A negative storage model for precise but compact storage of genetic variation data. (15th April 2020)

• Record Type: Journal Article
• Authors: Gonzalez-Calderon, Guillermo; Liu, Ruizheng; Carvajal, Rodrigo; Teer, Jamie K
• Is Part Of: Database. Volume 2020(2020)
• Publication Date: 2020-04-15
• Subjects: data storage -- next-generation sequencing -- genetic variation; Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/baz158
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract: Falling sequencing costs and large initiatives are resulting in increasing amounts of data available for investigator use. However, there are informatics challenges in being able to access genomic data. Performance and storage are well-appreciated issues, but precision is critical for meaningful analysis and interpretation of genomic data. There is an inherent accuracy vs. performance trade-off with existing solutions. The most common approach (Variant-only Storage Model, VOSM) stores only variant data. Systems must therefore assume that everything not variant is reference, sacrificing precision and potentially accuracy. A more complete model (Full Storage Model, FSM) would store the state of every base (variant, reference and missing) in the genome thereby sacrificing performance. A compressed variation of the FSM can store the state of contiguous regions of the genome as blocks (Block Storage Model, BLSM), much like the file-based gVCF model. We propose a novel approach by which this state is encoded such that both performance and accuracy are maintained. The Negative Storage Model (NSM) can store and retrieve precise genomic state from different sequencing sources, including clinical and whole exome sequencing panels. Reduced storage requirements are achieved by storing only the variant and missing states and inferring the reference state. We evaluate the performance characteristics of FSM, BLSM and NSM and demonstrate dramatic improvements in storage and performance using the NSM approach.