Computel

Computel computes mean telomere length from whole-genome next-generation sequencing (NGS) data to enable analysis of telomere dynamics in studies of cellular aging, senescence, and disease.

Key Features:

• R implementation: The software is implemented in R for integration with statistical and bioinformatics workflows.
• Short-read alignment-based approach: Employs short-read alignment to map sequencing reads to telomeric regions for telomere length computation.
• Integration of established tools: Integrates various established sequencing data analysis tools to support processing steps.
• Validation on synthetic and experimental data: Validated using both synthetic and experimental datasets to assess reliability.
• High accuracy and robustness: Demonstrates superior accuracy, independence from sequencing conditions, and stability against sequencing errors.
• Distinguishing telomeric and interstitial repeats: Differentiates pure telomeric sequences from interstitial telomeric repeats.

Scientific Applications:

• Aging research: Quantifying mean telomere length from whole-genome next-generation sequencing (NGS) data to study mechanisms of replicative senescence and cellular aging.
• Cancer studies: Investigating telomere length deregulation and its role in cancer development using whole-genome next-generation sequencing (NGS) data.
• Genomic medicine: Evaluating telomere length as a potential biomarker in age-related diseases and therapeutic research using whole-genome next-generation sequencing (NGS) data.

Methodology:

Implemented in R and employing a short-read alignment-based method to map reads to telomeric regions, integrating established sequencing-analysis tools and validated using synthetic and experimental datasets.