Although the chemopreventive effects of aspirin have been extensively investigated, the roles of many cell components, such as long non-coding RNAs, in these effects are still not completely understood.
SUMMARY: ncPRO-seq (Non-Coding RNA PROfiling in sRNA-seq) is a stand-alone, comprehensive and flexible ncRNA analysis pipeline. It can interrogate and perform detailed profiling analysis on small RNAs derived from annotated non-coding regions in miRBase, Rfam and RepeatMasker, as well as specific regions defined by users. The ncPRO-seq pipeline performs both gene-based and family-based analyses of small RNAs. It also has a module to identify regions significantly enriched with short reads, that cannot be classified under known ncRNA families, thus enabling the discovery of previously unknown ncRNA- or siRNA-producing regions. The ncPRO-seq pipeline supports input read sequences in fastq, fasta and color space format, as well as alignment results in BAM format, meaning that small RNA raw data from the 3 current major platforms (Roche-454, Illumina-Solexa and Life technologies-SOLiD) can be analyzed with this pipeline. The ncPRO-seq pipeline can be used to analyze read and alignment data, based on any sequenced genome, including, mammals and plants. AVAILABILITY: source code, annotation files, manual and online version are available at http://ncpro.curie.fr/. CONTACT: email@example.com; firstname.lastname@example.orgSupplementary information Supplementary data are available at Bioinformatics online.
Non-coding RNAs, especially small RNAs, play important roles in many biological processes. Several small RNA types, including microRNAs (miRNAs) and small interfering RNAs (siRNAs), are well-described in rice (Oryza sativa), although much remains to be learned about their function. Many small RNAs along with their targets have been characterized with deep sequencing technologies. Some special classes of these small RNAs have been found to be unique to rice or within the larger group of grasses. The functional and biological roles of numerous plants small RNAs have been described in detail, including functions as varied as the regulation of tissue development, phase transition, or abiotic and biotic stress resistance. Mutant analysis has proven useful in the genetic identification of components involved in small RNA biogenesis and also in identification of regulatory functions of small RNAs. Although many small RNAs have been identified by deep sequencing in rice, their precise regulatory functions and cell-type specificity are in many cases still unknown.
Altered activities of long non-coding RNAs (lncRNAs) have been implicated in the regulation of microRNAs. MicroRNA-27a (miR-27a) upregulation has been shown to induce endoplasmic reticulum stress (ER stress) and podocyte injury in diabetic nephropathy (DN). Herein, we aim to interrogate the mutually regulated network of miR-27a with long intergenic non-coding RNA 1619 (LINC01619) and the target gene.
Next generation sequencing is a key technique in small RNA biology research that has led to the discovery of functionally different classes of small non-coding RNAs in the past years. However, reliable annotation of the extensive amounts of small non-coding RNA data produced by high-throughput sequencing is time-consuming and requires robust bioinformatics expertise. Moreover, existing tools have a number of shortcomings including a lack of sensitivity under certain conditions, limited number of supported species or detectable sub-classes of small RNAs.
Non-coding RNAs have been drawing increasing attention in recent years as functional data suggest that they play important roles in key cellular processes. N-BLR is a primate-specific long non-coding RNA that modulates the epithelial-to-mesenchymal transition, facilitates cell migration, and increases colorectal cancer invasion.
Current clinical guidelines emphasize the unmet need for technological innovations to guide physician decision-making and to transit from conventional care to personalized cardiovascular medicine. Biomarker-guided cardiovascular therapy represents an interesting approach to inform tailored treatment selection and monitor ongoing efficacy. However, results from previous publications cast some doubts about the clinical applicability of biomarkers to direct individualized treatment. In recent years, the non-coding human transcriptome has emerged as a new opportunity for the development of novel therapeutic strategies and biomarker discovery. Non-coding RNA (ncRNA) signatures may provide an accurate molecular fingerprint of patient phenotypes and capture levels of information that could complement traditional markers and established clinical variables. Importantly, ncRNAs have been identified in body fluids and their concentrations change with physiology and pathology, thus representing promising non-invasive biomarkers. Previous publications highlight the translational applicability of circulating ncRNAs for diagnosis and prognostic stratification within cardiology. Numerous independent studies have also evaluated the potential of the circulating non-coding transcriptome to predict and monitor response to cardiovascular treatment. However, this field has not been reviewed in detail. Here, we discuss the state-of-the-art research into circulating ncRNAs, specifically microRNAs and long non-coding RNAs, to support clinical decision-making in cardiovascular therapy. Furthermore, we summarize current methodological and conceptual limitations and propose future steps for their incorporation into personalized cardiology. Despite the lack of robust population-based studies and technical barriers, circulating ncRNAs emerge as a promising tool for biomarker-guided therapy.
Small non-coding RNAs (sncRNAs) are highly abundant molecules that regulate essential cellular processes and are classified according to sequence and structure. Here we argue that read profiles from size-selected RNA sequencing capture the post-transcriptional processing specific to each RNA family, thereby providing functional information independently of sequence and structure. We developed SeRPeNT, a new computational method that exploits reproducibility across replicates and uses dynamic time-warping and density-based clustering algorithms to identify, characterize and compare sncRNAs by harnessing the power of read profiles. We applied SeRPeNT to: (i) generate an extended human annotation with 671 new sncRNAs from known classes and 131 from new potential classes, (ii) show pervasive differential processing of sncRNAs between cell compartments and (iii) predict new molecules with miRNA-like behaviour from snoRNA, tRNA and long non-coding RNA precursors, potentially dependent on the miRNA biogenesis pathway. Furthermore, we validated experimentally four predicted novel non-coding RNAs: a miRNA, a snoRNA-derived miRNA, a processed tRNA and a new uncharacterized sncRNA. SeRPeNT facilitates fast and accurate discovery and characterization of sncRNAs at an unprecedented scale. SeRPeNT code is available under the MIT license at https://github.com/comprna/SeRPeNT.
RNAcentral is a database of non-coding RNA (ncRNA) sequences that aggregates data from specialised ncRNA resources and provides a single entry point for accessing ncRNA sequences of all ncRNA types from all organisms. Since its launch in 2014, RNAcentral has integrated twelve new resources, taking the total number of collaborating database to 22, and began importing new types of data, such as modified nucleotides from MODOMICS and PDB. We created new species-specific identifiers that refer to unique RNA sequences within a context of single species. The website has been subject to continuous improvements focusing on text and sequence similarity searches as well as genome browsing functionality. All RNAcentral data is provided for free and is available for browsing, bulk downloads, and programmatic access at http://rnacentral.org/.