Steps and Scripts for Running SigMA HRD Pipeline in Cancer Genomes

Answered using ChatGPT 4o by ChatGPT4o

Question:

can you describe steps and scripts for SigMA - hrd pipeline

Answer:

SigMA (Signature Mutational Analysis) is a computational tool used to identify mutational signatures in cancer genomes. The HRD (Homologous Recombination Deficiency) pipeline within SigMA is specifically designed to detect mutational signatures associated with defects in the homologous recombination repair pathway, which is often implicated in cancers such as breast and ovarian cancer.

Below are the general steps and example scripts to run the SigMA HRD pipeline:

Step 1: Install SigMA

First, you need to install the SigMA package. You can do this using pip if you are using Python.

pip install sigMA

Step 2: Prepare Input Data

SigMA requires a VCF (Variant Call Format) file containing somatic mutations. Ensure your VCF file is properly formatted and contains the necessary information.

Step 3: Run SigMA HRD Pipeline

You can run the SigMA HRD pipeline using the following Python script. This script assumes you have a VCF file named input.vcf.

import sigMA

# Define the input VCF file and output directory
input_vcf = 'input.vcf'
output_dir = 'output/'

# Run the SigMA HRD pipeline
sigMA.run(
    input_vcf=input_vcf,
    output_dir=output_dir,
    analysis_type='hrd'
)

Step 4: Interpret Results

After running the pipeline, the results will be saved in the specified output directory. The output will typically include:

  • A summary of the detected mutational signatures.
  • Scores indicating the presence of HRD-related signatures.
  • Visualizations of the mutational spectra.

Example Script

Here is a more detailed example script that includes additional parameters for customization:

import sigMA

# Define the input VCF file and output directory
input_vcf = 'input.vcf'
output_dir = 'output/'

# Additional parameters
reference_genome = 'GRCh37'  # or 'GRCh38'
min_mutations = 50  # Minimum number of mutations required for analysis

# Run the SigMA HRD pipeline with additional parameters
sigMA.run(
    input_vcf=input_vcf,
    output_dir=output_dir,
    analysis_type='hrd',
    reference_genome=reference_genome,
    min_mutations=min_mutations
)

Step 5: Post-Processing and Visualization

You may want to further process and visualize the results. SigMA provides various functions for this purpose. For example, you can generate plots to visualize the mutational signatures:

import sigMA
import matplotlib.pyplot as plt

# Load the results
results = sigMA.load_results(output_dir)

# Plot the mutational signatures
sigMA.plot_signatures(results)
plt.show()

Step 6: Interpretation and Reporting

Finally, interpret the results in the context of your research or clinical question. The presence of HRD-related signatures can provide insights into the underlying genetic defects and potential therapeutic strategies.

Notes

  • Ensure you have all dependencies installed, including any specific versions of Python packages required by SigMA.
  • The exact parameters and options may vary depending on the version of SigMA you are using. Always refer to the official documentation for the most up-to-date information.

By following these steps and using the provided scripts, you should be able to run the SigMA HRD pipeline and analyze the mutational signatures in your cancer genome data.