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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Practical implications of using non-relational databases to store large genomic data files and novel phenotypes

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Author(s):
Souza, Andre Moreira [1] ; Santos Weigert, Rodrigo de Andrade [1] ; Machado de Sousa, Elaine Parros [1] ; Andrietta, Lucas Tassoni [2] ; Ventura, Ricardo Vieira [2]
Total Authors: 5
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, Sch Vet Med & Anim Sci, Dept Anim Nutr & Prod, BR-13635900 Pirassununga, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: JOURNAL OF ANIMAL BREEDING AND GENETICS; v. 139, n. 1 AUG 2021.
Web of Science Citations: 0
Abstract

The objective of our study was to provide practical directions on the storage of genomic information and novel phenotypes (treated here as unstructured data) using a non-relational database. The MongoDB technology was assessed for this purpose, enabling frequent data transactions involving numerous individuals under genetic evaluation. Our study investigated different genomic (Illumina Final Report, PLINK, 0125, FASTQ, and VCF formats) and phenotypic (including media files) information, using both real and simulated datasets. Advantages of our centralized database concept include the sublinear running time for queries after increasing the number of samples/markers exponentially, in addition to the comprehensive management of distinct data formats while searching for specific genomic regions. A comparison of our non-relational and generic solution, with an existing relational approach (developed for tabular data types using 2 bits to store genotypes), showed reduced importing time to handle 50M SNPs (PLINK format) achieved by the relational schema. Our experimental results also reinforce that data conversion is a costly step required to manage genomic data into both relational and non-relational database systems, and therefore, must be carefully treated for large applications. (AU)

FAPESP's process: 20/04461-6 - Applications of machine learning and genomic data to improve economic traits in dairy cattle
Grantee:Lucas Tassoni Andrietta
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 16/19514-2 - Development of a genomic database for Nellore cattle and computational tools for implementing large-scale studies
Grantee:Ricardo Vieira Ventura
Support Opportunities: Research Grants - Young Investigators Grants