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Efficient querying of genomic reference databases with gget

View ORCID ProfileLaura Luebbert, View ORCID ProfileLior Pachter
doi: https://doi.org/10.1101/2022.05.17.492392
Laura Luebbert
1Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
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  • ORCID record for Laura Luebbert
Lior Pachter
1Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
2Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California
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  • For correspondence: lpachter@caltech.edu
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Abstract

Motivation A recurring challenge in interpreting genomic data is the assessment of results in the context of existing reference databases. Currently, there is no tool implementing automated, easy programmatic access to curated reference information stored in a diverse collection of large, public genomic databases.

Results gget is a free and open-source command-line tool and Python package that enables efficient querying of genomic reference databases, such as Ensembl. gget consists of a collection of separate but interoperable modules, each designed to facilitate one type of database querying required for genomic data analysis in a single line of code.

Availability The manual and source code are available at https://github.com/pachterlab/gget.

Contact lpachter{at}caltech.edu

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • - Updated reference formatting - Updated Supplementary Figure 1

  • https://github.com/pachterlab/gget

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 27, 2022.
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Efficient querying of genomic reference databases with gget
Laura Luebbert, Lior Pachter
bioRxiv 2022.05.17.492392; doi: https://doi.org/10.1101/2022.05.17.492392
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Efficient querying of genomic reference databases with gget
Laura Luebbert, Lior Pachter
bioRxiv 2022.05.17.492392; doi: https://doi.org/10.1101/2022.05.17.492392

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