Benchmarking Long-read Sequencing Tools for Chromosome End-specific Telomere Analysis

Abstract

Measuring chromosome end-specific telomeres is of great importance and could help elucidate better treatment algorithms and aid in a better understanding of cancer, aging, cardiovascular disease, and neurodegenerative diseases. In this study, we present a comparison of two cutting-edge long-read sequencing telomere length analysis tools, TECAT and Telogator. We perform a comprehensive bench-marking of these two tools using Telseq as the standard. Our analysis included evaluating these tools on sensitivity, accuracy, and computational efficiency using a diverse data set of 9 samples from the 1000 Genomes Project which have matched long-read and short-read sequencing data. We found that while Teloga-tor demonstrated superior sensitivity, identifying on average 31% more telomeric reads across all samples, TECAT showed better accuracy with measurements more closely aligned with established literature values and Telseq benchmarks (R-squared = 0.74 vs 0.37), and TECAT displayed better computational efficiency, completing tasks approximately 41% faster. Both tools successfully mapped telomere lengths to individual chromosome arms, demonstrating unprecedented resolution for telomere length analysis. Our results provide crucial insights for researchers selecting tools for telomere length analysis and highlight the current capabilities and limitations of computational approaches in telomere biology.