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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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MetReS, an Efficient Database for Genomic Applications.

Jordi Vilaplana1, Rui Alves2, Francesc Solsona1

  • 11 Department of Computer Science and INSPIRES, University of Lleida , Lleida, Spain .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|November 30, 2017
PubMed
Summary
This summary is machine-generated.

The Metabolic Reconstruction Server (MetReS) database performance was evaluated. Migrating to Apache Hadoop demonstrated significant efficiency gains for large-scale biological data analysis.

Keywords:
Big data.HadoopMySQLgenomic database

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • MetReS is a genomic database supporting Biblio-MetReS and Homol-MetReS applications.
  • These applications address biological problems through text-mining and proteome annotation.

Purpose of the Study:

  • To identify performance bottlenecks in the MetReS database.
  • To assess scalability of performance improvements with larger datasets and complex analyses.

Main Methods:

  • Evaluated the performance of the existing MySQL relational database.
  • Tested the Apache Hadoop data-handling framework as an alternative.

Main Results:

  • Apache Hadoop shows potential for substantial efficiency improvements.
  • The framework is expected to scale effectively with increasing data and processing demands.

Conclusions:

  • Migrating to Apache Hadoop is recommended for enhanced MetReS application performance.
  • This migration will support future growth in data complexity and volume.