Eric Moyer
National Institutes of Health
4 Papers
Eric Moyer is an academic researcher from National Institutes of Health. The author has contributed to research in topics: RefSeq & dbSNP. The author has an hindex of 3, co-authored 4 publications.
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Papers
The GA4GH Variation Representation Specification: A computational framework for variation representation and federated identification
Alex H. Wagner,Alex H. Wagner,Lawrence J. Babb,Gil Alterovitz,Gil Alterovitz,Michael Baudis,Matthew H. Brush,Daniel L Cameron,Daniel L Cameron,Melissa S. Cline,Malachi Griffith,Obi L. Griffith,Sarah E. Hunt,David A. Kreda,Jennifer M. Lee,Stephanie Li,Javier Lopez,Eric Moyer,Tristan Nelson,Ronak Y. Patel,Kevin Riehle,Peter N. Robinson,Shawn Rynearson,Helen Schuilenburg,Kirill Tsukanov,Brian Walsh,Melissa A. Konopko,Heidi L. Rehm,Heidi L. Rehm,Andrew D. Yates,Robert R. Freimuth,Reece K. Hart +31 more
- 10 Nov 2021
TL;DR: The Variation Representation Specification (VRS) as discussed by the authors is an extensible framework for the computable representation of variation that complements contemporary human-readable and flat file standards for genomic variation representation.
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The GA4GH Variation Representation Specification (VRS): a Computational Framework for the Precise Representation and Federated Identification of Molecular Variation
Alex H. Wagner,Alex H. Wagner,Lawrence J. Babb,Gil Alterovitz,Gil Alterovitz,Michael Baudis,Matthew H. Brush,Daniel L Cameron,Daniel L Cameron,Melissa S. Cline,Malachi Griffith,Obi L. Griffith,Sarah E. Hunt,David A. Kreda,Jennifer Lee,Javier Lopez,Eric Moyer,Tristan Nelson,Ronak Y. Patel,Kevin Riehle,Peter N. Robinson,Shawn Rynearson,Helen Schuilenburg,Kirill Tsukanov,Brian Walsh,Melissa Konopko,Heidi L. Rehm,Heidi L. Rehm,Andrew D. Yates,Robert R. Freimuth,Reece K. Hart +30 more
TL;DR: The Variation Representation Specification (VRS) as discussed by the authors is an extensible framework for the semantically precise and computable representation of variation that complements contemporary human-readable and flat file standards for variation representation.
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SPDI: Data Model for Variants and Applications at NCBI
TL;DR: The SPDI (pronounced “speedy”) data model defines variants as a sequence of 4 operations: start at the boundary before the first position in the sequence S, advance P positions, delete D positions, then insert the sequence in the string I, giving the data model its name, SPDI.
SPDI: data model for variants and applications at NCBI.
TL;DR: This work states that normalizing sequence variants on a reference, projecting them across congruent sequences, and aggregating their diverse representations are critical to the elucidation of the genetic basis of disease and biological function.