uTR

uTR decomposes mosaic tandem repeats (TRs) from long-read sequencing data to enable accurate characterization of complex repeat architectures associated with diseases, including brain disorders.

Key Features:

• Algorithm: Uses an efficient algorithm specifically tailored to decompose mosaic tandem repeats with high sensitivity.
• Input data: Operates on long-read sequencing data to detect extended and complex TR structures.
• Mosaic TR resolution: Resolves mosaic TRs that comprise multiple distinct repeat units rather than single homogeneous repeats.
• Comparative accuracy: Demonstrates superior prediction accuracy compared with TRF (Tandem Repeats Finder) and RepeatMasker when handling complex mosaic configurations.
• Validation: Performance validated using synthetic benchmark data.
• Performance: Reports improved speed and accuracy relative to TRF and RepeatMasker on benchmark tests.

Scientific Applications:

• Tandem repeat annotation: Precise decomposition of mosaic TRs for improved repeat annotation and characterization.
• Disease association studies: Investigation of genetic underpinnings of diseases linked to extended and mosaic TRs.
• Brain disorder research: Analysis of complex repeat structures associated with various brain disorders.
• Method benchmarking: Benchmarking and comparison of TR decomposition methods using synthetic data.

Methodology:

Performs computational decomposition of mosaic tandem repeats from long-read sequencing reads using an efficient algorithm and validates results by comparison to TRF and RepeatMasker on synthetic benchmark data.