Deep sequencing of the transcripts of regulatory non-coding RNA generates footprints of post-transcriptional processes. After obtaining sequence reads, the short reads are mapped to a reference genome, and specific mapping patterns can be detected called read mapping profiles, which are distinct from random non-functional degradation patterns. These patterns reflect the maturation processes that lead to the production of shorter RNA sequences.
Next Generation Sequencing (NGS)
Non-coding (nc)RNAs are important structural and regulatory molecules. Accurate determination of the primary sequence and secondary structure of ncRNAs is important for understanding their functions. During cDNA synthesis, RNA 3' end stem-loops can self-prime reverse transcription, creating RNA-cDNA chimeras. We found that chimeric RNA-cDNA fragments can also be detected at 5' end stem-loops, although at much lower frequency.
MicroRNAs (miRNAs) present diverse regulatory functions in a wide range of biological activities. Studies on miRNA functions generally depend on determining miRNA expression profiles between libraries by using a next-generation sequencing (NGS) platform. Currently, several online web services are developed to provide small RNA NGS data analysis. However, the submission of large amounts of NGS data, conversion of data format, and limited availability of species bring problems. In this study, we developed miRSeq to provide alternatives.
While in the past decades nucleic acid analysis has been predominantly carried out using quantitative low- and high-throughput approaches such as qRT-PCR and microarray technology, next-generation sequencing (NGS) with its single base resolution is now frequently applied in DNA and RNA testing. Especially for small non-coding RNAs such as microRNAs there is a need for analysis and visualization tools that facilitate interpretation of the results also for clinicians.
miRPursuit is a pipeline developed for running end-to-end analyses of high-throughput small RNA (sRNA) sequence data in model and nonmodel plants, from raw data to identified and annotated conserved and novel sequences. It consists of a series of UNIX shell scripts, which connect open-source sRNA analysis software. The involved parameters can be combined with convenient workflow management by users without advanced computational skills.
Small RNA sequencing is the most widely used tool for microRNA (miRNA) discovery, and shows great potential for the efficient study of miRNA cross-species transport, i.e., by detecting the presence of exogenous miRNA sequences in the host species. Because of the increased appreciation of dietary miRNAs and their far-reaching implication in human health, research interests are currently growing with regard to exogenous miRNAs bioavailability, mechanisms of cross-species transport and miRNA function in cellular biological processes.
The genome-wide identification of microRNA transcription start sites (miRNA TSSs) is essential for understanding how miRNAs are regulated in development and disease. In this study, we developed mirSTP (mirna transcription Start sites Tracking Program), a probabilistic model for identifying active miRNA TSSs from nascent transcriptomes generated by global run-on sequencing (GRO-seq) and precision run-on sequencing (PRO-seq).
We present RISE (http://rise.zhanglab.net), a database of RNA Interactome from Sequencing Experiments. RNA-RNA interactions (RRIs) are essential for RNA regulation and function. RISE provides a comprehensive collection of RRIs that mainly come from recent transcriptome-wide sequencing-based experiments like PARIS, SPLASH, LIGR-seq, and MARIO, as well as targeted studies like RIA-seq, RAP-RNA and CLASH. It also includes interactions aggregated from other primary databases and publications.
detecting RNA editing associated with microRNAs, is a webserver for the identification of mature microRNA editing events using deep sequencing data. Raw microRNA sequencing reads can be provided as input, the reads are aligned against the genome and custom scripts process the data, search for potential editing sites and assess the statistical significance of the findings. The output is a text file with the location and the statistical description of all the putative editing sites detected.