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使用真实短读长数据对插入缺失(Indel)检测工具进行性能评估。

Performance evaluation of indel calling tools using real short-read data.

作者信息

Hasan Mohammad Shabbir, Wu Xiaowei, Zhang Liqing

机构信息

Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA.

Department of Statistics, Virginia Tech, Blacksburg, VA, 24061, USA.

出版信息

Hum Genomics. 2015 Aug 19;9(1):20. doi: 10.1186/s40246-015-0042-2.

Abstract

BACKGROUND

Insertion and deletion (indel), a common form of genetic variation, has been shown to cause or contribute to human genetic diseases and cancer. With the advance of next-generation sequencing technology, many indel calling tools have been developed; however, evaluation and comparison of these tools using large-scale real data are still scant. Here we evaluated seven popular and publicly available indel calling tools, GATK Unified Genotyper, VarScan, Pindel, SAMtools, Dindel, GTAK HaplotypeCaller, and Platypus, using 78 human genome low-coverage data from the 1000 Genomes project.

RESULTS

Comparing indels called by these tools with a known set of indels, we found that Platypus outperforms other tools. In addition, a high percentage of known indels still remain undetected and the number of common indels called by all seven tools is very low.

CONCLUSION

All these findings indicate the necessity of improving the existing tools or developing new algorithms to achieve reliable and consistent indel calling results.

摘要

背景

插入缺失(indel)是一种常见的基因变异形式,已被证明会导致或促成人类遗传疾病和癌症。随着下一代测序技术的发展,已经开发了许多插入缺失检测工具;然而,使用大规模真实数据对这些工具进行评估和比较仍然很少。在这里,我们使用来自千人基因组计划的78个人类基因组低覆盖数据,评估了七种流行且公开可用的插入缺失检测工具,即GATK统一基因分型器、VarScan、Pindel、SAMtools、Dindel、GTAK单倍型分型器和Platypus。

结果

将这些工具检测到的插入缺失与一组已知的插入缺失进行比较,我们发现Platypus的性能优于其他工具。此外,仍有很高比例的已知插入缺失未被检测到,并且所有七种工具检测到的常见插入缺失数量非常少。

结论

所有这些发现表明,有必要改进现有工具或开发新算法,以获得可靠且一致的插入缺失检测结果。

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