Benchmarking database systems for Genomic Selection implementation. (11th September 2019)

• Record Type: Journal Article
• Authors: Nti-Addae, Yaw; Matthews, Dave; Ulat, Victor Jun; Syed, Raza; Sempéré, Guilhem; Pétel, Adrien; Renner, Jon; Larmande, Pierre; Guignon, Valentin; Jones, Elizabeth; Robbins, Kelly
• Is Part Of: Database. Volume 2019(2019)
• Publication Date: 2019-09-11
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/baz096
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract: Motivation: With high-throughput genotyping systems now available, it has become feasible to fully integrate genotyping information into breeding programs. To make use of this information effectively requires DNA extraction facilities and marker production facilities that can efficiently deploy the desired set of markers across samples with a rapid turnaround time that allows for selection before crosses needed to be made. In reality, breeders often have a short window of time to make decisions by the time they are able to collect all their phenotyping data and receive corresponding genotyping data. This presents a challenge to organize information and utilize it in downstream analyses to support decisions made by breeders. In order to implement genomic selection routinely as part of breeding programs, one would need an efficient genotyping data storage system. We selected and benchmarked six popular open-source data storage systems, including relational database management and columnar storage systems. Results: We found that data extract times are greatly influenced by the orientation in which genotype data is stored in a system. HDF5 consistently performed best, in part because it can more efficiently work with both orientations of the allele matrix. Availability: http://gobiin1.bti.cornell.edu:6083/projects/GBM/repos/benchmarking/browse