Analyze CRISPR base editing and prime editing results from sequencing data for a target region. Use CRISPResso2 to quantify intended changes, unintended nearby edits, and DNA insertions or deletions, and compare editing outcomes.
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.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Reference examples tested with: CRISPResso2 2.2+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signatures<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"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.
CRISPResso --fastq_r1 reads.fq --amplicon_seq ATGC --base_editor_outputGoal: 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/
| 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 |
# 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
# 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 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/
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)]
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Support protein design with cloud laboratory testing for binding, expression, thermal stability, and enzyme activity. Covers sequence optimization, submitting experiments through the Adaptyv API, checking progress, and downloading experimental results.
Build flexible daily plans that account for ADHD-related difficulties with time estimation and getting started. Narrow priorities to three tasks, add transition buffers, and choose small next actions that match the energy available that day.
Build Python workflows for data recorded over time, including classification, forecasting, anomaly detection, and pattern search. Using aeon, the skill guides preprocessing, algorithm selection, feature extraction, and evaluation through interfaces compatible with scikit-learn.
Use a browser to research topics, extract page information, fill forms, and test websites. Guides agents through identifying page controls, interacting with web apps, saving screenshots or PDFs, and reusing saved login sessions.
Combine health measurements, exercise, sleep, nutrition, and mental health records to examine trends, unusual changes, and estimated risks. The skill produces personalized suggestions and text or interactive HTML reports using local data without external API calls.
Predict structures for designed protein sequences and protein complexes with AlphaFold2 or ColabFold. Inspect structure files and confidence scores to assess folding and chain interfaces, with guidance for local runs, Modal execution, and common prediction errors.