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A method for detecting long non-coding RNAs with tiled RNA expression microarrays.

ABSTRACT: Long non-coding ribonucleic acids (lncRNAs) have been proposed as biomarkers in prostate cancer. This paper proposes a selection method which uses data from tiled microarrays to identify relatively long regions of moderate expression independent of the microarray platform and probe design. The method is used to search for candidate long non-coding ribonucleic acids (lncRNAs) at locus 8q24 and is run on three independent experiments which all use samples from prostate cancer patients. The robustness of the method is tested by utilizing repeated copies of tiled probes. The method shows high consistency between experiments that used the same samples, but different probe layout. There also is statistically significant consistency when comparing experiments with different samples. The method selected the long non-coding ribonucleic acid PCNCR1 in all three experiments.

SUBMITTER: Lund SH

PROVIDER: S-EPMC4061049 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A method for detecting long non-coding RNAs with tiled RNA expression microarrays.

Lund Sigrun Helga SH Gudbjartsson Daniel Fannar DF Rafnar Thorunn T Sigurdsson Asgeir A Gudjonsson Sigurjon Axel SA Gudmundsson Julius J Stefansson Kari K Stefansson Gunnar G

PloS one 20140617 6

Long non-coding ribonucleic acids (lncRNAs) have been proposed as biomarkers in prostate cancer. This paper proposes a selection method which uses data from tiled microarrays to identify relatively long regions of moderate expression independent of the microarray platform and probe design. The method is used to search for candidate long non-coding ribonucleic acids (lncRNAs) at locus 8q24 and is run on three independent experiments which all use samples from prostate cancer patients. The robustn ...[more]

PMID: 24937006

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Project description:Interferons (IFNs) are key players in the antiviral response. IFN sensing by the cell activates transcription of IFN-stimulated genes (ISGs) able to induce an antiviral state by affecting viral replication and release. IFN also induces the expression of ISGs that function as negative regulators to limit the strength and duration of IFN response. The ISGs identified so far belong to coding genes. However, only a small proportion of the transcriptome corresponds to coding transcripts and it has been estimated that there could be as many coding as long non-coding RNAs (lncRNAs). To address whether IFN can also regulate the expression of lncRNAs, we analyzed the transcriptome of HuH7 cells treated or not with IFN?2 by expression arrays. Analysis of the arrays showed increased levels of several well-characterized coding genes that respond to IFN both at early or late times. Furthermore, we identified several IFN-stimulated or -downregulated lncRNAs (ISRs and IDRs). Further validation showed that ISR2, 8, and 12 expression mimics that of their neighboring genes GBP1, IRF1, and IL6, respectively, all related to the IFN response. These genes are induced in response to different doses of IFN?2 in different cell lines at early (ISR2 or 8) or later (ISR12) time points. IFN? also induced the expression of these lncRNAs. ISR2 and 8 were also induced by an influenza virus unable to block the IFN response but not by other wild-type lytic viruses tested. Surprisingly, both ISR2 and 8 were significantly upregulated in cultured cells and livers from patients infected with HCV. Increased levels of ISR2 were also detected in patients chronically infected with HIV. This is relevant as genome-wide guilt-by-association studies predict that ISR2, 8, and 12 may function in viral processes, in the IFN pathway and the antiviral response. Therefore, we propose that these lncRNAs could be induced by IFN to function as positive or negative regulators of the antiviral response.

Project description:BackgroundIn recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed when analyzing this data is distinguishing between long non-coding RNAs (lncRNAs) and protein coding transcripts (PCTs). Thus, we present a Support Vector Machine (SVM) based method to distinguish lncRNAs from PCTs, using features based on frequencies of nucleotide patterns and ORF lengths, in transcripts.MethodsThe proposed method is based on SVM and uses the first ORF relative length and frequencies of nucleotide patterns selected by PCA as features. FASTA files were used as input to calculate all possible features. These features were divided in two sets: (i) 336 frequencies of nucleotide patterns; and (ii) 4 features derived from ORFs. PCA were applied to the first set to identify 6 groups of frequencies that could most contribute to the distinction. Twenty-four experiments using the 6 groups from the first set and the features from the second set where built to create the best model to distinguish lncRNAs from PCTs.ResultsThis method was trained and tested with human (Homo sapiens), mouse (Mus musculus) and zebrafish (Danio rerio) data, achieving 98.21%, 98.03% and 96.09%, accuracy, respectively. Our method was compared to other tools available in the literature (CPAT, CPC, iSeeRNA, lncRNApred, lncRScan-SVM and FEELnc), and showed an improvement in accuracy by ≈3.00%. In addition, to validate our model, the mouse data was classified with the human model, and vice-versa, achieving ≈97.80% accuracy in both cases, showing that the model is not overfit. The SVM models were validated with data from rat (Rattus norvegicus), pig (Sus scrofa) and fruit fly (Drosophila melanogaster), and obtained more than 84.00% accuracy in all these organisms. Our results also showed that 81.2% of human pseudogenes and 91.7% of mouse pseudogenes were classified as non-coding. Moreover, our method was capable of re-annotating two uncharacterized sequences of Swiss-Prot database with high probability of being lncRNAs. Finally, in order to use the method to annotate transcripts derived from RNA-seq, previously identified lncRNAs of human, gorilla (Gorilla gorilla) and rhesus macaque (Macaca mulatta) were analyzed, having successfully classified 98.62%, 80.8% and 91.9%, respectively.ConclusionsThe SVM method proposed in this work presents high performance to distinguish lncRNAs from PCTs, as shown in the results. To build the model, besides using features known in the literature regarding ORFs, we used PCA to identify features among nucleotide pattern frequencies that contribute the most in distinguishing lncRNAs from PCTs, in reference data sets. Interestingly, models created with two evolutionary distant species could distinguish lncRNAs of even more distant species.

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A method for detecting long non-coding RNAs with tiled RNA expression microarrays.

Publications

A method for detecting long non-coding RNAs with tiled RNA expression microarrays.

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