Abstract

Telomeres are structures protecting chromosome ends. However, a scalable and cost-effective method to investigate chromosome arm-level (ChArm) telomeres (Telos) in large-scale projects is still lacking, hindering intensive investigation of high-resolution telomeres across cancers and other diseases. Here, ChArmTelo, the first computational toolbox to analyze telomeres at chromosome arm level in human and other animal species, using 10X linked-read and similar technologies, is presented. ChArmTelo currently consists of two algorithms, TeloEM and TeloKnow, for arm-level telomere length (TL) analysis. The algorithms are demonstrated by comprehensive analysis of chromosome arm-level telomere lengths (chArmTLs) in nearly 400whole genome sequencing samples (WGS) from human populations and animals, including healthy and cancer samples. Notably, considerable performance improvement contributed by using the latest complete telomere-to-telomere reference genome (CHM13v2), compared to hg38, is shown. ChArmTelo reveals population-specific chArmTL differences and liver cancer signatures of chArmTLs and that DNA replication origin disruption may contribute to cancer by affecting TLs. Importantly, ChArmTelo can be readily applied to tens of thousands of cancer and healthy samples with published WGS data.

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