Using bioinformatics to predict the functional impact of SNVs

Abstract

Motivation: 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.

Results: 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.

Fig. 1.

Flow chart for informed use of SNV function prediction tools. Black box metaservers can be useful for narrowing down a large number of SNVs to a tractable set. SNVs in coding regions may have regulatory impact and should be assessed by both cSNV and rSNV tools. Once a tractable set of SNVs is selected, tools should be carefully evaluated and used only if they meet the criteria shown. After a tool is applied, users should be careful that they both understand the tool's output and can rationalize for themselves why SNVs are predicted to be functional or not. Finally, results can be used as candidates for planned clinical studies or functional testing.