Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

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

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie - 1 Jan 2022

Moreira Souza André, Weigert Rodrigo de Andrade Santos, Machado de Sousa Elaine Parros, Tassoni Andrietta Lucas, Ventura Ricardo Vieira

Abstract excerpt

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,...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Database Management Systems
Share to Genomics

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genotype

Share to Information Storage and Retrieval

Share to Phenotype

Source

Identifiers

PMID: 34459042
DOI: 10.1111/jbg.12644

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Practical implications of using non-relational databases to store large genomic data files and novel phenotypes.PMID 34459042Read ↗

DNA variant databases: current state and future directions.Semantic proximity Read ↗

A dynamic query system for supporting phenotype mining in genetic studies.Semantic proximity Read ↗

High density genotype storage for plant breeding in the Chado schema of Breedbase.Semantic proximity Read ↗

Genotype-phenotype databases: challenges and solutions for the post-genomic era.Semantic proximity Read ↗

The phenotype and genotype experiment object model (PaGE-OM): a robust data structure for information related to DNA variation.Semantic proximity Read ↗

Damming the genomic data flood using a comprehensive analysis and storage data structure.Semantic proximity Read ↗

Locus-specific database domain and data content analysis: evolution and content maturation toward clinical use.Semantic proximity Read ↗

Data acquisition, data storage, and data presentation in a modern genetics laboratory.Semantic proximity Read ↗

New tools and methods for direct programmatic access to the dbSNP relational database.Semantic proximity Read ↗

The challenge of managing the evolution of genomics data over time: a conceptual model-based approach.Semantic proximity Read ↗

Integrating clinical and laboratory data in genetic studies of complex phenotypes: a network-based data management system.Semantic proximity Read ↗

eCOMPAGT -- efficient combination and management of phenotypes and genotypes for genetic epidemiology.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.