Synthego ICE

https://github.com/synthego-open/ice
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Synthego ICE is a CRISPR editing analysis tool that infers the presence of indels and other...
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### Install Synthego ICE

Source: https://context7.com/synthego-open/ice/llms.txt

Set up the environment and install the package via pip.

```bash
# Create and activate a virtual environment
conda create --name ice_env python=3
source activate ice_env

# Install synthego_ice from pip
pip install synthego_ice
```

--------------------------------

### Install Synthego ICE via Pip

Source: https://github.com/synthego-open/ice/blob/master/README.md

Set up a Python virtual environment and install the package using pip.

```bash
conda create --name ice_env python=3 # create a python3 virtual environment

source activate ice_env # activate the virtual environment

pip install sythego_ice # install synthego ice from pip
```

--------------------------------

### Clone Repository for Example Data

Source: https://github.com/synthego-open/ice/blob/master/README.md

Retrieve the repository to access test data files required for analysis examples.

```bash
git clone git@github.com:synthego-open/ice.git ice

cd ice # change into the ice directory
```

--------------------------------

### Install Local Python Dependencies

Source: https://github.com/synthego-open/ice/blob/master/DEVELOP.md

Clone the repository, navigate to the directory, and install Python dependencies using pip. Requires an activated virtual environment.

```bash
git clone git@github.com/synthego-open/ice.git ice
cd ice
pip install -r requirements.txt
```

--------------------------------

### Create and Activate Conda Environment

Source: https://github.com/synthego-open/ice/blob/master/DEVELOP.md

Create a new Python 3 virtual environment using conda and activate it. This is a prerequisite for local installation.

```bash
conda create --name ice_env python=3
source activate ice_env
```

--------------------------------

### Run Batch ICE Analysis Locally

Source: https://github.com/synthego-open/ice/blob/master/DEVELOP.md

Execute a batch ICE analysis using the locally installed Python scripts. Ensure you are in the cloned repository directory.

```bash
./ice_analysis_batch.py \
	--in ./ice/tests/test_data/batch_example.xlsx \
	--out ./results/ \
	--data ./ice/tests/test_data/
	--verbose
```

--------------------------------

### Perform Multiplex Analysis with Python

Source: https://context7.com/synthego-open/ice/llms.txt

Use the Python API to analyze experiments with multiple guide RNAs by providing comma-separated sequences.

```python
from ice.analysis import single_sanger_analysis

# Multiplex analysis with two guides
results = single_sanger_analysis(
    control_path='./ice/tests/test_data/multiplex0_control.ab1',
    sample_path='./ice/tests/test_data/multiplex0_edited.ab1',
    base_outputname='./results/multiplex',
    guide='AACCAGTTGCAGGCGCCCCA,GCGATCGATCGATCGATCGA',  # Comma-separated guides
    donor=None,
    verbose=True
)

# Results include edits attributed to each guide (g1, g2)
# and potential dropout events between cut sites
print(f"Total editing: {results['ice']}%")
```

--------------------------------

### Run Single ICE Analysis Locally

Source: https://github.com/synthego-open/ice/blob/master/DEVELOP.md

Execute a single ICE analysis using the locally installed Python scripts. Ensure you are in the cloned repository directory.

```bash
./ice_analysis_single.py \
	--control ./ice/tests/test_data/good_example_control.ab1  \
	--edited ./ice/tests/test_data/good_example_edited.ab1 \
	--target AACCAGTTGCAGGCGCCCCA \
	--out results/testing \
	--verbose
```

--------------------------------

### ICE Output JSON Structure

Source: https://context7.com/synthego-open/ice/llms.txt

Example of the JSON output format containing sequence-level editing outcomes and indel distributions.

```json
{
  "contribs_list": [
    {
      "rel_abundance": 0.301,
      "human_readable": "CCCAACACAACCAGTTGCAGGCGCC|-CATGGTGAGCATCAGCCTCTGGGTGGCCCTCCCTCTGGGCCTCGGGTAT",
      "indel": "-1[g1]",
      "wt": false
    },
    {
      "rel_abundance": 0.2,
      "human_readable": "CCCAACACAACCAGTTGCAGGCGCC|CCATGGTGAGCATCAGCCTCTGGGTGGCCCTCCCTCTGGGCCTCGGGTAT",
      "indel": "0[g1]",
      "wt": false
    }
  ],
  "editing_outcomes": {
    "-1": 33.23,
    "0": 19.96,
    "1": 18.18,
    "-2": 11.28
  }
}
```

--------------------------------

### Analyze Single Sample via Command Line

Source: https://github.com/synthego-open/ice/blob/master/README.md

Run ICE analysis on a single pair of control and edited ab1 files.

```bash
synthego_ice \
	--control ./ice/tests/test_data/good_example_control.ab1 \
	--edited ./ice/tests/test_data/good_example_edited.ab1 \
	--target AACCAGTTGCAGGCGCCCCA \
	--out results/testing \
	--verbose
```

--------------------------------

### Analyze Multiple Samples via Command Line

Source: https://github.com/synthego-open/ice/blob/master/README.md

Perform batch analysis using an Excel definition file and a directory of ab1 files.

```bash
synthego_ice_batch \
	--in ./ice/tests/test_data/batch_example.xlsx \
	--out ./results/ \
	--data ./ice/tests/test_data/
	--verbose
```

--------------------------------

### Run Batch ICE Analysis with Docker

Source: https://github.com/synthego-open/ice/blob/master/DEVELOP.md

Execute a batch ICE analysis using the locally built Docker image. Mounts the current directory as a volume and sets the working directory.

```bash
docker run -it -v ${PWD}:/data -w /ice -i ice:latest \
	python ice_analysis_batch.py\
	--in /data/ice/tests/test_data/batch_example.xlsx \
	--data /data/ice/tests/test_data/ \
	--out /data/results/ \
	--verbose
```

--------------------------------

### Analyze Multiple Samples via Docker

Source: https://github.com/synthego-open/ice/blob/master/README.md

Execute batch analysis within a Docker container using an Excel input file.

```bash
docker run -it -v ${PWD}:/data -w /ice -i ice:latest \
	python ice_analysis_batch.py \
	--in /data/ice/tests/test_data/batch_example.xlsx \
	--data /data/ice/tests/test_data/ \
	--out /data/results/ \
	--verbose
```

--------------------------------

### Run ICE Analysis with Docker

Source: https://context7.com/synthego-open/ice/llms.txt

Execute single or batch analysis using the Synthego ICE Docker container.

```bash
docker run -it -v ${PWD}:/data -w /ice -i synthego/ice:latest \
    python ice_analysis_single.py \
    --control /data/ice/tests/test_data/good_example_control.ab1 \
    --edited /data/ice/tests/test_data/good_example_edited.ab1 \
    --target AACCAGTTGCAGGCGCCCCA \
    --out /data/results/docker_analysis \
    --verbose
```

```bash
docker run -it -v ${PWD}:/data -w /ice -i synthego/ice:latest \
    python ice_analysis_batch.py \
    --in /data/ice/tests/test_data/batch_example.xlsx \
    --data /data/ice/tests/test_data/ \
    --out /data/results/ \
    --verbose
```

--------------------------------

### Build Docker Image Locally

Source: https://github.com/synthego-open/ice/blob/master/DEVELOP.md

Build the Docker image for the ICE project. This command assumes you have cloned the repository and are in the root directory.

```bash
git clone git@github.com/synthego-open/ice.git ice
cd ice
docker build . -t ice
```

--------------------------------

### Run Single ICE Analysis with Docker

Source: https://github.com/synthego-open/ice/blob/master/DEVELOP.md

Execute a single ICE analysis using the locally built Docker image. Mounts the current directory as a volume and sets the working directory.

```bash
docker run -it -v ${PWD}:/data -w /ice -i ice:latest \
	python ice_analysis_single.py\
	--control /data/ice/tests/test_data/good_example_control.ab1 \
	--edited /data/ice/tests/test_data/good_example_edited.ab1 \
	--target AACCAGTTGCAGGCGCCCCA \
	--out /data/results/testing \
	--verbose
```

--------------------------------

### Analyze Single Sample via Docker

Source: https://github.com/synthego-open/ice/blob/master/README.md

Execute single-sample analysis within a Docker container, mounting the local directory to access data.

```bash
docker run -it -v ${PWD}:/data -w /ice -i ice:latest \
	python ice_analysis_single.py \
	--control /data/ice/tests/test_data/good_example_control.ab1 \
	--edited /data/ice/tests/test_data/good_example_edited.ab1 \
	--target AACCAGTTGCAGGCGCCCCA \
	--out /data/results/testing \
	--verbose
```

--------------------------------

### Docker Usage

Source: https://context7.com/synthego-open/ice/llms.txt

Instructions for running ICE analysis using the official Docker container.

```APIDOC
## Docker Usage

### Description
Run ICE analysis using the official Docker container without local installation.

### Commands
1. **Pull the latest ICE Docker image:**
   ```bash
   docker pull synthego/ice
   ```
2. **Clone repository for test data (optional):**
   ```bash
   git clone git@github.com:synthego-open/ice.git ice
   cd ice
   ```
```

--------------------------------

### Analyze Single Sample via CLI

Source: https://context7.com/synthego-open/ice/llms.txt

Run analysis on a control and edited sample pair. Optionally include a donor sequence for HDR analysis.

```bash
synthego_ice \
    --control ./ice/tests/test_data/good_example_control.ab1 \
    --edited ./ice/tests/test_data/good_example_edited.ab1 \
    --target AACCAGTTGCAGGCGCCCCA \
    --out results/my_analysis \
    --verbose

# Optional: Include donor sequence for HDR analysis
synthego_ice \
    --control ./ice/tests/test_data/donor_example_control.ab1 \
    --edited ./ice/tests/test_data/donor_example_knockin.ab1 \
    --target AACCAGTTGCAGGCGCCCCA \
    --donor ATGCGATCGATCGATCGATCG \
    --out results/hdr_analysis \
    --verbose
```

--------------------------------

### Analyze Batch Samples via CLI

Source: https://context7.com/synthego-open/ice/llms.txt

Process multiple samples defined in an Excel file. You can run the entire batch or specify a single line.

```bash
# Create batch_example.xlsx with columns:
# Label | Control File | Experiment File | Guide Sequence | Donor Sequence (optional)

synthego_ice_batch \
    --in ./ice/tests/test_data/batch_example.xlsx \
    --data ./ice/tests/test_data/ \
    --out ./results/ \
    --verbose

# Optional: Run only a specific line from the Excel file
synthego_ice_batch \
    --in ./ice/tests/test_data/batch_example.xlsx \
    --data ./ice/tests/test_data/ \
    --out ./results/ \
    --line 0 \
    --verbose
```

--------------------------------

### Run ICE analysis using Docker

Source: https://context7.com/synthego-open/ice/llms.txt

Commands to pull the official ICE Docker image and clone the repository for test data access.

```bash
# Pull the latest ICE Docker image
docker pull synthego/ice

# Clone repository for test data
git clone git@github.com:synthego-open/ice.git ice
cd ice
```

--------------------------------

### Pull Synthego ICE Docker Image

Source: https://github.com/synthego-open/ice/blob/master/README.md

Download the latest ICE container image from Docker Hub.

```bash
docker pull synthego/ice
```

--------------------------------

### Run pytest commands

Source: https://github.com/synthego-open/ice/blob/master/README.md

Commands to execute test suites for the ICE project using the pytest framework.

```bash
$ pytest
```

```bash
$ pytest ice/tests/{{filename}}.py::{{test_function_name}}
```

```bash
$ pytest ice/tests/test_utility.py::test_sequence_util
```

```bash
$ py.test --cov-report term-missing --cov=ice ice/tests
```

--------------------------------

### Run Pytest Unit Tests

Source: https://github.com/synthego-open/ice/blob/master/DEVELOP.md

Execute unit tests for the project using pytest. Ensure you are in a virtual environment within the repository root folder.

```bash
python -m pytest
```

--------------------------------

### Perform fine-grained analysis with SangerAnalysis

Source: https://context7.com/synthego-open/ice/llms.txt

The SangerAnalysis class provides granular control over the ICE pipeline, including alignment and NNLS regression. Use initialize_with to configure parameters before calling analyze_sample.

```python
from ice.classes.sanger_analysis import SangerAnalysis

# Initialize the analysis object
sa = SangerAnalysis(verbose=True)
sa.debug = False
sa.allprops = False

# Configure the analysis with input files and parameters
sa.initialize_with(
    control_path='./ice/tests/test_data/good_example_control.ab1',
    edited_path='./ice/tests/test_data/good_example_edited.ab1',
    gRNA_sequences='AACCAGTTGCAGGCGCCCCA',  # Comma-separated for multiplex
    indel_max_size=20,                       # Maximum indel size to consider
    base_outputname='./results/analysis',
    donor=None,                              # Optional donor sequence for HDR
    allprops=False
)

# Run the full analysis pipeline
sa.analyze_sample()

# Access detailed results
print(f"Editing Efficiency: {sa.results.edit_eff * 100:.1f}%")
print(f"R-squared: {sa.results.r_squared:.2f}")
print(f"ICE-D Score: {sa.results.ice_d:.1f}")
print(f"KO Score: {sa.results.ko_score:.1f}")
print(f"HDR Percentage: {sa.results.hdr_percentage:.1f}%")

# Access aggregated indel distribution
for indel_size, abundance in sa.results.aggregate.items():
    if abundance > 0:
        print(f"  Indel {indel_size:+d}: {abundance * 100:.1f}%")

# Access individual sequence contributions
for contrib in sa.results.contribs[:5]:  # Top 5 contributors
    print(f"  {contrib.summary}: {contrib.x_rel * 100:.1f}%")

# Export results to JSON
json_results = sa.results.to_json(sa.guide_targets, sa.warnings)
```

--------------------------------

### CLI: Batch Analysis

Source: https://context7.com/synthego-open/ice/llms.txt

Processes multiple CRISPR editing experiments defined in an Excel file.

```APIDOC
## CLI: synthego_ice_batch

### Description
Processes multiple samples defined in an Excel file containing columns: Label, Control File, Experiment File, Guide Sequence, and optional Donor Sequence.

### Parameters
- **--in** (string) - Required - Path to input Excel file
- **--data** (string) - Required - Directory containing .ab1 files
- **--out** (string) - Required - Output directory
- **--line** (integer) - Optional - Run only a specific line from the Excel file
- **--verbose** (flag) - Optional - Enable verbose output
```

--------------------------------

### Batch process CRISPR experiments with multiple_sanger_analysis

Source: https://context7.com/synthego-open/ice/llms.txt

Use this function to process multiple samples defined in an Excel file. It requires a directory of .ab1 files and returns a list of dictionaries containing statistics for each sample.

```python
import os
from ice.analysis import multiple_sanger_analysis

# Excel file with columns: Label, Control File, Experiment File, Guide Sequence, Donor Sequence
definition_file = os.path.abspath('./ice/tests/test_data/batch_example.xlsx')

# Directory containing .ab1 files referenced in the Excel
data_directory = os.path.abspath('./ice/tests/test_data/')

# Output directory for results
output_dir = './results/batch_example'

# Run batch analysis
results = multiple_sanger_analysis(
    definition_file=definition_file,
    output_dir=output_dir,
    data_dir=data_directory,
    verbose=True,
    single_line=None,  # Set to integer to run only that row
    allprops=False
)

# Results is a list of dictionaries, one per sample:
# [
#     {
#         'sample_name': 'Sample1',
#         'ice': 80,
#         'ice_d': 75,
#         'r_squared': 0.95,
#         'hdr_pct': 0.0,
#         'ko_score': 65.5,
#         'guides': 'AACCAGTTGCAGGCGCCCCA',
#         'notes': '',
#         'experiment_file': 'sample1_edited.ab1',
#         'control_file': 'control.ab1',
#         'donor': None
#     },
#     ...
# ]
for sample_result in results:
    print(f"{sample_result['sample_name']}: ICE={sample_result['ice']}%")
```

--------------------------------

### Python API: single_sanger_analysis

Source: https://context7.com/synthego-open/ice/llms.txt

Core Python function for analyzing a single CRISPR editing experiment.

```APIDOC
## Python: single_sanger_analysis

### Description
Analyzes control and edited .ab1 files to infer the abundance of each edit type using NNLS regression.

### Parameters
- **control_path** (string) - Required - Absolute path to control .ab1 file
- **sample_path** (string) - Required - Absolute path to edited .ab1 file
- **base_outputname** (string) - Required - Output file prefix
- **guide** (string) - Required - Guide sequence (17-23 bases)
- **donor** (string) - Optional - Donor sequence for HDR analysis
- **verbose** (boolean) - Optional - Enable verbose output
- **allprops** (boolean) - Optional - Output all edit proposals

### Response
- **ice** (float) - Overall editing efficiency (0-100)
- **ice_d** (float) - Discordance-based editing estimate
- **rsq** (float) - R-squared goodness of fit
- **hdr_pct** (float) - HDR percentage
- **ko_score** (float) - Knockout score
- **status** (string) - Execution status
```

--------------------------------

### Analyze Single Sample via Python API

Source: https://context7.com/synthego-open/ice/llms.txt

Use the single_sanger_analysis function to programmatically analyze CRISPR experiments.

```python
import os
from pprint import pprint
from ice.analysis import single_sanger_analysis

# Define paths to Sanger sequencing files
control_path = os.path.abspath('./ice/tests/test_data/good_example_control.ab1')
sample_path = os.path.abspath('./ice/tests/test_data/good_example_edited.ab1')

# Guide sequence (17-23 bases, RNA or DNA)
guide = 'AACCAGTTGCAGGCGCCCCA'

# Output base path (files will be named with this prefix)
base_outputname = './results/good_example'

# Run the analysis
results = single_sanger_analysis(
    control_path=control_path,
    sample_path=sample_path,
    base_outputname=base_outputname,
    guide=guide,
    donor=None,         # Optional: donor sequence for HDR analysis
    verbose=True,
    allprops=False      # Set True to output all edit proposals including zero contribution
)

# Results dictionary structure:
# {
#     'ice': 80,                    # Overall editing efficiency (0-100)
#     'ice_d': 75,                  # Discordance-based editing estimate
#     'rsq': 0.95,                  # R-squared goodness of fit
#     'hdr_pct': 0.0,               # HDR percentage (if donor provided)
#     'ko_score': 65.5,             # Knockout score (frameshift mutations)
#     'mean_discord_before': 0.05,  # Discordance in alignment window
#     'mean_discord_after': 0.35,   # Discordance after cut site
#     'guides': [...],              # Guide target information
#     'notes': '',                  # Warnings or error messages
#     'status': 'succeeded'
# }
pprint(results)
```

--------------------------------

### CLI: Single Sample Analysis

Source: https://context7.com/synthego-open/ice/llms.txt

Analyzes a single control and edited Sanger sequencing pair to infer CRISPR editing efficiency.

```APIDOC
## CLI: synthego_ice

### Description
Analyzes a single control/edited sample pair against a target guide sequence.

### Parameters
- **--control** (string) - Required - Path to control .ab1 file
- **--edited** (string) - Required - Path to edited sample .ab1 file
- **--target** (string) - Required - Guide RNA sequence
- **--out** (string) - Required - Output directory/prefix
- **--donor** (string) - Optional - Donor sequence for HDR analysis
- **--verbose** (flag) - Optional - Enable verbose output
```

--------------------------------

### Process Sanger sequencing files with SangerObject

Source: https://context7.com/synthego-open/ice/llms.txt

Use SangerObject to read .ab1 files, extract base calls, quality scores, and chromatogram data. It provides methods to identify high-quality alignment windows.

```python
from ice.classes.sanger_object import SangerObject

# Initialize and load an ab1 file
sanger = SangerObject(verbose=True)
sanger.initialize_from_path('./ice/tests/test_data/good_example_control.ab1')

# Access primary sequence data
sequence = sanger.primary_base_calls
print(f"Sequence length: {len(sequence)} bp")
print(f"First 50 bases: {sequence[:50]}")

# Access quality scores (Phred scores)
quality_scores = sanger.phred_scores
avg_quality = sum(quality_scores) / len(quality_scores)
print(f"Average quality score: {avg_quality:.1f}")

# Find high-quality alignment window
alignment_window = sanger.find_alignable_window(
    window_size=30,     # Size of sliding window for quality averaging
    QUAL_CUTOFF=40      # Minimum average quality threshold
)
print(f"Best alignment window: {alignment_window['max_window']}")
print(f"All high-quality regions: {alignment_window['regions']}")

# Access raw chromatogram peak values
peak_values = sanger.get_peak_values()
# Returns dict: {'A': [...], 'C': [...], 'G': [...], 'T': [...]}

# Get peak intensity at each base position
for i in range(5):  # First 5 positions
    print(f"Position {i}: A={peak_values['A'][i]}, C={peak_values['C'][i]}, "
          f"G={peak_values['G'][i]}, T={peak_values['T'][i]}")

# Access peak locations (x-coordinates in chromatogram)
peak_locs = sanger.peak_locations
print(f"Peak locations: {peak_locs[:10]}...")
```

--------------------------------

### multiple_sanger_analysis Function

Source: https://context7.com/synthego-open/ice/llms.txt

Performs batch analysis of multiple CRISPR editing experiments from a definition file. Outputs a consolidated results file with all sample statistics.

```APIDOC
## multiple_sanger_analysis Function

### Description
Batch processing function for analyzing multiple CRISPR editing experiments from an Excel definition file. Outputs a consolidated results file with all sample statistics.

### Parameters
- **definition_file** (str) - Required - Path to the Excel file with experiment definitions.
- **output_dir** (str) - Required - Directory to save the results.
- **data_dir** (str) - Required - Directory containing the .ab1 files.
- **verbose** (bool) - Optional - If True, prints detailed progress information.
- **single_line** (int) - Optional - If set to an integer, runs analysis only for the specified row in the definition file.
- **allprops** (bool) - Optional - If True, includes all properties in the results.

### Returns
- **list** - A list of dictionaries, where each dictionary contains the analysis results for a single sample.
```

--------------------------------

### SangerAnalysis Class

Source: https://context7.com/synthego-open/ice/llms.txt

The core analysis class that performs the complete ICE workflow, including alignment, edit proposal generation, and NNLS regression. Use this class directly for fine-grained control over the analysis process.

```APIDOC
## SangerAnalysis Class

### Description
The core analysis class that performs the complete ICE workflow including alignment, edit proposal generation, and NNLS regression. Use this class directly for fine-grained control over the analysis process.

### Initialization
- **verbose** (bool) - Optional - If True, prints detailed progress information.
- **debug** (bool) - Optional - If True, enables debug mode.
- **allprops** (bool) - Optional - If True, includes all properties in the results.

### Methods
#### initialize_with
Configures the analysis with input files and parameters.
- **control_path** (str) - Required - Path to the control .ab1 file.
- **edited_path** (str) - Required - Path to the edited .ab1 file.
- **gRNA_sequences** (str) - Required - Comma-separated guide RNA sequences.
- **indel_max_size** (int) - Optional - Maximum indel size to consider.
- **base_outputname** (str) - Required - Base name for output files.
- **donor** (str) - Optional - Donor sequence for HDR analysis.
- **allprops** (bool) - Optional - If True, includes all properties in the results.

#### analyze_sample
Runs the full analysis pipeline.

### Results Access
- **results.edit_eff** (float) - Editing efficiency.
- **results.r_squared** (float) - R-squared value.
- **results.ice_d** (float) - ICE-D score.
- **results.ko_score** (float) - KO score.
- **results.hdr_percentage** (float) - HDR percentage.
- **results.aggregate** (dict) - Aggregated indel distribution.
- **results.contribs** (list) - Individual sequence contributions.

### Export
#### to_json
Exports results to JSON format.
- **guide_targets** - List of guide targets.
- **warnings** - List of warnings.
```

--------------------------------

### SangerObject Class

Source: https://context7.com/synthego-open/ice/llms.txt

Class for reading and processing Sanger sequencing .ab1 files. Extracts base calls, quality scores, and chromatogram peak data for downstream analysis.

```APIDOC
## SangerObject Class

### Description
Class for reading and processing Sanger sequencing .ab1 files. Extracts base calls, quality scores, and chromatogram peak data for downstream analysis.

### Initialization
- **verbose** (bool) - Optional - If True, prints detailed progress information.

### Methods
#### initialize_from_path
Loads an .ab1 file.
- **path** (str) - Required - Path to the .ab1 file.

### Properties
- **primary_base_calls** (str) - The primary base calls from the sequence.
- **phred_scores** (list) - Quality scores (Phred scores) for each base.
- **peak_locations** (list) - X-coordinates of the chromatogram peaks.

### Methods
#### find_alignable_window
Finds a high-quality alignment window.
- **window_size** (int) - Optional - Size of the sliding window for quality averaging.
- **QUAL_CUTOFF** (int) - Optional - Minimum average quality threshold.
- **Returns**: A dictionary containing the best alignment window and all high-quality regions.

#### get_peak_values
Retrieves raw chromatogram peak values.
- **Returns**: A dictionary with lists of peak intensities for each base (A, C, G, T).
```
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