TRsv: simultaneous detection of tandem repeat variations, structural variations, and short indels using long read...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Tandem repeat copy number variations (TR-CNVs), structural variations (SVs), and short indels are significant genetic variants implicated in numerous diseases and traits.
• Existing computational tools lack the capability to effectively distinguish and detect these diverse variant types simultaneously.

Purpose of the Study:

• To develop and validate a novel computational tool, TRsv, for the accurate distinction and detection of TR-CNVs, SVs, and short indels.
• To assess the performance of TRsv against existing methods using both simulated and real-world genomic datasets.

Main Methods:

• Development of the TRsv computational tool, specifically designed for analyzing long-read sequencing data.
• Comparative analysis of TRsv's performance against established variant detection tools using simulated and real whole genome sequencing datasets.

Main Results:

• TRsv demonstrated superior performance in detecting TR-CNVs and short indels compared to existing tools.
• TRsv achieved comparable performance to existing tools in the detection of structural variations (SVs).
• Genome-wide TR-CNV detection was successfully demonstrated, identifying variants associated with gene expression, diseases, and quantitative traits.

Conclusions:

• TRsv is a highly effective tool for distinguishing and detecting multiple types of genetic variants, including TR-CNVs, SVs, and indels, from long-read sequencing data.
• The tool's capabilities facilitate a deeper understanding of the genetic underpinnings of various biological phenomena, including disease and trait variations.