Topsicle: a method for estimating telomere length from whole genome long-read sequencing data

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Telomeres are crucial protective caps at chromosome ends.
• Telomere length variation impacts various biomedical and eco-evolutionary phenotypes.
• Understanding telomere length significance is vital across multiple biological disciplines.

Purpose of the Study:

• To introduce Topsicle, a novel computational method for estimating telomere length.
• To enable telomere length analysis from whole genome long-read sequencing data.

Main Methods:

• Topsicle utilizes k-mer analysis and change point detection.
• The method is applied to whole genome long-read sequencing data.
• Simulations were performed to assess robustness.

Main Results:

• Topsicle demonstrated robustness against sequencing errors and coverage variations.
• Accurate telomere length estimation was achieved in plant and human cancer cells.
• Results were comparable to direct telomere length measurement techniques.

Conclusions:

• Topsicle is a reliable and accurate computational tool for telomere length estimation.
• The method is suitable for diverse applications in telomere biology research.
• Topsicle is expected to advance the study of telomere length variation and its implications.