title: In Silico Functional Annotation of Genomic Variation.
abstract: This unit describes the concepts and practical techniques for annotating genomic variants in the human genome to estimate their functional significance. With the rapid increase of available whole exome and whole genome sequencing information for human studies, annotation techniques have become progressively more important for highlighting and prioritizing nucleotide variants and their potential impact on genes and other genetic constructs. Here, we present an overview of different types of variant annotation approaches and elaborate on their foundations, assumptions, and the downstream consequences of their use. Computational approaches and tools to assign annotations and to identify variants are reviewed. Further, the general philosophy of assigning potential function to a genetic change within the biological context of a disease is discussed.
authors.affiliation: Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio.
authors.forename: Mariusz
authors.initials: M
authors.lastname: Butkiewicz
authors.affiliation: Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio.
authors.forename: William S
authors.initials: WS
authors.lastname: Bush
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D020022:Genetic Predisposition to Disease / Q000235:genetics; D014644:Genetic Variation; D015894:Genome, Human / Q000235:genetics*; D055106:Genome-Wide Association Study / Q000379:methods; D023281:Genomics / Q000379:methods; D006801:Humans; D058977:Molecular Sequence Annotation / Q000379:methods*; D015203:Reproducibility of Results
keywords: functional prediction; genome annotation; sequence analysis; variant annotation