GeneMark-EP+: eukaryotic gene prediction with self-training in the space of genes and proteins.


Journal

NAR genomics and bioinformatics
ISSN: 2631-9268
Titre abrégé: NAR Genom Bioinform
Pays: England
ID NLM: 101756213

Informations de publication

Date de publication:
Jun 2020
Historique:
received: 13 12 2019
revised: 10 03 2020
accepted: 12 05 2020
entrez: 23 5 2020
pubmed: 23 5 2020
medline: 23 5 2020
Statut: ppublish

Résumé

We have made several steps toward creating a fast and accurate algorithm for gene prediction in eukaryotic genomes. First, we introduced an automated method for efficient

Identifiants

pubmed: 32440658
doi: 10.1093/nargab/lqaa026
pii: lqaa026
pmc: PMC7222226
doi:

Types de publication

Journal Article

Langues

eng

Pagination

lqaa026

Subventions

Organisme : NIGMS NIH HHS
ID : R01 GM128145
Pays : United States

Informations de copyright

© The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

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Auteurs

Tomáš Brůna (T)

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Alexandre Lomsadze (A)

Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Mark Borodovsky (M)

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Classifications MeSH