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Efficient algorithms for tandem copy number variation reconstruction in repeat-rich regions

机译:富重复区中串联拷贝数变异重建的高效算法

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Motivation: Structural variations and in particular copy number variations (CNVs) have dramatic effects of disease and traits. Technologies for identifying CNVs have been an active area of research for over 10 years. The current generation of high-throughput sequencing techniques presents new opportunities for identification of CNVs. Methods that utilize these technologies map sequencing reads to a reference genome and look for signatures which might indicate the presence of a CNV. These methods work well when CNVs lie within unique genomic regions. However, the problem of CNV identification and reconstruction becomes much more challenging when CNVs are in repeat-rich regions, due to the multiple mapping positions of the reads.Results: In this study, we propose an efficient algorithm to handle these multi-mapping reads such that the CNVs can be reconstructed with high accuracy even for repeat-rich regions. To our knowledge, this is the first attempt to both identify and reconstruct CNVs in repeat-rich regions. Our experiments show that our method is not only computationally efficient but also accurate.
机译:动机:结构变异,尤其是拷贝数变异(CNV)对疾病和性状有巨大影响。十多年来,用于识别CNV的技术一直是研究的活跃领域。当前一代的高通量测序技术为鉴定CNV提供了新的机会。利用这些技术的方法将测序读数映射到参考基因组,并寻找可能表明CNV存在的特征。当CNV位于独特的基因组区域内时,这些方法效果很好。但是,由于CNV位于重复序列丰富的区域,由于读取的多个映射位置,因此CNV的识别和重构问题变得更具挑战性。这样,即使对于重复序列丰富的区域,也可以高精度地重建CNV。据我们所知,这是在富含重复序列的区域识别和重建CNV的首次尝试。我们的实验表明,我们的方法不仅计算效率高而且准确。

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