跳到正文
cccskills
免费在 GitHub 公开

bio-crispr-screens-crispresso-editing

Analyze CRISPR sequencing data with CRISPResso2 to measure editing efficiency, insertions and deletions, and template-directed repair. Generate editing reports and allele frequency tables, process multiple samples, and compare outcomes between experiments.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

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.

实测评分 未实测

查看安装方法

适用场景

  • Measure editing efficiency at a target site
  • Quantify template-directed repair efficiency
  • Process multiple samples or pooled amplicons
  • Analyze base editing and prime editing outcomes
  • Compare editing results between two samples

包含的文件(3)

  • SKILL.md6.4 KB
  • examples/crispresso_analysis.sh1.3 KB
  • usage-guide.md2.5 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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 EXTENSION \
    --prime_editing_pegRNA_scaffold_seq SCAFFOLD \
    --output_folder prime_edit_output

Compare Samples

# Compare two CRISPResso runs
CRISPRessoCompare \
    --crispresso_output_folder_1 sample1_output \
    --crispresso_output_folder_2 sample2_output \
    --output_folder comparison_output

Related Skills

  • screen-qc - QC for editing experiments
  • read-alignment/bwa-alignment - Align reads for WGS analysis
  • variant-calling/variant-calling - Detect editing-induced variants

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

adaptyv

免费

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.

中文简介正在准备中。当前显示英语简介。

  • Validate designed proteins with laboratory assays
  • Screen and redesign sequences for better solubility
  • Submit protein testing experiments through an API
FreedomIntelligence/OpenClaw-Medical-Skills30802026年7月21日 更新

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.

中文简介正在准备中。当前显示英语简介。

  • Choosing three priorities for today
  • Planning a schedule with transition buffers
  • Finding a small first step when starting feels difficult
FreedomIntelligence/OpenClaw-Medical-Skills30802026年7月21日 更新

aeon

免费

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.

中文简介正在准备中。当前显示英语简介。

  • Classify time series by their patterns
  • Forecast future values from historical data
  • Detect anomalies and change points
FreedomIntelligence/OpenClaw-Medical-Skills30802026年7月21日 更新

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.

中文简介正在准备中。当前显示英语简介。

  • Researching topics and reading web articles
  • Filling and submitting online forms
  • Extracting page text and links
FreedomIntelligence/OpenClaw-Medical-Skills30802026年7月21日 更新

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.

中文简介正在准备中。当前显示英语简介。

  • Review health records together in a comprehensive report
  • Explore links between sleep, mood, exercise, and weight
  • Summarize estimated disease and nutrition risks
FreedomIntelligence/OpenClaw-Medical-Skills30802026年7月21日 更新

alphafold

免费

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.

中文简介正在准备中。当前显示英语简介。

  • Check predicted folding of a designed protein sequence
  • Predict a binder–target protein complex
  • Review structure and interface confidence scores
FreedomIntelligence/OpenClaw-Medical-Skills30802026年7月21日 更新

查看 FreedomIntelligence 的全部技能

报告此技能的问题