Benchmarking database systems for Genomic Selection implementation

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

[1]  Reynold Xin,et al.  Apache Spark , 2016 .

[2]  Yike Guo,et al.  High dimensional biological data retrieval optimization with NoSQL technology , 2014, BMC Genomics.

[3]  Felipe Meneguzzi,et al.  NeuroView: a customizable browser-base utility , 2016 .

[4]  Todd M. Smith,et al.  Standardizing the next generation of bioinformatics software development with BioHDF (HDF5). , 2010, Advances in experimental medicine and biology.

[5]  Alberto Riva,et al.  BigQ: a NoSQL based framework to handle genomic variants in i2b2 , 2015, BMC Bioinformatics.

[6]  Aaron R. Quinlan,et al.  Poretools: a toolkit for analyzing nanopore sequence data , 2014, bioRxiv.

[7]  Pierre Larmande,et al.  Gigwa—Genotype investigator for genome-wide analyses , 2016, GigaScience.

[8]  Wensheng Wang,et al.  SNP-Seek database of SNPs derived from 3000 rice genomes , 2014, Nucleic Acids Res..

[9]  John M Hickey,et al.  Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery , 2017, Nature Genetics.

[10]  Robert J. Elshire,et al.  TASSEL-GBS: A High Capacity Genotyping by Sequencing Analysis Pipeline , 2014, PloS one.

[11]  Michael J. Thomson,et al.  High-Throughput SNP Genotyping to Accelerate Crop Improvement , 2014 .

[12]  Muhammad Ali Ismail,et al.  Performance Comparison of Spark Clusters Configured Conventionally and a Cloud Service , 2016 .

[13]  Hans D. Daetwyler,et al.  Genomic selection in crops, trees and forages: a review , 2014, Crop and Pasture Science.

[14]  M. McMullen,et al.  Genetic Properties of the Maize Nested Association Mapping Population , 2009, Science.

[15]  M. Goddard,et al.  Prediction of total genetic value using genome-wide dense marker maps. , 2001, Genetics.

[16]  Ying Zhang,et al.  Genome sequence analysis with MonetDB , 2015, Datenbank-Spektrum.

[17]  Lavanya Ramakrishnan,et al.  Performance evaluation of a MongoDB and hadoop platform for scientific data analysis , 2013, Science Cloud '13.

[18]  Kevin R Coombes,et al.  Relax with CouchDB--into the non-relational DBMS era of bioinformatics. , 2012, Genomics.

[19]  William Stafford Noble,et al.  The Genomedata format for storing large-scale functional genomics data , 2010, Bioinform..

[20]  Thomas J. S. Durant,et al.  Evaluation of relational and NoSQL database architectures to manage genomic annotations , 2016, J. Biomed. Informatics.

[21]  Yingrui Li,et al.  Construction of the third-generation Zea mays haplotype map , 2015, bioRxiv.

[22]  Maristela Holanda,et al.  A study of genomic data provenance in NoSQL document-oriented database systems , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).