Browsing by keyword "RNA-Seq"
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Evolutionary analysis across mammals reveals distinct classes of long noncoding RNAs [preprint]BACKGROUND: Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across multitudes of species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: 1) identifying truly noncoding genes from de novo reconstructed transcriptomes, and 2) prioritizing hundreds of resulting putative lncRNAs from each sample for downstream experimental interrogation. RESULTS: We present slncky, a computational lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-Sequencing data and further prioritizes lncRNAs by characterizing selective constraint as a proxy for function. Our filtering pipeline is comparable to manual curation efforts and more sensitive than previously published approaches. Further, we develop, for the first time, a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for both sequence and transcript evolution. Our analysis reveals that selection acts in several distinct patterns, and uncovers two notable classes of lncRNAs: one showing strong purifying selection at RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript. CONCLUSION: Our novel comparative methods for lncRNAs reveals 233 constrained lncRNAs out of tens of thousands of currently annotated transcripts, which we believe should be prioritized for further interrogation. To aid in their analysis we provide the slncky Evolution Browser as a resource for experimentalists.
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Identification of Factors Involved in 18S Nonfunctional Ribosomal RNA Decay and a Method for Detecting 8-oxoguanosine by RNA-SeqThe translation of mRNA into functional proteins is essential for all life. In eukaryotes, aberrant RNAs containing sequence features that stall or severely slow down ribosomes are subject to translation-dependent quality control. Targets include mRNAs encoding a strong secondary structure (No-Go Decay; NGD) or stretches of positively-charged amino acids (Peptide-dependent Translation Arrest/Ribosome Quality Control; PDTA/RQC), mRNAs lacking an in-frame stop codon (Non-Stop Decay; NSD), or defective 18S rRNAs (18S Nonfunctional rRNA Decay; 18S NRD). Previous work from our lab showed that the S. cerevisiae NGD factors DOM34 and HBS1, and PDTA/RQC factor ASC1, all participate in the kinetics of 18S NRD. Upon further investigation of 18S NRD, our research revealed the critical role of ribosomal protein S3 (RPS3), thus adding to the emerging evidence that the ribosome senses its own translational status. While aberrant mRNAs mentioned above can occur endogenously, damaging agents, such as oxidative stress or UV irradiation, can negatively affect the chemical integrity of RNA. Such lesions could lead to translation errors and ribosome stalling. However, current tools to monitor the fate of damaged RNA are quite limited and only provide a low-resolution picture. Therefore, we sought to develop a deep-sequencing method to detect damaged RNA, taking advantage of reverse transcriptase's ability to insert a mutation across a damaged site. Using oxidized RNA as a model damaged RNA, our preliminary data showed increased G>T mutations in oxidized RNA. This method provides the foundation for future work aimed at understanding how cells deal with damaged RNA.

