base_editor_output \
    -o results/

Key Metrics

Base Editor Types

Cytosine Base Editors (CBE)

# C->T conversion (or G->A on opposite strand)
CRISPResso --fastq_r1 reads.fq.gz \
    --amplicon_seq $AMPLICON \
    --guide_seq $GUIDE \
    --base_editor_output \
    --conversion_nuc_from C \
    --conversion_nuc_to T

Adenine Base Editors (ABE)

# A->G conversion (or T->C on opposite strand)
CRISPResso --fastq_r1 reads.fq.gz \
    --amplicon_seq $AMPLICON \
    --guide_seq $GUIDE \
    --base_editor_output \
    --conversion_nuc_from A \
    --conversion_nuc_to G

Prime Editing Analysis

# Prime editing with pegRNA
CRISPResso --fastq_r1 reads.fq.gz \
    --amplicon_seq $AMPLICON \
    --guide_seq $SPACER \
    --expected_hdr_amplicon_seq $EDITED_AMPLICON \
    --prime_editing_pegRNA_extension_seq $EXTENSION \
    -o prime_edit_results/

Editing Window Analysis

import pandas as pd

# Load CRISPResso quantification
quant = pd.read_csv('CRISPResso_output/Quantification_window_nucleotide_percentage_table.txt',
                    sep='\t')

# Calculate per-position editing
editing_window = quant[(quant['Position'] >= -5) & (quant['Position'] <= 5)]

Quality Thresholds

• Editing efficiency: >30% considered good for most applications
• Indel rate: <5% ideal for base editors
• Bystander rate: depends on application; <10% often acceptable

Related Skills

• crispr-screens/crispresso-editing - General editing QC
• crispr-screens/library-design - Guide design considerations
• variant-calling/vcf-basics - Downstream variant analysis