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Light Acquisition02:16

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Related Experiment Video

Updated: Jun 13, 2026

Scalable Transfection of Maize Mesophyll Protoplasts
08:38

Scalable Transfection of Maize Mesophyll Protoplasts

Published on: June 23, 2023

EuGène-maize: a web site for maize gene prediction.

Pierre Montalent1, Johann Joets

  • 1INRA, UMR 0320 / UMR 8120 Génétique Végétale, Gif-sur-Yvette, France.

Bioinformatics (Oxford, England)
|April 20, 2010
PubMed
Summary
This summary is machine-generated.

A new gene predictor tool, EuGène, has been trained for maize genome sequences. This tool integrates multiple evidence sources for accurate gene model prediction, addressing a gap in current bioinformatics resources for maize.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Plant Science

Background:

  • The maize B73 genome sequence is largely available, with new technologies enabling sequencing of diverse maize genotypes.
  • Accurate gene content prediction is crucial for interpreting genomic data from these sequences.
  • Currently, no publicly accessible gene predictor is specifically trained for maize.

Purpose of the Study:

  • To develop and train a gene predictor specifically for maize genome sequences.
  • To address the need for a specialized tool to analyze maize genetic information.

Main Methods:

  • Training the EuGène software, a gene prediction tool.
  • Utilizing multiple sources of evidence to build consolidated gene models.
  • Focusing on maize sequences for tailored prediction accuracy.

Main Results:

  • Successful training of the EuGène software for maize gene prediction.
  • Development of a consolidated gene model prediction system.
  • Availability of a new resource for maize genomic analysis.

Conclusions:

  • The trained EuGène tool provides a valuable solution for maize gene content prediction.
  • This advancement facilitates the interpretation of genomic data from various maize varieties.
  • The tool enhances the utility of emerging sequencing technologies for maize research.