Meerkat

Meerkat detects and characterizes structural variations in human cancer genomes from paired-end high-throughput short-read sequencing data to elucidate complex somatic rearrangements.

Key Features:

• Algorithmic Approach: Employs a robust algorithm to predict structural variations from short-read paired-end sequencing data.
• Comprehensive Cataloging: Generates extensive catalogs of somatic structural variations across tumor types from high-coverage whole-genome sequencing (WGS) data.
• Mechanistic Insights: Distinguishes simple and complex deletions and reports that approximately 20% of somatic deletions are complex and often result from replication errors.
• Comparative Analysis: Enables comparative analysis between somatic and germline alterations to probe mutational mechanisms in cancer genomes.
• Event Reconstruction: Reconstructs detailed rearrangement events responsible for focal changes such as loss of CDKN2A/B and gain of EGFR in glioblastoma, revealing multiple mechanisms can drive these alterations within a single genome.

Scientific Applications:

• Cancer Genomics: Profiles somatic structural variation landscapes to inform studies of tumorigenesis across cancer types.
• Mechanistic Studies: Investigates mutational mechanisms of somatic rearrangements, including replication-error–associated complex deletions.
• Targeted Therapies: Identifies recurrent and focal structural alterations that can inform development of targeted therapeutic hypotheses.

Methodology:

Analyzes paired-end high-throughput short-read sequencing data using computational algorithms to predict and characterize structural variations and to reconstruct detailed rearrangement events.