You are here

A new algorithm identifies mutation patterns across coding and noncoding DNA

Editage Insights | 2014年12月19日 | 4,182 浏览次数
A new algorithm identifies mutation patterns across coding and noncoding DNA

Researchers have been able study 2 percent of the human genome in depth, which includes protein-coding DNA sequences. While mutations in this area of 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


《意得辑专家视点》深信知识需要开放给所有大众并传播,因此我们鼓励读者重复发表我们的内容,重复发表形式可为在线或印刷。我们采用知识共享(Creative Commons license),只要您遵守以下事项,即可免费重复发表我们的内容:
  • 作者信息:请尊重我们的作者,他们花费了时间精力为您撰写这些有价值的内容,重复发表时加注作者信息。
  • 意得辑专家视点:必须注明文章出自《意得辑专家视点》。
  • 表达您的情意:您可以加句“前往《意得辑专家视点》阅读全文”之类的话,啊,还有,别忘了加上文章链接。
  • 重复使用图片:要使用某些文章的图片必须事先取得许可,并加注图片原始出处。
  • 镶嵌代码:要重复使用这篇文章最简单的方式就是将下面的代码复制贴上您的页面!