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GeneMark-ETP significantly improves the accuracy of automatic annotation of large eukaryotic genomes.
Bruna, Tomás; Lomsadze, Alexandre; Borodovsky, Mark.
Affiliation
  • Bruna T; School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
  • Lomsadze A; Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
  • Borodovsky M; School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA; borodovsky@gatech.edu.
Genome Res ; 34(5): 757-768, 2024 06 25.
Article in En | MEDLINE | ID: mdl-38866548
ABSTRACT
Large-scale genomic initiatives, such as the Earth BioGenome Project, require efficient methods for eukaryotic genome annotation. Here we present an automatic gene finder, GeneMark-ETP, integrating genomic-, transcriptomic-, and protein-derived evidence that has been developed with a focus on large plant and animal genomes. GeneMark-ETP first identifies genomic loci where extrinsic data are sufficient for making gene predictions with "high confidence." The genes situated in the genomic space between the high-confidence genes are predicted in the next stage. The set of high-confidence genes serves as an initial training set for the statistical model. Further on, the model parameters are iteratively updated in the rounds of gene prediction and parameter re-estimation. Upon reaching convergence, GeneMark-ETP makes the final predictions and delivers the whole complement of predicted genes. GeneMark-ETP outperforms gene finders using a single type of extrinsic evidence. Comparisons with gene finders MAKER2 and TSEBRA, those that use both transcript- and protein-derived extrinsic evidence, show that GeneMark-ETP delivers state-of-the-art gene-prediction accuracy, with the margin of outperforming existing approaches increasing in its application to larger and more complex eukaryotic genomes.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Molecular Sequence Annotation Limits: Animals Language: En Journal: Genome Res Journal subject: BIOLOGIA MOLECULAR / GENETICA Year: 2024 Document type: Article Affiliation country: Estados Unidos Country of publication: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Molecular Sequence Annotation Limits: Animals Language: En Journal: Genome Res Journal subject: BIOLOGIA MOLECULAR / GENETICA Year: 2024 Document type: Article Affiliation country: Estados Unidos Country of publication: Estados Unidos