更好地理解来自高覆盖率样本的变异检测中的伪影
Towards Better Understanding of Artifacts in Variant Calling from High-Coverage Samples
摘要：
动机：全基因组高覆盖测序已广泛用于个人和癌症基因组学以及各种研究领域。然而，在缺乏无偏的全基因组真值集的情况下，即使在评估变异检测方法方面做出了巨大努力，变异识别的全局错误率和主要的因果伪影仍然不清楚。结果如下：我们使用两个读段作图器和五个变体调用器制作了十个SNP和INDEL调用集，均在相似覆盖度的单倍体人类基因组和二倍体基因组上。通过研究单倍体基因组中的假杂合调用，我们确定了低复杂性区域的错误重新排列和相对于样品的不完整参考基因组是错误的两个主要来源，这两个方面需要继续改进。我们估计原始基因型调用的错误率高达1/10- 15 kb，但后过滤调用的错误率降低到1/100- 200 kb，而不显著损害灵敏度。可用性：BWA-MEM对齐：http：//bit.ly/1g8XqRt;链接：https：//github.com/lh3/varcmp;其他数据：https://figshare.com/articles/Towards_better_understanding_of_artifacts_in_variating_calling_from_high_coverage_samples/981073
英文摘要：
Motivation: Whole-genome high-coverage sequencing has been widely used for personal and cancer genomics as well as in various research areas. However, in the lack of an unbiased whole-genome truth set, the global error rate of variant calls and the leading causal artifacts still remain unclear even given the great efforts in the evaluation of variant calling methods. Results: We made ten SNP and INDEL call sets with two read mappers and five variant callers, both on a haploid human genome and a diploid genome at a similar coverage. By investigating false heterozygous calls in the haploid genome, we identified the erroneous realignment in low-complexity regions and the incomplete reference genome with respect to the sample as the two major sources of errors, which press for continued improvements in these two areas. We estimated that the error rate of raw genotype calls is as high as 1 in 10-15kb, but the error rate of post-filtered calls is reduced to 1 in 100-200kb without significant compromise on the sensitivity. Availability: BWA-MEM alignment: http://bit.ly/1g8XqRt; Scripts: https://github.com/lh3/varcmp; Additional data: https://figshare.com/articles/Towards_better_understanding_of_artifacts_in_variating_calling_from_high_coverage_samples/981073
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
