Dynamic expression of 3′ UTRs revealed by Poisson hidden Markov modeling of RNA-Seq: Implications in gene expression profiling

Lü, Jun; Bushel, Pierre R.

doi:10.1016/j.gene.2013.06.052

Cited by 21 publications

(16 citation statements)

References 50 publications

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“…We first used Poisson hidden Markov model 34 to detect 345 and 381 transcripts with shortened 3’ UTRs (Fisher’s exact test q-value ≤ 0.01) when compared to control (Supplementary Table 10). There are 278 transcripts in common, which enrich for Spliceosome and Ubiquitin mediated proteolysis KEGG pathways.…”

Section: Resultsmentioning

confidence: 99%

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The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

et al. 2014

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RNA-seq facilitates unbiased genome-wide gene-expression profiling. However, its concordance with the well-established microarray platform must be rigorously assessed for confident uses in clinical and regulatory application. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same set of liver samples of rats under varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOA). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is highly correlated with treatment effect size, gene-expression abundance and the biological complexity of the MOA. RNA-seq outperforms microarray (90% versus 76%) in DEG verification by quantitative PCR and the main gain is its improved accuracy for low expressed genes. Nonetheless, predictive classifiers derived from both platforms performed similarly. Therefore, the endpoint studied and its biological complexity, transcript abundance, and intended application are important factors in transcriptomic research and for decision-making.

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Section: Resultsmentioning

confidence: 99%

“…RNA-seq read alignments by TopHat (pipeline P4) were analyzed by a Poisson hidden markov model (PHMM) to detect 3’ UTR shortening 34 . Briefly, all the terminal exons located within the 3’ UTR region of the RefSeq genes were collected.…”

Section: Methodsmentioning

confidence: 99%

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

et al. 2014

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“…miRNAs are key in the regulation of cell proliferation, apoptosis and other important cellular processes. The role of miRNA in each specific cell line is dependent on the specific target gene of the miRNA ( 15 , 24 – 26 ). Thus, a single miRNA may exhibit an opposite role in a different cell line.…”

Section: Discussionmentioning

confidence: 99%

MicroRNA-27a directly targets KRAS to inhibit cell proliferation in esophageal squamous cell carcinoma

2014

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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.

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“…Due to the low computational complexity of the algorithm, the method is applicable to processing very large datasets but the software does not attempt a quantification of relative expression of the corresponding isoforms. Lu and Bushel's PHMM method employs Hidden Markov Models (HMMs) to treat alternative poly‐adenylation events as transitions between two distinct (and hidden) states, a “high expression” state corresponding to the part of the UTR that is contained in both short and long isoforms and a “low expression” state corresponding to part covered only by the longer isoform. Transcripts for which a two‐state model has a better fit than a one‐state model are selected and the method looks for transitions in the Markov chain from high to low expression states in the sequences.…”

Section: Predicting Alternative Tss and Pas From Transcriptomic Sumentioning

confidence: 99%

Untranslated Parts of Genes Interpreted: Making Heads or Tails of High‐Throughput Transcriptomic Data via Computational Methods

Szkop

Nobeli

2017

BioEssays

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In this review we highlight the importance of defining the untranslated parts of transcripts, and present a number of computational approaches for the discovery and quantification of alternative transcription start and poly-adenylation events in high-throughput transcriptomic data. The fate of eukaryotic transcripts is closely linked to their untranslated regions, which are determined by the position at which transcription starts and ends at a genomic locus. Although the extent of alternative transcription starts and alternative poly-adenylation sites has been revealed by sequencing methods focused on the ends of transcripts, the application of these methods is not yet widely adopted by the community. We suggest that computational methods applied to standard high-throughput technologies are a useful, albeit less accurate, alternative to the expertise-demanding 5' and 3' sequencing and they are the only option for analysing legacy transcriptomic data. We review these methods here, focusing on technical challenges and arguing for the need to include better normalization of the data and more appropriate statistical models of the expected variation in the signal.

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Dynamic expression of 3′ UTRs revealed by Poisson hidden Markov modeling of RNA-Seq: Implications in gene expression profiling

Cited by 21 publications

References 50 publications

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

MicroRNA-27a directly targets KRAS to inhibit cell proliferation in esophageal squamous cell carcinoma

Untranslated Parts of Genes Interpreted: Making Heads or Tails of High‐Throughput Transcriptomic Data via Computational Methods

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