Dynamic expression of 3 ' UTRs revealed by Poisson hidden Markov modeling of RNA-Seq: Implications in gene expression profiling
ABSTRACT RNA sequencing (RNA-Seq) allows for the identification of novel exon-exon junctions and quantification of gene expression levels. We show that from RNA-Seq data one may also detect utilization of alternative polyadenylation (APA) in 3' untranslated regions (3'UTRs) known to play a critical role in the regulation of mRNA stability, cellular localization and translation efficiency. Given the dynamic nature of APA, it is desirable to examine the APA on a sample by sample basis. We used a Poisson hidden Markov model (PHMM) of RNA-Seq data to identify potential APA in human liver and brain cortex tissues leading to shortened 3' UTRs. Over three hundred transcripts with shortened 3'UTRs were detected with sensitivity >75% and specificity >60%. Tissue-specific 3'UTR shortening was observed for 32 genes with a q-value≤0.1. When compared to alternative isoforms detected by Cufflinks or MISO, our PHMM method agreed on over 100 transcripts with shortened 3' UTRs. Given the increasing usage of RNA-Seq for gene expression profiling, using PHMM to investigate sample-specific 3'UTR shortening could be an added benefit from this emerging technology.
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ABSTRACT: MicroRNAs (miRNAs) are a type of small non-coding RNA that negatively regulate gene expression levels by binding to the 3'-untranslated region of specific target mRNAs. To investigate the role of miR-27a in esophageal squamous cell carcinoma (ESCC), TargetScan software was used to predict the target gene of miR-27a. Kirsten rat sarcoma viral oncogene homolog (KRAS), which has been implicated as a regulator of cell proliferation, differentiation and transformation, was identified as a potential target gene of miR-27a and, thus, was the focus of the present study. Luciferase activity in cells transfected with miR-27a mimics was 48% lower when compared with that of the miRNA-negative control. Furthermore, expression levels of the K-ras protein were reduced by ≤50% in cells cotransfected with an expression vector containing miR-27a and miR-27a binding sequences, when compared with the control. The expression level of miR-27a was significantly lower in ESCC cell lines and tissues when compared with healthy esophageal epithelial cells and tissues. However, the expression level of the target gene, KRAS was upregulated and ESCC cell proliferation was significantly inhibited following miR-27a mimic or small interfering K-ras transfection. In conclusion, the present study demonstrated that the expression level of miR-27a was low in ESCC and that miR-27a directly targets the KRAS gene, resulting in inhibited cell proliferation in esophageal cancer.Oncology letters 01/2015; 9(1):471-477. DOI:10.3892/ol.2014.2701 · 0.99 Impact Factor
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ABSTRACT: The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R2≈0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.Nature Biotechnology 08/2014; DOI:10.1038/nbt.3001 · 39.08 Impact Factor