BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data.
BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data.
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DOI：
10.1093/bioinformatics/btw044
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发表时间：
2016-06-01
期刊：
影响因子：
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通讯作者：
Durbin R
中科院分区：
文献类型：
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作者：
Narasimhan V;Danecek P;Scally A;Xue Y;Tyler-Smith C;Durbin R
摘要：纯合子序列（RoHs）是二倍体基因组的基因组延伸，在两条染色体上显示相同的等位基因。较长的RoHs不太可能是偶然出现的，但很可能表明自合子，即基因组的两个拷贝来自同一个最近的祖先。早期检测RoH的工具使用基因型阵列数据，但从测序数据中可以获得更多信息。在这里，我们展示并评估了BCFtools/RoH，它是BCFtools软件包的扩展，可以使用隐马尔可夫模型检测测序数据中的自合子区域，特别是外显子组数据。通过将其应用于1000基因组计划的模拟数据和实际数据，我们估计了它的准确性，并表明在一系列测序错误率和自合子水平下，它比现有方法具有更高的灵敏度和特异性。可用性和实现：BCFtools/RoH及其相关的二进制/源文件可从https://github.com/samtools/BCFtools免费获得。联系方式：vn2@sanger.ac.uk或pd3@sanger.ac.uk补充信息：补充数据可在Bioinformatics在线获取。
Summary: Runs of homozygosity (RoHs) are genomic stretches of a diploid genome that show identical alleles on both chromosomes. Longer RoHs are unlikely to have arisen by chance but are likely to denote autozygosity, whereby both copies of the genome descend from the same recent ancestor. Early tools to detect RoH used genotype array data, but substantially more information is available from sequencing data. Here, we present and evaluate BCFtools/RoH, an extension to the BCFtools software package, that detects regions of autozygosity in sequencing data, in particular exome data, using a hidden Markov model. By applying it to simulated data and real data from the 1000 Genomes Project we estimate its accuracy and show that it has higher sensitivity and specificity than existing methods under a range of sequencing error rates and levels of autozygosity. Availability and implementation: BCFtools/RoH and its associated binary/source files are freely available from https://github.com/samtools/BCFtools. Contact: vn2@sanger.ac.uk or pd3@sanger.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.