SVLR

SVLR detects and characterizes structural variants from Pacific Biosciences and Oxford Nanopore long-read sequencing alignments (NGMLR .sam or LAST .maf) to identify diverse and complex SV types—including deletions, insertions, inversions, tandem and interspersed duplications, cut-and-paste insertions, block replacements, block interchanges, and translocations—across sizes >10 bp up to ~10,000 nucleotides while improving recall without compromising precision.

Key Features:

• Detection of diverse SV types: Identifies deletions, insertions, inversions, tandem duplications, interspersed duplications, cut & paste insertions, block replacements, block interchanges, and translocations for events >10 bp and up to ~10,000 nucleotides.
• Detection of structurally complex SVs: Detects complex event classes specifically including block replacements, block interchanges, and translocations.
• Signature-based workflow: Marks SV signatures from alignment data, clusters similar signatures, combines clusters into candidate SVs, and optimizes clusters to refine calls.
• Improved recall with maintained precision: Reports recall improvements up to 38% relative to classic SV detection methods while maintaining precision above 78% and comparable overall accuracy.
• Alignment format compatibility: Consumes NGMLR (.sam) and LAST (.maf) alignment files as input.
• Long-read sequencing support: Operates on Pacific Biosciences (PacBio) and Oxford Nanopore long-read sequencing data to leverage reads spanning large SVs.

Scientific Applications:

• Structural variation discovery and characterization: Enables comprehensive detection and characterization of a wide range of SVs, including complex classes, in genomic research using long-read data.
• Disease and phenotype association studies: Supports investigation of SVs linked to human phenotypic variation and diseases by improving detection sensitivity for both classic and complex SV types.

Methodology:

Identify SV signatures from alignment data, cluster similar signatures, combine clusters into comprehensive SV profiles, and optimize clusters to refine detected SVs.