<p>Telomeres protect chromosome ends and their length varies significantly between organisms. Because telomere length variation is associated with various biomedical and eco-evolutionary phenotypes, many biological fields are interested in understanding its biological significance. Here, we introduce Topsicle, a computational method that estimates telomere length from whole genome long-read sequencing data using <i>k</i>-mer and change-point detection analysis. Simulations show Topsicle is robust to sequencing errors and coverage. Application of Topsicle to plant and human cancer cells shows high accuracy and comparable results to direct telomere length measurements. We predict Topsicle will be a useful tool for studying telomere biology.</p>