Bio crispr screens base editing analysis

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.

Base Editing Analysis

"Analyze my base editing outcomes" → Quantify base editing efficiency, bystander edits, and indel frequencies from amplicon sequencing data for CBE, ABE, and prime editing experiments.

• CLI: CRISPResso --fastq_r1 reads.fq --amplicon_seq ATGC --base_editor_output

CRISPResso2 for Base Editing

Goal: Quantify base editing efficiency and bystander edits from amplicon sequencing.

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

Metric	Description
Editing efficiency	% reads with target base change
Bystander edits	Unintended edits in editing window
Indel frequency	Insertions/deletions (should be low)
Purity	Target edit without bystanders

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