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Shared data science infrastructure for genomics data.

Hamid Bagheri1, Usha Muppirala2, Rick E Masonbrink2

  • 1Department of Computer Science, Iowa State University, 226 Atanasoff Hall, Ames, 50011, USA. hbagheri@iastate.edu.

BMC Bioinformatics
|August 24, 2019
PubMed
Summary
This summary is machine-generated.

Boa for genomics (Boa_g) offers a scalable computational infrastructure for analyzing biological data. This domain-specific language efficiently processes large datasets, improving upon existing solutions for genomics research.

Keywords:
BoagDomain-Specific LanguageGenome AnnotationShared Data Science Infrastructure

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Analyzing vast biological data requires scalable computational infrastructure.
  • Existing methods face challenges in organizing, extracting, and analyzing data from repositories.
  • Shared data science infrastructures are crucial for efficient data processing.

Purpose of the Study:

  • To introduce Boa for genomics (Boa_g), a shared data science infrastructure.
  • To demonstrate Boa_g's capability in analyzing large-scale biological data.
  • To showcase Boa_g's efficiency in handling genome assembly and annotation data.

Main Methods:

  • Implementation of Boa for genomics (Boa_g) as a proof of concept.
  • Utilizing domain-specific language integrated with Hadoop infrastructure.
  • Analysis of RefSeq's 153,848 annotation (GFF) and assembly (FASTA) file metadata.

Main Results:

  • Boa_g significantly reduces storage footprint compared to Python and MongoDB.
  • Queries on large datasets are expedited through automated parallelization and distribution via Hadoop.
  • Identified trends in genome sizes, exon frequencies, assembly programs, and improvements in animal genome assemblies since 2016.

Conclusions:

  • Innovative methods are essential to manage the increasing volume of biological data.
  • Boa_g enhances researchers' access to and ability to explore biological data efficiently.
  • Boa_g demonstrates potential for analyzing large biological datasets, exemplified by RefSeq data analysis.