This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Structural variants and so-called tandem repeats are individual differences in the human genome. They are increasingly linked to diseases, but analyzing and interpreting them poses challenges for researchers.
In two new studies published in the journals NAR Genomics & Bioinformatics and Genome Biology, Martin Vingron's laboratory at the Max Planck Institute for Molecular Genetics presents new bioinformatics tools. These tools are designed to help identify pathogenic structural variants and tandem repeats in the genome more easily, thereby facilitating the genetic diagnosis of many diseases. With the help of modern sequencing methods, the causes of many diseases can be precisely located in the genome.
Nevertheless, only about 30% to 40% of patients receive a clear molecular diagnosis. One reason is that, for a long time, the focus was primarily on point mutations—that is, the substitution of individual letters in the genetic sequence. Only recently have so-called structural variants and tandem repeats come to the forefront.
Structural variants affect entire sections of the genome: they may be missing, duplicated or displaced. Tandem repeats, on the other hand, are short sequences repeated many times. Both make every genome unique and are increasingly linked to diseases.
"The most common approach in genetic diagnostics is short-read sequencing, which reads short stretches of DNA," explains Nico Alavi, the first author of the study in Genome Biology. "Because structural variants are often larger than the read segments themselves, they are difficult to detect in this data." The research group has now turned to machine learning. "The core idea is that this method allows us to learn the patterns behind real structural variants and thus correctly classify new variants," Alavi explains.
This makes structural variants useful for diagnostics, as it remedies a major problem with previous methods, in which false positives often arise and must be checked manually. The researchers then tested their approach using patient data. Their tool, called "Dicast," was able to detect all pathogenic structural variants while filtering out a large number of false-positive artifacts.
Extract — continue reading at the source.
