Telomere content estimation software tools | Whole-genome sequencing data analysis
Telomere shortening plays an important role in cellular aging and tumor suppression. The availability of large next-generation sequencing cohorts of matched tumor and control samples enables a computational high-throughput analysis of changes in telomere content and composition in cancer.
A method to measure average telomere length from whole genome or exome shotgun sequence data. TelSeq allows any cohort with existing genome-wide sequence data, including increasingly many cancer genomics and epidemiological cohort studies, to produce a validated measure of telomere length at effectively no cost, with no need for the further sample collection and experimental procedures required by other methods of ascertaining telomere length.
Estimates telomere length from cancer whole genome sequencing (WGS) data. Telomerecat calculates and corrects the number of Interstitial telomeric repeat (ITR)-originating reads without consideration for how these highly repetitive reads are aligned to a reference genome. It is designed to be applicable to cancer experiments as it does not assume a given number of telomeres. It also correlates with existing computational and experimental methods as well as with sample donor age.
An alternative, alignment-based approach for telomere length estimation using whole-genome NGS data. We have performed its extensive validation using both synthetic and experimental data. Comparison of Computel with TelSeq showed that our approach outperforms the latter and allows for more flexibility and convenience of telomere length estimation in different genomes. The general workflow of mean telomere length estimation by Computel consists of the following steps: (1) building a telomeric index, (2) mapping reads to the telomeric index, (3) coverage calculation at the telomeric index, (4) determination of mean coverage at reference genome (optional), (5) estimation of mean telomere length.
A tool for estimating telomere content from human whole-genome sequencing data. TelomereHunter is designed to take BAM files from a tumor and a matching control sample as input. However, it is also possible to run TelomereHunter with one input file. TelomereHunter extracts and sorts telomeric reads from the input sample(s). For the estimation of telomere content, GC biases are taken into account. Finally, the results of TelomereHunter are visualized in several diagrams. In contrast to existing tools, TelomereHunter takes alignment information into account and reports the abundance of variant repeats in telomeric sequences.
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