Transposon-derived RNAs Research Articles

Non-Coding RNAs in Response to Drought Stress.

Drought stress causes changes in the morphological, physiological, biochemical and molecular characteristics of plants. The response to drought in different plants may vary from avoidance, tolerance and escape to recovery from stress. This response is genetically programmed and regulated in a very complex yet synchronized manner. The crucial genetic regulations mediated by non-coding RNAs (ncRNAs) have emerged as game-changers in modulating the plant responses to drought and other abiotic stresses. The ncRNAs interact with their targets to form potentially subtle regulatory networks that control multiple genes to determine the overall response of plants. Many long and small drought-responsive ncRNAs have been identified and characterized in different plant varieties. The miRNA-based research is better documented, while lncRNA and transposon-derived RNAs are relatively new, and their cellular role is beginning to be understood. In this review, we have compiled the information on the categorization of non-coding RNAs based on their biogenesis and function. We also discuss the available literature on the role of long and small non-coding RNAs in mitigating drought stress in plants.

PiPipes: a set of pipelines for piRNA and transposon analysis via small RNA-seq, RNA-seq, degradome- and CAGE-seq, ChIP-seq and genomic DNA sequencing.

Motivation: PIWI-interacting RNAs (piRNAs), 23–36 nt small silencing RNAs, repress transposon expression in the metazoan germ line, thereby protecting the genome. Although high-throughput sequencing has made it possible to examine the genome and transcriptome at unprecedented resolution, extracting useful information from gigabytes of sequencing data still requires substantial computational skills. Additionally, researchers may analyze and interpret the same data differently, generating results that are difficult to reconcile. To address these issues, we developed a coordinated set of pipelines, ‘piPipes’, to analyze piRNA and transposon-derived RNAs from a variety of high-throughput sequencing libraries, including small RNA, RNA, degradome or 7-methyl guanosine cap analysis of gene expression (CAGE), chromatin immunoprecipitation (ChIP) and genomic DNA-seq. piPipes can also produce figures and tables suitable for publication. By facilitating data analysis, piPipes provides an opportunity to standardize computational methods in the piRNA field.Supplementary information: Supplementary information, including flowcharts and example figures for each pipeline, are available at Bioinformatics online.Availability and implementation: piPipes is implemented in Bash, C++, Python, Perl and R. piPipes is free, open-source software distributed under the GPLv3 license and is available at http://bowhan.github.io/piPipes/.Contact: Phillip.Zamore@umassmed.edu or Zhiping.Weng@umassmed.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Using machine learning and high-throughput RNA sequencing to classify the precursors of small non-coding RNAs

Recent advances in high-throughput sequencing allow researchers to examine the transcriptome in more detail than ever before. Using a method known as high-throughput small RNA-sequencing, we can now profile the expression of small regulatory RNAs such as microRNAs and small interfering RNAs (siRNAs) with a great deal of sensitivity. However, there are many other types of small RNAs (<50nt) present in the cell, including fragments derived from snoRNAs (small nucleolar RNAs), snRNAs (small nuclear RNAs), scRNAs (small cytoplasmic RNAs), tRNAs (transfer RNAs), and transposon-derived RNAs. Here, we present a user’s guide for CoRAL (Classification of RNAs by Analysis of Length), a computational method for discriminating between different classes of RNA using high-throughput small RNA-sequencing data. Not only can CoRAL distinguish between RNA classes with high accuracy, but it also uses features that are relevant to small RNA biogenesis pathways. By doing so, CoRAL can give biologists a glimpse into the characteristics of different RNA processing pathways and how these might differ between tissue types, biological conditions, or even different species. CoRAL is available at http://wanglab.pcbi.upenn.edu/coral/.

CoRAL: predicting non-coding RNAs from small RNA-sequencing data

The surprising observation that virtually the entire human genome is transcribed means we know little about the function of many emerging classes of RNAs, except their astounding diversities. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their abilities to classify the various collections of non-coding RNAs (ncRNAs). To address this, we developed Classification of RNAs by Analysis of Length (CoRAL), a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length and cleavage specificity to distinguish between different ncRNA populations. We evaluated CoRAL using genome-wide small RNA sequencing data sets from four human tissue types and were able to classify six different types of RNAs with ∼80% cross-validation accuracy. Analysis by CoRAL revealed that microRNAs, small nucleolar and transposon-derived RNAs are highly discernible and consistent across all human tissue types assessed, whereas long intergenic ncRNAs, small cytoplasmic RNAs and small nuclear RNAs show less consistent patterns. The ability to reliably annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using small RNA sequencing data in less well-characterized organisms.

Drosophila suppressor of sable protein [Su(s)] promotes degradation of aberrant and transposon-derived RNAs.

RNA-binding proteins act at various stages of gene expression to regulate and fine-tune patterns of mRNA accumulation. One protein in this class is Drosophila Su(s), a nuclear protein that has been previously shown to inhibit the accumulation of mutant transcripts by an unknown mechanism. Here, we have identified several additional RNAs that are downregulated by Su(s). These Su(s) targets include cryptic wild-type transcripts from the developmentally regulated Sgs4 and ng1 genes, noncoding RNAs derived from tandemly repeated alphabeta/alphagamma elements within an Hsp70 locus, and aberrant transcripts induced by Hsp70 promoter transgenes inserted at ectopic sites. We used the alphabeta RNAs to investigate the mechanism of Su(s) function and obtained evidence that these transcripts are degraded by the nuclear exosome and that Su(s) promotes this process. Furthermore, we showed that the RNA binding domains of Su(s) are important for this effect and mapped the sequences involved to a 267-nucleotide region of an alphabeta element. Taken together, these results suggest that Su(s) binds to certain nascent transcripts and stimulates their degradation by the nuclear exosome.

Drosophila Suppressor of Sable Protein [Su(s)] Promotes Degradation of Aberrant and Transposon-Derived RNAs

Drosophila Suppressor of Sable Protein [Su(s)] Promotes Degradation of Aberrant and Transposon-Derived RNAs