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Analysis of genomic rearrangements by using the Burrows-Wheeler transform of short-read data

机译:利用短读数据的Burrows-Wheeler变换分析基因组重排

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Background The potential utility of the Burrows-Wheeler transform (BWT) of a large amount of short-read data ("reads") has not been fully studied. The BWT basically serves as a lossless dictionary of reads, unlike the heuristic and lossy reads-to-genome mapping results conventionally obtained in the first step of sequence analysis. Thus, it is naturally expected to lead to development of sensitive methods for analysis of short-read data. Recently, one of the most active areas of research in sequence analysis is sensitive detection of rare genomic rearrangements from whole-genome sequencing (WGS) data of heterogeneous cancer samples. The application the BWT of reads to the analysis of genomic rearrangements is addressed in this study. Results A new method for sensitive detection of genomic rearrangements by using the BWT of reads in the following three steps is proposed: first, breakpoint regions, which contain breakpoints and are joined together by rearrangement, are predicted from the distribution of so-called discordant pairs by using a kind of the conjugate gradient method; second, reads partially matching the breakpoint regions are collected from the BWT of reads; and third, breakpoints are detected as branching points among the collected reads, and their precise positions are determined. The method was experimentally implemented, and its performance (i.e., sensitivity and specificity) was evaluated by using simulated data with known artificial rearrangements. It was applied to publicly available real biological WGS data of cancer patients, and the detection results were compared with published results. Conclusions Serving as a lossless dictionary of reads, the BWT of short reads enables sensitive analysis of genomic rearrangements in heterogeneous cancer-genome samples when used in conjunction with breakpoint-region predictions based on a conjugate gradient method.
机译:背景技术尚未完全研究大量短读取数据(“读取”)的Burrows-Wheeler变换(BWT)的潜在效用。 BWT基本上用作读取的无损字典,这与在序列分析的第一步中通常获得的启发式和有损读取基因组映射结果不同。因此,自然期望导致短读数据分析的灵敏方法的发展。最近,序列分析研究中最活跃的领域之一是从异质癌症样本的全基因组测序(WGS)数据中敏感地检测罕见的基因组重排。这项研究解决了读取的BWT在基因组重排分析中的应用。结果提出了一种新的方法,该方法可通过以下三个步骤使用读取的BWT灵敏地检测基因组重排:首先,从所谓的不一致对的分布中预测包含断点并通过重排连接在一起的断点区域通过使用一种共轭梯度法;第二,从读取的BWT中收集与断点区域部分匹配的读取;第三,将断点检测为收集的读数之间的分支点,并确定其精确位置。该方法是通过实验实现的,其性能(即敏感性和特异性)是通过使用具有已知人工重排的模拟数据进行评估的。将其应用于可公开获得的癌症患者实际生物学WGS数据,并将检测结果与已发表的结果进行比较。结论短读的BWT作为读物的无损字典,当与基于共轭梯度法的断点区域预测结合使用时,可以对异质癌基因组样本中的基因组重排进行灵敏的分析。

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