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EuGene: An Automated Integrative Gene Finder for Eukaryotes and Prokaryotes.
Sallet, Erika; Gouzy, Jérôme; Schiex, Thomas.
Afiliación
  • Sallet E; Laboratoire des Interactions Plantes-Microorganismes (LIPM), Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France. Erika.Sallet@inra.fr.
  • Gouzy J; Laboratoire des Interactions Plantes-Microorganismes (LIPM), Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France.
  • Schiex T; Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT), Université de Toulouse, INRA, Castanet-Tolosan, France.
Methods Mol Biol ; 1962: 97-120, 2019.
Article en En | MEDLINE | ID: mdl-31020556
EuGene is an integrative gene finder applicable to both prokaryotic and eukaryotic genomes. EuGene annotated its first genome in 1999. Starting from genomic DNA sequences representing a complete genome, EuGene is able to predict the major transcript units in the genome from a variety of sources of information: statistical information, similarities with known transcripts and proteins, but also any GFF3 structured information supporting the presence or absence of specific types of elements. EuGene has been used to find genes in the plants Arabidopsis thaliana, Medicago truncatula, and Theobroma cacao; tomato, sunflower, and Rosa genomes; and in the nematode Meloidogyne incognita genome, among many others. The large fraction of plant in this list probably influenced EuGene development, especially in its capacities to withstand a genome with a large number of repeated regions and transposable elements.Depending on the sources of information used for prediction, EuGene can be considered as purely ab initio, purely similarity based, or hybrid. With the general availability of NGS-transcribed sequence data in genome projects, EuGene adopts a default hybrid behavior that strongly relies on similarity information. Initially targeted at eukaryotic genomes, EuGene has also been extended to offer integrative gene prediction for bacteria, allowing for richer and robust predictions than either purely statistical or homology-based prokaryotic gene finders.This text has been written as a practical guide that will give you the capacity to train and execute EuGene on your favorite eukaryotic genome. As the prokaryotic case is simpler and has already been described, only the main differences with the eukaryotic version were reported.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Células Procariotas / Programas Informáticos / Biología Computacional / Células Eucariotas Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2019 Tipo del documento: Article País de afiliación: Francia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Células Procariotas / Programas Informáticos / Biología Computacional / Células Eucariotas Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2019 Tipo del documento: Article País de afiliación: Francia
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