Dataset Information

Trypanosome RNA editing: the complexity of getting U in and taking U out.

ABSTRACT: RNA editing, which adds sequence information to RNAs post-transcriptionally, is a widespread phenomenon throughout eukaryotes. The most complex form of this process is the uridine (U) insertion/deletion editing that occurs in the mitochondria of kinetoplastid protists. RNA editing in these flagellates is specified by trans-acting guide RNAs and entails the insertion of hundreds and deletion of dozens of U residues from mitochondrial RNAs to produce mature, translatable mRNAs. An emerging model indicates that the machinery required for trypanosome RNA editing is much more complicated than previously appreciated. A family of RNA editing core complexes (RECCs), which contain the required enzymes and several structural proteins, catalyze cycles of U insertion and deletion. A second, dynamic multiprotein complex, the Mitochondrial RNA Binding 1 (MRB1) complex, has recently come to light as another essential component of the trypanosome RNA editing machinery. MRB1 likely serves as the platform for kinetoplastid RNA editing, and plays critical roles in RNA utilization and editing processivity. MRB1 also appears to act as a hub for coordination of RNA editing with additional mitochondrial RNA processing events. This review highlights the current knowledge regarding the complex molecular machinery involved in trypanosome RNA editing. WIREs RNA 2016, 7:33-51. doi: 10.1002/wrna.1313 For further resources related to this article, please visit the WIREs website.

SUBMITTER: Read LK

PROVIDER: S-EPMC4835692 | biostudies-literature | 2016 Jan-Feb

REPOSITORIES: biostudies-literature

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Trypanosome RNA editing: the complexity of getting U in and taking U out.

Read Laurie K LK Lukeš Julius J Hashimi Hassan H

Wiley interdisciplinary reviews. RNA 20151102 1

RNA editing, which adds sequence information to RNAs post-transcriptionally, is a widespread phenomenon throughout eukaryotes. The most complex form of this process is the uridine (U) insertion/deletion editing that occurs in the mitochondria of kinetoplastid protists. RNA editing in these flagellates is specified by trans-acting guide RNAs and entails the insertion of hundreds and deletion of dozens of U residues from mitochondrial RNAs to produce mature, translatable mRNAs. An emerging model i ...[more]

PMID: 26522170

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Project description:Background. A common research goal in transcriptome projects is to find genes that are differentially expressed in different phenotype classes. Biologists might wish to validate such gene candidates experimentally, or use them for downstream systems biology analysis. Producing a coherent differential gene expression analysis from RNA-seq count data requires an understanding of how numerous sources of variation such as the replicate size, the hypothesized biological effect size, and the specific method for making differential expression calls interact. We believe an explicit demonstration of such interactions in real RNA-seq data sets is of practical interest to biologists. Results. Using two large public RNA-seq data sets-one representing strong, and another mild, biological effect size-we simulated different replicate size scenarios, and tested the performance of several commonly-used methods for calling differentially expressed genes in each of them. We found that, when biological effect size was mild, RNA-seq experiments should focus on experimental validation of differentially expressed gene candidates. Importantly, at least triplicates must be used, and the differentially expressed genes should be called using methods with high positive predictive value (PPV), such as NOISeq or GFOLD. In contrast, when biological effect size was strong, differentially expressed genes mined from unreplicated experiments using NOISeq, ASC and GFOLD had between 30 to 50% mean PPV, an increase of more than 30-fold compared to the cases of mild biological effect size. Among methods with good PPV performance, having triplicates or more substantially improved mean PPV to over 90% for GFOLD, 60% for DESeq2, 50% for NOISeq, and 30% for edgeR. At a replicate size of six, we found DESeq2 and edgeR to be reasonable methods for calling differentially expressed genes at systems level analysis, as their PPV and sensitivity trade-off were superior to the other methods'. Conclusion. When biological effect size is weak, systems level investigation is not possible using RNAseq data, and no meaningful result can be obtained in unreplicated experiments. Nonetheless, NOISeq or GFOLD may yield limited numbers of gene candidates with good validation potential, when triplicates or more are available. When biological effect size is strong, NOISeq and GFOLD are effective tools for detecting differentially expressed genes in unreplicated RNA-seq experiments for qPCR validation. When triplicates or more are available, GFOLD is a sharp tool for identifying high confidence differentially expressed genes for targeted qPCR validation; for downstream systems level analysis, combined results from DESeq2 and edgeR are useful.

Dataset Information

Trypanosome RNA editing: the complexity of getting U in and taking U out.

Publications

Trypanosome RNA editing: the complexity of getting U in and taking U out.

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