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

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Telomeres are protective caps on chromosome ends, crucial for genomic stability.
• Significant variation in telomere length exists across species, influencing various phenotypes.
• Understanding telomere length dynamics is vital in biomedical and evolutionary studies.

Purpose of the Study:

• To introduce Topsicle, a novel computational method for estimating telomere length.
• To leverage whole genome long-read sequencing data for telomere length analysis.
• To provide a robust tool for advancing telomere biology research.

Main Methods:

• Topsicle utilizes k-mer analysis and change-point detection algorithms.
• The method is applied to whole genome long-read sequencing data.
• Performance was validated through simulations and experimental applications.

Main Results:

• Simulations demonstrate Topsicle's robustness against sequencing errors and varying coverage.
• Accurate telomere length estimations were achieved in plant and human cancer cell lines.
• Results showed high accuracy and comparability with direct telomere measurement techniques.

Conclusions:

• Topsicle is a reliable and accurate computational tool for telomere length estimation.
• The method facilitates the study of telomere biology across diverse organisms.
• Topsicle is expected to be a valuable asset for researchers in genomics and related fields.