NCI-Funded Researchers Develop Tool for Deciphering Telomeres
If you have issues with telomeres (those “caps” of noncoding material at the ends of each chromosome) when using long-read sequencing for your genomic research, you should read about this new tool.
NCI-funded researchers used machine learning to develop a new approach for digitally measuring telomeres via nanopore long-read sequencing. With their approach, they were able to distinguish between healthy people and those with telomere disorders, some of which can set the stage for cancer.
Read the full report, “Digital Telomere Measurement by Long-Read Sequencing Distinguishes Healthy Aging From Disease,” in Nature Communications. You can access the code for the bioinformatic pipeline on GitHub.
As noted by corresponding author, Dr. Steven Artandi, of Stanford University, “Digital telomere measurement (DTM) gives us the ability to measure telomere lengths using whole-genome, long-read sequencing from human samples with unprecedented accuracy.”
He added, “There’s been such a paucity of telomere content information relative to the rest of the human genome. This is due, in part, to the crude nature of our current technologies to measure telomere length. Our study confirms that DTM is a useful tool for clinical investigations of these elusive structures—giving us insight into how they’re inherited over time, as well as how they impact cells and lead to diseases as well as aging.”