Bohannan Zachary S, Mitrofanova Antonina
Rutgers, The State University of New Jersey, School of Health Professions, Department of Health Informatics, 65 Bergen Street, Suite 120, Newark, NJ 07107-1709, United States of America.
Comput Struct Biotechnol J. 2019 Apr 8;17:561-569. doi: 10.1016/j.csbj.2019.04.002. eCollection 2019.
Deep sequencing genomic analysis is becoming increasingly common in clinical research and practice, enabling accurate identification of diagnostic, prognostic, and predictive determinants. Variant calling, distinguishing between true mutations and experimental errors, is a central task of genomic analysis and often requires sophisticated statistical, computational, and/or heuristic techniques. Although variant callers seek to overcome noise inherent in biological experiments, variant calling can be significantly affected by outside factors including those used to prepare, store, and analyze samples. The goal of this review is to discuss known experimental features, such as sample preparation, library preparation, and sequencing, alongside diverse biological and clinical variables, and evaluate their effect on variant caller selection and optimization.
深度测序基因组分析在临床研究和实践中越来越普遍，能够准确识别诊断、预后和预测决定因素。变异检测，即区分真正的突变和实验误差，是基因组分析的核心任务，通常需要复杂的统计、计算和/或启发式技术。尽管变异检测工具试图克服生物实验中固有的噪声，但变异检测可能会受到外部因素的显著影响，包括用于制备、储存和分析样本的因素。本综述的目的是讨论已知的实验特征，如样本制备、文库制备和测序，以及各种生物学和临床变量，并评估它们对变异检测工具选择和优化的影响。
