Identification of protein coding regions in RNA transcripts

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...

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Bibliographic Details
Published in:Nucleic acids research Vol. 43; no. 12; p. e78
Main Authors: Tang, Shiyuyun, Lomsadze, Alexandre, Borodovsky, Mark
Format: Journal Article
Language:English
Published: England Oxford University Press 13.07.2015
Subjects:
Algorithms
Animals
Arabidopsis - genetics
Drosophila melanogaster - genetics
Gene Expression Profiling
Genes
High-Throughput Nucleotide Sequencing - methods
Methods Online
Mice
Open Reading Frames
Peptide Chain Initiation, Translational
RNA, Messenger - chemistry
Schizosaccharomyces - genetics
Sequence Analysis, RNA - methods
Software
ISSN:0305-1048, 1362-4962, 1362-4962
Online Access:Get full text
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Summary: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.
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ISSN:0305-1048
1362-4962
1362-4962
DOI:10.1093/nar/gkv227