bio-crispr-screens-crispresso-editing

## 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.

```bash
# 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

```bash
# 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.

```bash
# 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)

```bash
# 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

```bash
# 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.

```python
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

```bash
# 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

```bash
# 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

```bash
# 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

```bash
# 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