superSTR

superSTR detects repeat expansions in next-generation DNA and RNA sequencing data using an alignment-free approach to identify regions of repetitive motif enrichment associated with genetic disorders.

Key Features:

• Alignment-Free Detection: Operates without sequence alignment to detect repeat expansions directly from sequencing reads.
• Ultrafast Processing: Provides high computational performance for rapid screening of large datasets.
• Computationally Efficient: Optimized for reduced computational overhead when analyzing extensive sequencing data.
• Versatile Data Processing: Supports whole-genome sequencing (WGS) and whole-exome sequencing (WES) data.
• RNA Sequencing Compatibility: Applies to RNA sequencing data, including analyses of human patients and mouse models.
• Motif Enrichment Identification: Identifies repetitive motif enrichment at genomic locations from sequencing reads.

Scientific Applications:

• Disease Association Studies: Identifies repeat expansions associated with Huntington’s disease, spinocerebellar ataxia, Fuchs’ endothelial corneal dystrophy, and myotonic dystrophy.
• Novel Mutation Discovery: Screens large cohorts such as the UK Biobank to detect known mutations and novel associations involving repeat expansions.
• Transcriptomic Analysis: Detects repeat-expansion–related transcriptomic variations in RNA-seq data from human patients and mouse models.

Methodology:

Bypasses sequence alignment and identifies regions of repetitive motif enrichment directly from next-generation sequencing reads.