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A graph-based algorithm for estimating clonal haplotypes of tumor sample from sequencing data

机译:一种基于图的算法可从测序数据估算肿瘤样品的克隆单倍型

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摘要

BackgroundHaplotype phasing is an important step in many bioinformatics workflows. In cancer genomics, it is suggested that reconstructing the clonal haplotypes of a tumor sample could facilitate a comprehensive understanding of its clonal architecture and further provide valuable reference in clinical diagnosis and treatment. However, the sequencing data is an admixture of reads sampled from different clonal haplotypes, which complicates the computational problem by exponentially increasing the solution-space and leads the existing algorithms to an unacceptable time-/space- complexity. In addition, the evolutionary process among clonal haplotypes further weakens those algorithms by bringing indistinguishable candidate solutions.
机译:背景单倍型定相是许多生物信息学工作流程中的重要步骤。在癌症基因组学中,建议重建肿瘤样品的克隆单倍型可以促进对其克隆结构的全面了解,并进一步为临床诊断和治疗提供有价值的参考。然而,测序数据是从不同的克隆单倍型采样的读数的混合,这通过成指数地增加解空间而使计算问题复杂化,并使现有算法陷入不可接受的时间/空间复杂度。此外,克隆单倍型之间的进化过程通过带来难以区分的候选解进一步削弱了那些算法。

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