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Identification of protein coding regions in RNA transcripts.

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.

SUBMITTER: Tang S

PROVIDER: S-EPMC4499116 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Identification of protein coding regions in RNA transcripts.

Tang Shiyuyun S Lomsadze Alexandre A Borodovsky Mark M

Nucleic acids research 20150413 12

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 tra ...[more]

PMID: 25870408

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Project description:We used RNA sequencing to analyze transcript profiles of ten autopsy brain regions from ten subjects. RNA sequencing techniques were designed to detect both coding and non-coding RNA, splice isoform composition, and allelic expression. Brain regions were selected from five subjects with a documented history of smoking and five non-smokers. Paired-end RNA sequencing was performed on SOLiD instruments to a depth of >40 million reads, using linearly amplified, ribosomally depleted RNA. Sequencing libraries were prepared with both poly-dT and random hexamer primers to detect all RNA classes, including long non-coding (lncRNA), intronic and intergenic transcripts, and transcripts lacking poly-A tails, providing additional data not previously available. The study was designed to generate a database of the complete transcriptomes in brain region for gene network analyses and discovery of regulatory variants.Of 20,318 protein coding and 18,080 lncRNA genes annotated from GENCODE and lncipedia, 12 thousand protein coding and 2 thousand lncRNA transcripts were detectable at a conservative threshold. Of the aligned reads, 52 % were exonic, 34 % intronic and 14 % intergenic. A majority of protein coding genes (65 %) was expressed in all regions, whereas ncRNAs displayed a more restricted distribution. Profiles of RNA isoforms varied across brain regions and subjects at multiple gene loci, with neurexin 3 (NRXN3) a prominent example. Allelic RNA ratios deviating from unity were identified in?>?400 genes, detectable in both protein-coding and non-coding genes, indicating the presence of cis-acting regulatory variants. Mathematical modeling was used to identify RNAs stably expressed in all brain regions (serving as potential markers for normalizing expression levels), linked to basic cellular functions. An initial analysis of differential expression analysis between smokers and nonsmokers implicated a number of genes, several previously associated with nicotine exposure.RNA sequencing identifies distinct and consistent differences in gene expression between brain regions, with non-coding RNA displaying greater diversity between brain regions than mRNAs. Numerous RNAs exhibit robust allele selective expression, proving a means for discovery of cis-acting regulatory factors with potential clinical relevance.

Dataset Information

Identification of protein coding regions in RNA transcripts.

Publications

Identification of protein coding regions in RNA transcripts.

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