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Development of Compendium for Esophageal Squamous Cell Carcinoma
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Efficient querying of genomic reference databases with gget.

Laura Luebbert1, Lior Pachter1,2

  • 1Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.

Bioinformatics (Oxford, England)
|January 7, 2023
PubMed
Summary
This summary is machine-generated.

gget provides command line and Python users with automated access to genomic reference databases. This tool simplifies genomic data interpretation by enabling efficient querying of large public databases like Ensembl.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomic Data Analysis

Background:

  • Interpreting genomic data requires context from reference databases.
  • Automated access to curated genomic information is crucial for researchers.
  • Existing tools may lack comprehensive programmatic access to diverse genomic databases.

Purpose of the Study:

  • To introduce gget, a novel tool for accessing genomic reference databases.
  • To provide a solution for efficient and automated genomic data interpretation.
  • To support command line and Python users in their research workflows.

Main Methods:

  • gget is developed as a free and open-source command line tool and Python package.
  • It comprises interoperable modules for specific database querying tasks.
  • The tool facilitates single-line code execution for database access.

Main Results:

  • gget enables efficient querying of genomic reference databases, including Ensembl.
  • The tool offers programmatic access to curated reference information.
  • It simplifies the process of integrating external genomic data into analyses.

Conclusions:

  • gget addresses the challenge of interpreting genomic data by providing streamlined database access.
  • The tool enhances the efficiency of genomic data analysis for command line and Python users.
  • gget promotes wider accessibility to curated genomic reference information.