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Nucleic Acids Res. 2015 Jul 13;43(12):e78. doi: 10.1093/nar/gkv227. Epub 2015 Apr 13.

Identification of protein coding regions in RNA transcripts.

Author information

1
School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA.
2
Joint Georgia Tech and Emory Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
3
Joint Georgia Tech and Emory 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 Center for Bioinformatics and Computational Genomics, Georgia Institute of Technology, Atlanta, GA 30332, USA Department of Biological and Medical Physics, Moscow Institute of Physics and Technology, Moscow, Russia borodovsky@gatech.edu.

Abstract

Massive parallel sequencing of RNA transcripts by next-generation technology (RNA-Seq) generates critically important data for eukaryotic gene discovery. Gene finding in transcripts can be done by statistical (alignment-free) as well as by alignment-based methods. We describe a new tool, GeneMarkS-T, for ab initio identification of protein-coding regions in RNA transcripts. The algorithm parameters are estimated by unsupervised training which makes unnecessary manually curated preparation of training sets. We demonstrate that (i) the unsupervised training is robust with respect to the presence of transcripts assembly errors and (ii) the accuracy of GeneMarkS-T in identifying protein-coding regions and, particularly, in predicting translation initiation sites in modelled as well as in assembled transcripts compares favourably to other existing methods.

PMID:
25870408
PMCID:
PMC4499116
DOI:
10.1093/nar/gkv227
[Indexed for MEDLINE]
Free PMC Article

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