Claude-skill-registry bio-crispr-screens-base-editing-analysis
Analyzes base editing and prime editing outcomes including editing efficiency, bystander edits, and indel frequencies. Use when quantifying CRISPR base editor results, comparing ABE vs CBE efficiency, or assessing prime editing fidelity.
install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/base-editing-analysis" ~/.claude/skills/majiayu000-claude-skill-registry-bio-crispr-screens-base-editing-analysis && rm -rf "$T"
manifest:
skills/data/base-editing-analysis/SKILL.mdsource content
Base Editing Analysis
CRISPResso2 for Base Editing
# 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