A new algorithm identifies mutation patterns across coding and noncoding DNA

A new algorithm identifies mutation patterns across coding and noncoding DNA

Researchers have been able to study 2 percent of the human genome in depth, which includes protein-coding DNA sequences. While mutations in this area of the genome have led to the understanding of the nature of many diseases, several disease-related mutations also happen in noncoding regions of the genome that comprises the remaining 98 percent—parts that do not directly make proteins but control genes’ behavior. Professor Brendan Frey of the University of Toronto, who studies genetic networks, has developed a “deep-learning” machine algorithm that can recognize patterns of mutation across coding and noncoding DNA. Frey’s system predicts whether or not a mutation will cause a change in RNA splicing that could lead to a disease phenotype. The algorithm was tested on autism spectrum disorder and it not only confirmed the existing knowledge about autism genetics but also uncovered 17 new disease-causing genes. Frey’s method combines whole-genome analysis and predictive models for RNA splicing, which can contribute significantly to the development of new treatment methods and diagnoses.

Read more in Scientific American.  

Image: Wikimedia Commons

期待学术生涯高歌猛进,发表过程一帆风顺?

来加入我们活力洋溢的在线社区吧。免费注册,无限阅览。

社交账号一键登入

已有54300名科研人员在此注册。

意得辑专家视点 Editage Insights 目前正在维护中。维护期间,部分站点功能,如登录、注册可能无法正常工作。

觉得有用?

如果是的话,和你的同事分享吧