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Related Experiment Videos

Fast parsers for Entrez Gene.

Mingyi Liu1, Andrei Grigoriev

  • 1GPC Biotech AG Fraunhoferstrasse 20, 82152 Martinsried, Germany.

Bioinformatics (Oxford, England)
|May 10, 2005
PubMed
Summary

Bioinformaticians can now efficiently parse Entrez Gene data with new Perl tools. These high-performance parsers address the urgent need for Entrez Gene (NCBI

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • NCBI transitioned its primary genome annotation database from LocusLink to Entrez Gene in 2005.
  • A scarcity of functional parsers for the Entrez Gene annotation file existed post-transition.

Purpose of the Study:

  • To develop and compare high-performance Perl parsers for the Entrez Gene annotation file.
  • To provide the bioinformatics community with efficient tools for accessing Entrez Gene data.

Main Methods:

  • Development of four distinct Perl parsers utilizing various parsing techniques: Parse::RecDescent, Parse::Yapp, Perl-byacc, and Perl 5 regular expressions.
  • Comparative analysis of the performance and efficiency of the developed parsers.

Main Results:

  • Successful development of four Perl parsers for Entrez Gene annotation data.
  • Identification of the fastest parser capable of processing the entire human Entrez Gene annotation file in under 12 minutes on a single CPU.
  • Demonstration of the practical utility of these parsers for the bioinformatics community.

Conclusions:

  • The developed Perl parsers offer a high-performance solution for accessing Entrez Gene data.
  • These tools facilitate bioinformatics research and data analysis during and after the LocusLink to Entrez Gene transition.

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