Next generation tools for the annotation of human SNPs

Abstract

Computational biology has the opportunity to play an important role in the identification of functional single nucleotide polymorphisms (SNPs) discovered in large-scale genotyping studies, ultimately yielding new drug targets and biomarkers. The medical genetics and molecular biology communities are increasingly turning to computational biology methods to prioritize interesting SNPs found in linkage and association studies. Many such methods are now available through web interfaces, but the interested user is confronted with an array of predictive results that are often in disagreement with each other. Many tools today produce results that are difficult to understand without bioinformatics expertise, are biased towards non-synonymous SNPs, and do not necessarily reflect up-to-date versions of their source bioinformatics resources, such as public SNP repositories. Here, I assess the utility of the current generation of webservers; and suggest improvements for the next generation of webservers to better deliver value to medical geneticists and molecular biologists.

Figure 1:

Directed graph of relationships among SNP prediction webservers and their bioinformatics sources. A heuristic partition of the graph identifies three communities. They are loosely defined as (1) focus on protein properties (ellipse); (2) focus on regulation (trapezoid); and (3) connect to HapMap and consider linkage disequilibrium among SNPs (rectangle). SNPFunctionPortal is a singleton, perhaps an emerging community (white rectangle) that is equally connected to the protein and LD communities. LD = linkage disequilibrium.

Figure 2:

Trends in scope of SNP webservers. (a) Prior to 2006, protein-based servers that only handle non-synonymous SNPs were predominant. Newer servers include regulatory SNPs and annotate associations among SNPs through linkage disequilibrium estimates. (b) The earliest servers implemented original computational methods, but the current trend is towards meta-servers and hybrids. Dates associated with each server are based on date of first journal publication, unless an alternate date is documented on its website.