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ppBAM: ProteinPaint BAM track for read alignment visualization and variant genotyping.

Robin Paul1, Jian Wang1, Colleen Reilly1

  • 1Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, United States.

Bioinformatics (Oxford, England)
|May 4, 2023
PubMed
Summary

ProteinPaint BAM track (ppBAM) enhances cancer variant review by enabling rapid genotyping of thousands of sequencing reads. It visualizes complex variants and integrates with the Genomic Data Commons for comprehensive cancer genomics data analysis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Variant review is crucial for cancer research and clinical genomics.
  • Existing tools may struggle with visualizing complex variants and handling large datasets.
  • Efficient analysis of cancer sequencing data is essential for reinterpreting variant calls.

Purpose of the Study:

  • To introduce ProteinPaint BAM track (ppBAM) as a tool for improved variant review in cancer genomics.
  • To facilitate on-the-fly variant genotyping and visualization of complex variants.
  • To enable seamless examination of large-scale cancer sequencing data through integration with the NCI Genomic Data Commons (GDC).

Main Methods:

  • Utilizes performant server-side computing and rendering for efficient data processing.
  • Implements Smith-Waterman alignment for on-the-fly variant genotyping of thousands of reads.
  • Employs ClustalO for read realignment against mutated reference sequences to visualize complex variants.
  • Supports the BAM slicing API of the NCI Genomic Data Commons (GDC) for data access.

Main Results:

  • ppBAM provides efficient, on-the-fly variant genotyping.
  • Complex variants are visualized effectively through read realignment.
  • Seamless integration with GDC allows convenient examination of vast cancer sequencing data.

Conclusions:

  • ppBAM is a valuable tool for enhancing variant review in cancer research and clinical genomics.
  • The tool facilitates deeper analysis and reinterpretation of variant calls in large datasets.
  • ppBAM improves the visualization and understanding of complex genomic alterations.