Characterizing tandem repeat complexities across long-read sequencing platforms with TREAT and otter

Area of Science:

• Genomics
• Bioinformatics
• Human Genetics

Background:

• Tandem repeats (TRs) are crucial for human genomic variation and disease susceptibility.
• Accurate TR characterization is challenging, especially with long-read sequencing data.
• Existing bioinformatics tools struggle with comprehensive TR analysis.

Purpose of the Study:

• To introduce otter, a fast, cross-platform local assembler for TRs.
• To present TREAT, an end-to-end workflow for TR characterization, visualization, and analysis.
• To enable accurate TR genotyping and identification of pathogenic expansions across diverse sequencing platforms.

Main Methods:

• Developed otter, a targeted local assembler compatible with Oxford Nanopore Technology (ONT) and Pacific Biosciences (PacBio) data.
• Integrated otter into TREAT, a comprehensive workflow for TR analysis.
• Compared otter/TREAT performance against existing tools using long-read sequencing data.

Main Results:

• Achieved state-of-the-art accuracy in TR genotyping and motif characterization.
• Successfully identified individuals with pathogenic TR expansions in clinically relevant loci.
• Replicated known associations between TRs and Alzheimer's disease (e.g., near APOC1, SPI1, ABCA7).
• Evaluated potential biases in long-read TR genotyping, noting rare coverage drops impacting accuracy.

Conclusions:

• TREAT/otter provides accurate, end-to-end TR analysis across different sequencing technologies.
• The tools facilitate TR comparisons and applications in both research and clinical settings.
• Identified and addressed potential sources of TR misgenotyping in long-read data.