Detection of repeat expansions in large next generation DNA and RNA sequencing data without alignment

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Short tandem repeat (STR) expansions are linked to various human diseases.
• Current bioinformatic methods for detecting STR expansions in short-read sequencing data require alignment, limiting efficiency.
• Identifying novel STR expansions is crucial for understanding genetic disorders.

Purpose of the Study:

• To develop and present superSTR, an ultrafast, alignment-free method for detecting short tandem repeat expansions.
• To apply superSTR to large-scale sequencing datasets, including whole-genome and whole-exome data.
• To perform the first comprehensive STR analysis of the UK Biobank and screen RNA sequencing data for repeat expansions.

Main Methods:

• Development of superSTR, a novel bioinformatics tool for rapid STR expansion detection.
• Processing of whole-genome and whole-exome sequencing data from the UK Biobank (49,953 participants).
• Application of superSTR to RNA sequencing data from human and mouse models of ataxia and dystrophy.

Main Results:

• superSTR demonstrates ultrafast performance without requiring sequence alignment.
• Efficient screening and identification of known and potential disease-associated STRs in UK Biobank exome data.
• Successful demonstration of bioinformatic screening of RNA sequencing data for repeat expansions in disease models.

Conclusions:

• superSTR provides an efficient and rapid approach for detecting short tandem repeat expansions across diverse sequencing data types.
• The method enables large-scale population screening and the analysis of RNA sequencing data for repeat expansion detection.
• superSTR has the potential to advance the discovery of genetic variants associated with human diseases.