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Directional allelic imbalance profiling and visualization from multi-sample data with RECUR.

Yasminka A Jakubek1, F Anthony San Lucas1,2, Paul Scheet1

  • 1Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

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This summary is machine-generated.

RECUR analyzes allelic imbalance (AI) in cancer genetics using multi-sample data. This R application detects mirrored AI, offering insights into cancer

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Area of Science:

  • Genomics and Cancer Research
  • Computational Biology

Background:

  • Somatic copy number alterations and allelic imbalance (AI) are crucial in cancer development.
  • Analyzing AI across multiple patient samples aids in understanding mutation rates, clonality, and cancer etiology.

Purpose of the Study:

  • To introduce the REpeat Chromosomal changes Uncovered by Reflection (RECUR) R application.
  • To provide a tool for comparative analysis of AI profiles from SNP array and next-generation sequencing data.
  • To detect and visualize mirrored subclonal allelic imbalance for cancer heterogeneity insights.

Main Methods:

  • RECUR accepts genotype calls and B allele frequencies (BAFs) from at least two samples per individual.
  • The algorithm compares BAF values in predefined genomic regions with AI.
  • It detects segments with opposite haplotype shifts (mirrored AI) and visualizes BAFs using a two-color scheme.

Main Results:

  • RECUR effectively identifies genomic regions exhibiting mirrored subclonal allelic imbalance.
  • The application provides intuitive visualization of BAF values across multiple samples.
  • This facilitates the inference of recurrent mutations and understanding of cancer genomic architecture.

Conclusions:

  • RECUR is a valuable R application for analyzing allelic imbalance in multi-sample cancer genetic studies.
  • The tool aids in uncovering complex genomic alterations and heterogeneity linked to cancer.
  • It supports research into mutation rates, clonality, and the underlying causes of cancer.