We report an efficient method for detecting functional RNAs. The approach, which combines comparative sequence analysis and structure prediction, already has yielded excellent results for a small number of aligned sequences and is suitable for large-scale genomic screens.
The Vienna RNA secondary structure server provides a web interface to the most frequently used functions of the Vienna RNA software package for the analysis of RNA secondary structures. It currently offers prediction of secondary structure from a single sequence, prediction of the consensus secondary structure for a set of aligned sequences and the design of sequences that will fold into a predefined structure.
There are numerous examples of RNA-RNA complexes, including microRNA-mRNA and small RNA-mRNA duplexes for regulation of translation, guide RNA interactions with target RNA for post-transcriptional modification and small nuclear RNA duplexes for splicing. Predicting the base pairs formed between two interacting sequences remains difficult, at least in part because of the competition between unimolecular and bimolecular structure.
MicroRNAs (miRNAs) are a group of small, approximately 21 nt long, riboregulators inhibiting gene expression at a post-transcriptional level. Their most distinctive structural feature is the foldback hairpin of their precursor pre-miRNAs. Even though each pre-miRNA deposited in miRBase has its secondary structure already predicted, little is known about the patterns of structural conservation among pre-miRNAs.
MicroRNAs (miRNA) are approximately 22 nt long non-coding RNAs that are derived from larger hairpin RNA precursors and play important regulatory roles in both animals and plants. The short length of the miRNA sequences and relatively low conservation of pre-miRNA sequences restrict the conventional sequence-alignment-based methods to finding only relatively close homologs. On the other hand, it has been reported that miRNA genes are more conserved in the secondary structure rather than in primary sequences.
Four hundred and eighty-one ultraconserved sequences (UCRs) longer than 200 bases were discovered in the genomes of human, mouse and rat. These are DNA sequences showing 100% identity among the three species. UCRs are frequently located at genomic regions involved in cancer, differentially expressed in human leukemias and carcinomas and in some instances regulated by microRNAs (miRNAs). Here we present UCbase & miRfunc, the first database which provides ultraconserved sequences data and shows miRNA function.
Small non-coding RNAs (21 to 24 nucleotides) regulate a number of developmental processes in plants and animals by silencing genes using multiple mechanisms. Among these, the most conserved classes are microRNAs (miRNAs) and small interfering RNAs (siRNAs), both of which are produced by RNase III-like enzymes called Dicers. Many plant miRNAs play critical roles in nutrient homeostasis, developmental processes, abiotic stress and pathogen responses. Currently, only 70 miRNA have been identified in soybean.
Most computational methodologies for microRNA gene prediction utilize techniques based on sequence conservation and/or structural similarity. In this study we describe a new technique, which is applicable across several species, for predicting miRNA genes. This technique is based on machine learning, using the Naive Bayes classifier. It automatically generates a model from the training data, which consists of sequence and structure information of known miRNAs from a variety of species.
microRNAs (miRNA) are a class of non-protein coding functional RNAs that are thought to regulate expression of target genes by direct interaction with mRNAs. miRNAs have been identified through both experimental and computational methods in a variety of eukaryotic organisms. Though these approaches have been partially successful, there is a need to develop more tools for detection of these RNAs as they are also thought to be present in abundance in many genomes.
Dicer, an RNase III enzyme, plays a vital role in the processing of pre-miRNAs for generating the miRNAs. The structural and sequence features on pre-miRNA which can facilitate position and efficiency of cleavage are not well known. A precise cleavage by Dicer is crucial because an inaccurate processing can produce miRNA with different seed regions which can alter the repertoire of target genes.