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首页> 外文会议>International Conference on Bioinformatics and Biomedicine Workshops >Read-mapping using personalized diploid reference genome for RNA sequencing data reduced bias for detecting allele-specific expression
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Read-mapping using personalized diploid reference genome for RNA sequencing data reduced bias for detecting allele-specific expression

机译：使用个性化二倍体参考基因组进行RNA测序数据的读取映射减少了检测等位基因特异性表达的偏差

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Next generation sequencing (NGS) technologies have been applied extensively in many areas of genetics and genomics research. A fundamental problem when comes to analyzing NGS data is mapping short sequencing reads back to the reference genome. Most of existing software packages rely on a single uniform reference genome and do not automatically take into the consideration of genetic variants. On the other hand, large proportions of incorrectly mapped reads affect the correct interpretation of the NGS experimental results. As an example, Degner et al. showed that detecting allele-specific expression from RNA sequencing data was biased toward the reference allele. In this study, we developed a method that utilize DirectX 11 enabled graphics processing unit (GPU)'s parallel computing power to produces a personalized diploid reference genome based on all known genetic variants of that particular individual. We show that using such a personalized diploid reference genome can improve mapping accuracy and significantly reduce the bias toward reference allele in allele-specific expression analysis. Our method can be applied to any individual that has genotype information obtained either from array-based genotyping or resequencing. Besides the reference genome, no additional changes to alignment algorithm are needed for performing read mapping therefore one can utilize any of the existing read mapping tools and achieve the improved read mapping result. C++ and GPU compute shader source code of the software program is available at: http://code.google.com/p/diploid-mapping/downloads/list.
机译：下一代测序（NGS）技术已在遗传和基因组学研究的许多领域广泛应用。分析NGS数据时的基本问题是映射短测序读回参考基因组。现有的大多数现有软件包依赖于单个均匀参考基因组，不会自动考虑遗传变体。另一方面，大比例的错误映射读数会影响对NGS实验结果的正确解释。作为一个例子，Degner等人。表明，检测来自RNA测序数据的等位基因特异性表达偏向参考等位基因。在这项研究中，我们开发了一种利用DirectX 11的图形处理单元（GPU）的并联计算能力的方法，该方法基于该特定个体的所有已知的遗传变体产生个性化二倍体参考基因组。我们表明，使用这种个性化二倍体参考基因组可以提高映射精度，并显着降低等位基因特异性表达分析中的参考等位基因的偏差。我们的方法可以应用于任何具有从基于阵列的基因分型或重构获得的基因型信息的个体。除了参考基因组之外，不需要对对准算法的额外改变来执行读取映射，因此可以利用任何现有的读取映射工具并实现改进的读取映射结果。 C ++和GPU计算软件程序的着色器源代码可用于：http：//code.google.com/p/diploid-mappping/downloads/list。

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