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A high-throughput computational framework for identifying significant copy number aberrations from array comparative genomic hybridisation data

机译:高通量计算框架,可从阵列比较基因组杂交数据中识别出显着的拷贝数畸变

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摘要

Reliable identification of copy number aberrations (CNA) from comparative genomic hybridization data would be improved by the availability of a generalised method for processing large datasets. To this end, we developed swatCGH, a data analysis framework and region detection heuristic for computational grids. swatCGH analyses sequentially displaced (sliding) windows of neighbouring probes and applies adaptive thresholds of varying stringency to identify the 10 of each chromosome that contains the most frequently occurring CNAs. We used the method to analyse a published dataset, comparing data preprocessed using four different DNA segmentation algorithms, and two methods for prioritising the detected CNAs. The consolidated list of the most commonly detected aberrations confirmed the value of swatCGH as a simplified high-throughput method for identifying biologically significant CNA regions of interest.
机译:通过可用于处理大型数据集的通用方法,可以改善从比较基因组杂交数据中可靠识别拷贝数畸变(CNA)的能力。为此,我们开发了swatCGH,数据分析框架和针对计算网格的区域检测启发式方法。 swatCGH分析相邻探针的顺序移位(滑动)窗口,并应用严格度不同的自适应阈值,以识别每个染色体中包含最频繁出现的CNA的10个。我们使用该方法分析已发布的数据集,比较使用四种不同的DNA分割算法预处理的数据,以及两种用于对检测到的CNA进行优先排序的方法。最常见检测到的像差的合并列表确认了swatCGH的价值,它是用于鉴定感兴趣的生物学上重要的CNA区域的简化的高通量方法。

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