Motivation

Whole genome sequencing (WGS) is widely used for copy number variation (CNV) detection. However, for most bacteria, their circular genome structure and high replication rate make reads more enriched near the replication origin. CNV detection based on read depth could be seriously influenced by such replication bias.

Results

We show that the replication bias is widespread using ~200 bacterial WGS data. We develop CNV-BAC that can properly normalize the replication bias as well as other known biases in bacterial WGS data and can accurately detect CNVs. Simulation and real data analysis show that CNV-BAC achieves the best performance in CNV detection compared with available algorithms.

Availability and implementation

CNV-BAC is available at https://github.com/XiDsLab/CNV-BAC.

中文翻译：

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CNV-BAC：细菌环状基因组中拷贝数变异检测。

动机

全基因组测序（WGS）被广泛用于拷贝数变异（CNV）检测。但是，对于大多数细菌而言，它们的环状基因组结构和高复制率使复制起点附近的读数更加丰富。基于读取深度的CNV检测可能会受到此类复制偏差的严重影响。

结果

我们显示，使用〜200个细菌WGS数据，复制偏差普遍存在。我们开发了CNV-BAC，它可以正确地标准化细菌WGS数据中的复制偏差以及其他已知偏差，并可以准确检测CNV。仿真和实际数据分析表明，与可用算法相比，CNV-BAC在CNV检测中达到了最佳性能。

可用性和实施

CNV-BAC可从https://github.com/XiDsLab/CNV-BAC获得。