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BISQUE: locus- and variant-specific conversion of genomic, transcriptomic and proteomic database identifiers.

Michael J Meyer1, Philip Geske2, Haiyuan Yu2

  • 1Department of Biological Statistics and Computational Biology, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA and Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA.

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Summary

BISQUE is a new tool that converts molecular identifiers between biological databases. It simplifies analysis by handling associated loci and variants, improving data integration for researchers.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biological sequence databases are crucial for molecular characterization and data sharing.
  • Scientists face challenges with disparate molecular identifiers from various databases.
  • Analyzing associated loci and variants can be cumbersome with non-standardized data.

Purpose of the Study:

  • To present BISQUE, a tool for converting molecular identifiers, loci, and variants between database conventions.
  • To provide a flexible solution for downstream biological data analysis.
  • To facilitate seamless data integration across different biological databases.

Main Methods:

  • Developed BISQUE as a web server and command-line tool.
  • Utilized a graph traversal algorithm for generalized conversion.
  • Supported conversion for human genome residues, genes, transcripts, and proteins.

Main Results:

  • BISQUE enables conversion of molecular identifiers, loci, and variants.
  • The tool supports conversion across different molecule classes and in all directions.
  • An intuitive web interface and URL-based web service are provided.

Conclusions:

  • BISQUE simplifies the conversion of molecular data, enhancing biological data analysis.
  • The tool promotes interoperability between diverse biological sequence databases.
  • BISQUE offers a generalized approach to managing molecular identifiers and associated data.