The majority of copy number callers requires high read coverage data that is often achieved with elevated material input, which increases the heterogeneity of tissue samples. However, to gain insights into smaller areas within a tissue sample, e.g. a cancerous area in a heterogeneous tissue sample, less material is used for sequencing, which results in lower read coverage. Therefore, more focus needs to be put on copy number calling that is sensitive enough for low coverage data. We present MetaCNV, a copy number caller that infers reliable copy numbers for human genomes with a consensus approach. MetaCNV specializes in low coverage data, but also performs well on normal and high coverage data. MetaCNV integrates the results of multiple copy number callers and infers absolute and unbiased copy numbers for the entire genome. MetaCNV is based on a meta-model that bypasses the weaknesses of current calling models while combining the strengths of existing approaches. Here we apply MetaCNV based on ReadDepth, SVDetect, and CNVnator to real and simulated datasets in order to demonstrate how the approach improves copy number calling. MetaCNV, available at , provides accurate copy number prediction on low coverage data and performs well on high coverage data.

中文翻译：

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MetaCNV-一种共识性方法，可以从低覆盖率数据推断出准确的拷贝数。

大多数拷贝数调用者需要高读取覆盖率数据，这通常是通过增加材料输入来实现的，这会增加组织样本的异质性。但是，为了深入了解组织样本中的较小区域（例如异质组织样本中的癌性区域），需要使用较少的材料进行测序，从而导致读取覆盖率降低。因此，需要更加关注对低覆盖率数据足够敏感的副本号码呼叫。我们介绍了MetaCNV，它是一个拷贝数调用者，它以一种共识方法推断出人类基因组的可靠拷贝数。MetaCNV专门研究低覆盖率数据，但在普通和高覆盖率数据上也表现出色。MetaCNV整合了多个拷贝数调用者的结果，并推断出整个基因组的绝对拷贝数和无偏拷贝数。MetaCNV基于一个元模型，该模型绕开了当前调用模型的弱点，同时结合了现有方法的优点。在这里，我们将基于ReadDepth，SVDetect和CNVnator的MetaCNV应用于真实和模拟的数据集，以演示该方法如何改善拷贝数调用。可从处获得的MetaCNV在低覆盖率数据上提供准确的拷贝数预测，并在高覆盖率数据上表现良好。