SHEAR

SHEAR predicts structural variants and estimates their subpopulation percentages from heterogeneous next-generation sequencing samples to assemble personal genomic sequences that improve RNA-seq and ChIP-seq analyses of tumor genomes.

Key Features:

• Structural variant prediction and quantification: Predicts structural variants (SVs) and estimates representative or subpopulation percentages for each variant within a sample.
• Advanced SV detection algorithms: Employs advanced structural variant detection algorithms to improve detection accuracy.
• Complex SV handling: Explicitly addresses difficult structural variant types such as tandem duplications.
• Personalized reference generation: Generates personal genomic sequences that incorporate the subpopulation percentage of each variant.
• Computational efficiency and robustness: Improves computational efficiency and robustness in assembling sequences from heterogeneous samples.
• Empirical validation: Validated against competing approaches using simulated scenarios and real tumor cell line data with known heterogeneous variants.

Scientific Applications:

• Tumor genome analysis: Assembly and heterogeneity estimation in tumor genomes containing multiple subpopulations of variants.
• Personalized reference construction: Creation of personal genomic sequences to better represent individual genetic variation in downstream analyses.
• RNA-seq and ChIP-seq accuracy improvement: Enhances accuracy of RNA-seq and ChIP-seq analyses by providing personalized genomic references.
• Structural variant detection in heterogeneous samples: Enables more precise SV detection in samples with mixed subpopulations.

Methodology:

Uses advanced structural variant detection algorithms to predict SVs and estimate their representative/subpopulation percentages, generates personal genomic sequences incorporating these percentages, and evaluates performance using simulated scenarios and real tumor cell line data with known heterogeneous variants.