Design armored CAR-T cells with cytokine payloads and resistance mechanisms.
tooluniverse-crispr-screen-analysis
维护者 FreedomIntelligence · 最近更新 2026年4月1日
CRISPR screens enable genome-wide functional genomics by systematically perturbing genes and measuring fitness effects. This skill provides an 8-phase workflow for: - Processing sgRNA count matrices - Quality control and normali.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-crispr-screen-analysis
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Comprehensive skill ,用于 analyzing CRISPR-Cas9 genetic screens to identify essential genes,synthetic lethal interactions,、 therapeutic targets through robust 统计分析 、 pathway enrichment。
- Processing sgRNA count matrices。
- Quality control 、 normalization。
- Gene-level essentiality scoring (MAGeCK-like 、 BAGEL-like approaches)。
- Synthetic lethality detection。
原始文档
SKILL.md 摘录
Phase 1: Data Import & sgRNA Count Processing
Load sgRNA Count Matrix
import pandas as pd
import numpy as np
def load_sgrna_counts(counts_file):
"""
Load sgRNA count matrix from MAGeCK format or generic TSV.
Expected format:
sgRNA | Gene | Sample1 | Sample2 | Sample3 | ...
sgRNA_1 | BRCA1 | 1500 | 1200 | 1100 | ...
sgRNA_2 | BRCA1 | 1800 | 1500 | 1400 | ...
"""
counts = pd.read_csv(counts_file, sep='\t')
# Validate required columns
required_cols = ['sgRNA', 'Gene']
if not all(col in counts.columns for col in required_cols):
raise ValueError(f"Missing required columns: {required_cols}")
# Extract sample columns
sample_cols = [col for col in counts.columns if col not in ['sgRNA', 'Gene']]
# Create count matrix
count_matrix = counts[sample_cols].copy()
count_matrix.index = counts['sgRNA']
# Gene mapping
sgrna_to_gene = dict(zip(counts['sgRNA'], counts['Gene']))
metadata = {
'n_sgrnas': len(counts),
'n_genes': counts['Gene'].nunique(),
'n_samples': len(sample_cols),
'sample_names': sample_cols,
'sgrna_to_gene': sgrna_to_gene
}
return count_matrix, metadata
## Load counts
counts, meta = load_sgrna_counts("sgrna_counts.txt")
print(f"Loaded {meta['n_sgrnas']} sgRNAs targeting {meta['n_genes']} genes across {meta['n_samples']} samples")
python
def create_design_matrix(sample_names, conditions, timepoints=None):
"""
Create experimental design linking samples to conditions.
Example:
Sample | Condition | Timepoint | Replicate
T0_rep1 | baseline | 0 | 1
T14_rep1 | treatment | 14 | 1
"""
design = pd.DataFrame({
'Sample': sample_names,
'Condition': conditions
})
if timepoints is not None:
design['Timepoint'] = timepoints
# Auto-detect replicates
design['Replicate'] = design.groupby('Condition').cumcount() + 1
return design
## Example usage
sample_names = ['T0_rep1', 'T0_rep2', 'T14_rep1', 'T14_rep2', 'T14_rep3']
conditions = ['baseline', 'baseline', 'treatment', 'treatment', 'treatment']
design = create_design_matrix(sample_names, conditions)
适用场景
- 可用于CRISPR screen analysis,gene essentiality studies,synthetic lethality detection,functional genomics,drug target validation,或 identifying genetic vulnerabilities。
不适用场景
- Do not rely on this catalog entry alone ,用于 installation 或 maintenance details。
相关技能
相关技能
arxiv-search
Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic sea…
Autonomous oncology research agent: literature mining, trial matching, biomarker analysis, and treatment hypothesis gene…
Preprocesses cell-free DNA sequencing data including adapter trimming, alignment optimized for short fragments, and UMI-…