Department of Molecular Cell and Developmental Biology, University of California, Santa Cruz, CA, USA.
Bioinformatics. 2011 Feb 15;27(4):441-8. doi: 10.1093/bioinformatics/btq695. Epub 2010 Dec 15.
The past decade has seen the introduction of fast and relatively inexpensive methods to detect genetic variation across the genome and exponential growth in the number of known single nucleotide variants (SNVs). There is increasing interest in bioinformatics approaches to identify variants that are functionally important from millions of candidate variants. Here, we describe the essential components of bioinformatics tools that predict functional SNVs.
Bioinformatics tools have great potential to identify functional SNVs, but the black box nature of many tools can be a pitfall for researchers. Understanding the underlying methods, assumptions and biases of these tools is essential to their intelligent application.
在过去的十年中,已经出现了快速且相对廉价的方法来检测整个基因组中的遗传变异,并且已知单核苷酸变体 (SNV) 的数量呈指数级增长。人们越来越感兴趣的是生物信息学方法,用于从数百万个候选变体中识别具有功能重要性的变体。在这里,我们描述了预测功能 SNV 的生物信息学工具的基本组成部分。
生物信息学工具具有识别功能 SNV 的巨大潜力,但许多工具的黑盒性质可能是研究人员的一个陷阱。了解这些工具的基本方法、假设和偏见对于它们的智能应用至关重要。