ArticleNucleic acids research2015
Identification of protein coding regions in RNA transcripts.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 297 papers.
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Who cites it
297 citing papers in PubMed.
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- Native long-read RNA sequencing of human monocytes reveals activation-induced alternative splicing toward functional isoforms.Nature communications · 2026Article
- Long-read transcriptomics of purified human cortical cell types exposes glial isoform complexity and disease-relevant transcript architecture.bioRxiv : the preprint server for biology · 2026Article
- Novel insights into retrogene evolution and cold adaptation in the alpine plant Crucihimalaya himalaica (Brassicaceae).BMC biology · 2026Article
- Chromosome-level genome assembly of a citrus and tea insect pest, Aphis aurantii.Scientific data · 2026Article
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- A telomere-to-telomere genome assembly of Chinese water deer (Hydropotes inermis).Scientific data · 2026Article
- Chromosome-level genome assembly of Youngia japonica reveals evolutionary dynamics underlying adaptation in a synanthropic Asteraceae species.BMC plant biology · 2026Article
- A chromosome-level genome assembly of the panda loach (Yaoshania pachychilus).Scientific data · 2026Article
237 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
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.
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