laSV

laSV detects genomic structural variations from paired-end high-throughput sequencing data using local de novo assembly to localize SV breakpoints at single-base resolution for studies of cancer and other diseases.

Key Features:

• Local assembly-based algorithm: Implements a local de novo assembly approach to assemble reads around candidate breakpoints for structural variation detection.
• Paired-end high-throughput sequencing support: Leverages paired-end sequencing data as input for SV discovery and breakpoint resolution.
• Single base-pair breakpoint resolution: Identifies SV breakpoints at single base-pair precision to enable detailed genomic characterization.
• Application to TCGA tumor-normal cohorts: Applied to 97 tumor-normal paired genomic sequencing datasets across six cancer types from The Cancer Genome Atlas Research Network.
• Mechanism identification: Enables inference of SV-generating mechanisms, including identification of non-allelic homologous recombination as a primary mechanism in acute myeloid leukemia versus predominant non-homologous end joining and microhomology end joining in solid tumors.
• Differential mutation analysis: Facilitates distinguishing genes mutated by single nucleotide alterations from those affected by structural variations.
• Functional impact characterization: Supports analysis of how somatic SVs affect gene structures of oncogenes and tumor suppressors, exemplified by JAK1, KDM6A, and RB1.

Scientific Applications:

• Cancer genomics: Analysis of tumor-normal paired sequencing to map structural variation landscapes across cancer types.
• Mechanistic studies of SV formation: Comparative identification of SV-generation mechanisms such as non-allelic homologous recombination, non-homologous end joining, and microhomology end joining.
• Functional genomics of cancer genes: Characterization of structural variation effects on oncogenes (e.g., JAK1) and tumor suppressors (e.g., KDM6A, RB1).
• Mutation-type differentiation: Distinguishing genomic regions and genes altered by structural variations versus single nucleotide alterations.

Methodology:

Local de novo assembly of paired-end high-throughput sequencing reads using a local assembly-based algorithm to detect structural variation breakpoints at single base-pair resolution.