Reductions in sequencing costs and increases in sequencing efficiency are quickly making high-throughput sequencing accessible to individual laboratories looking to use sequencing as a powerful tool in their research endeavors. In fact, as costs continue to decline, we can expect high-throughput sequencing to become a commonly used tool, not only in human phenotype based sequencing projects, but also as an effective tool in forward genetics applications in model organisms, and potentially for the diagnosis idiopathic disease. However, very few laboratories have the computational expertise and infrastructure to make sense of the genetic variants identified through these studies. The goal of this proposal is to make high-throughput sequencing data interpretation as accessible as data generation through expansion of the Scripps Genome Annotation and Distributed Variant Interpretation SERver (SG-ADVISER) and companion data processing and visualization tools. SG-ADVISER is a web-server based tool for holistic, in-depth, annotations and functional predictions of variants generated from high-throughput sequencing. Annotations are formed on at least four major levels: 1) annotation of the genomic element within which a variant resides; 2) prediction of the functional impact of a variant on a genomic element; 3) annotation of molecular and biological processes which link variants across genes and/or genomic elements with one another, and 4) annotation of known clinical characteristics of the gene or variant. The annotations currently provided by SG-ADVISER cover many of these levels of annotation, but are incomplete. Therefore, we propose to expand the capabilities of SG-ADVISER to cover as many generally interesting annotation types as possible, while also extending SG-ADVISER's capabilities to model organism studies. Moreover, we recognize a need for flexibility, and have included a plan to provide customized annotations through the SG-ADVISER web-server. Finally, we feel that truly powerful data interpretation can only be achieved through visualization of massive datasets. Therefore, we propose a plan to produce simple companion tools to process, filter, and visualize SG-ADVISER annotations through currently available genome browsers.
Identification and interpretation of variants associated with inherited but not strongly familial disease is a crucial step in translating the investment in huma genome sequencing efforts into a truly significant impact on public health. Annotation, prioritization and grouping of variants logically will be required to bring enough statistical powe to sequencing studies so that disease causing variants can be identified.
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