Next generation tools for the annotation of human SNPs.
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.
Karchin
Rachel
R
Biomedical Engineering Department and Institute for Computational Medicine, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 212218, USA. karchin@jhu.edu
D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D020407:Internet / Q000639:trends; D020641:Polymorphism, Single Nucleotide; D012559:Schizophrenia / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods*; D014584:User-Computer Interface