HIVE-七边形：一种具有比对后质量控制的合理变异检测算法。

HIVE-heptagon: A sensible variant-calling algorithm with post-alignment quality controls.

作者信息

Simonyan Vahan, Chumakov Konstantin, Donaldson Eric, Karagiannis Konstantinos, Lam Phuc VinhNguyen, Dingerdissen Hayley, Voskanian Alin

机构信息

Center for Biologics Evaluation and Research, US Food and Drug Administration, 10993 New Hampshire Ave., Silver Spring, MD, United States.

Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, 10993 New Hampshire Ave., United States.

出版信息

Genomics. 2017 Jul;109(3-4):131-140. doi: 10.1016/j.ygeno.2017.01.002. Epub 2017 Feb 8.

DOI:10.1016/j.ygeno.2017.01.002

PMID:28188908

Abstract

UNLABELLED

Advances in high-throughput sequencing (HTS) technologies have greatly increased the availability of genomic data and potential discovery of clinically significant genomic variants. However, numerous issues still exist with the analysis of these data, including data complexity, the absence of formally agreed upon best practices, and inconsistent reproducibility. Toward a more robust and reproducible variant-calling paradigm, we propose a series of selective noise filtrations and post-alignment quality control (QC) techniques that may reduce the rate of false variant calls. We have implemented both novel and refined post-alignment QC mechanisms to augment existing pre-alignment QC measures. These techniques can be used independently or in combination to identify and correct issues caused during data generation or early analysis stages. The adoption of these procedures by the broader scientific community is expected to improve the identification of clinically significant variants both in terms of computational efficiency and in the confidence of the results.

AVAILABILITY

https://hive.biochemistry.gwu.edu/.

摘要

未标注

高通量测序（HTS）技术的进步极大地提高了基因组数据的可用性，并增加了发现具有临床意义的基因组变异的可能性。然而，这些数据的分析仍存在许多问题，包括数据复杂性、缺乏正式认可的最佳实践以及可重复性不一致。为了实现更稳健和可重复的变异检测范式，我们提出了一系列选择性噪声过滤和比对后质量控制（QC）技术，这些技术可能会降低错误变异检测的发生率。我们已经实施了新颖且完善的比对后QC机制，以增强现有的比对前QC措施。这些技术可以独立使用或组合使用，以识别和纠正数据生成或早期分析阶段出现的问题。预计更广泛的科学界采用这些程序将在计算效率和结果可信度方面提高对具有临床意义的变异的识别。