Erlich Yaniv, Gordon Assaf, Brand Michael, Hannon Gregory J, Mitra Partha P
Watson School of Biological Science, Cold Spring Harbor Laboratory, NY, 11724 USA.
IEEE Trans Inf Theory. 2010 Feb;56(2):706-723. doi: 10.1109/TIT.2009.2037043.
Over the past three decades we have steadily increased our knowledge on the genetic basis of many severe disorders. Nevertheless, there are still great challenges in applying this knowledge routinely in the clinic, mainly due to the relatively tedious and expensive process of genotyping. Since the genetic variations that underlie the disorders are relatively rare in the population, they can be thought of as a sparse signal. Using methods and ideas from compressed sensing and group testing, we have developed a cost-effective genotyping protocol to detect carriers for severe genetic disorders. In particular, we have adapted our scheme to a recently developed class of high throughput DNA sequencing technologies. The mathematical framework presented here has some important distinctions from the 'traditional' compressed sensing and group testing frameworks in order to address biological and technical constraints of our setting.
在过去三十年里，我们在许多严重疾病的遗传基础方面的知识稳步增长。然而，要在临床实践中常规应用这些知识仍面临巨大挑战，主要原因是基因分型过程相对繁琐且成本高昂。由于导致这些疾病的基因变异在人群中相对罕见，它们可被视为稀疏信号。利用压缩感知和分组测试的方法与理念，我们开发了一种经济高效的基因分型方案，用于检测严重遗传疾病的携带者。特别是，我们已将我们的方案适配于最近开发的一类高通量DNA测序技术。为了应对我们所面临的生物学和技术限制，这里提出的数学框架与“传统”的压缩感知和分组测试框架有一些重要区别。
