Suravajhala Prashanth, Benso Alfredo
Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy.
Adv Appl Bioinform Chem. 2017 Jun 12;10:57-64. doi: 10.2147/AABC.S123604. eCollection 2017.
Next-generation sequencing technology has provided resources to easily explore and identify candidate single-nucleotide polymorphisms (SNPs) and variants. However, there remains a challenge in identifying and inferring the causal SNPs from sequence data. A problem with different methods that predict the effect of mutations is that they produce false positives. In this hypothesis, we provide an overview of methods known for identifying causal variants and discuss the challenges, fallacies, and prospects in discerning candidate SNPs. We then propose a three-point classification strategy, which could be an additional annotation method in identifying causalities.
下一代测序技术为轻松探索和识别候选单核苷酸多态性(SNP)及变异提供了资源。然而,从序列数据中识别和推断因果SNP仍存在挑战。不同方法预测突变效应时存在的一个问题是会产生假阳性。在此假设中,我们概述了已知的识别因果变异的方法,并讨论了辨别候选SNP时的挑战、谬误和前景。然后我们提出了一种三点分类策略,这可能是识别因果关系时的一种额外注释方法。