MicroRNAs (miRNAs) are small noncoding regulatory RNAs that reduce stability and/or translation of fully or partially sequence-complementary target mRNAs. In order to identify miRNAs and to assess their expression patterns, we sequenced over 250 small RNA libraries from 26 different organ systems and cell types of human and rodents that were enriched in neuronal as well as normal and malignant hematopoietic cells and tissues. We present expression profiles derived from clone count data and provide computational tools for their analysis.
There are abundance of transcripts that code for no particular protein and that remain functionally uncharacterized. Some of these transcripts may have novel functions while others might be junk transcripts. Unfortunately, the experimental validation of such transcripts to find functional non-coding RNA candidates is very costly. Therefore, our primary interest is to computationally mine candidate functional transcripts from a pool of uncharacterized transcripts.
MicroRNAs (miRs) have been broadly implicated in animal development and disease. We developed a novel computational strategy for the systematic, whole-genome identification of miRs from high throughput sequencing information. This method, miRTRAP, incorporates the mechanisms of miR biogenesis and includes additional criteria regarding the prevalence and quality of small RNAs arising from the antisense strand and neighboring loci.
Regulation of post-transcriptional gene expression by microRNAs (miRNA) has so far been validated for only a few mRNA targets. Based on the large number of miRNA genes and the possibility that one miRNA might influence gene expression of several targets simultaneously, the quantity of ribo-regulated genes is expected to be much higher. Here, we describe the web tool MicroInspector that will analyse a user-defined RNA sequence, which is typically an mRNA or a part of an mRNA, for the occurrence of binding sites for known and registered miRNAs.
MicroRNAs (miRNAs) play important roles in gene expression regulation in animals and plants. Since plant miRNAs recognize their target mRNAs by near-perfect base pairing, computational sequence similarity search can be used to identify potential targets. A web-based integrated computing system, miRU, has been developed for plant miRNA target gene prediction in any plant, if a large number of sequences are available.
MicroRNAs (miRNAs) in eukaryotes guide post-transcriptional regulation by means of targeted RNA degradation and translational arrest. They are released by a Dicer nuclease as a 21-24-nucleotide RNA duplex from a precursor in which an imperfectly matched inverted repeat forms a partly double-stranded region. One of the two strands is then recruited by an Argonaute nuclease that is the effector protein of the silencing mechanism. Short interfering RNAs (siRNAs), which are similar to miRNAs, are also produced by Dicer but the precursors are perfectly double-stranded RNA.
Recently, genome-wide surveys for non-coding RNAs have provided evidence for tens of thousands of previously undescribed evolutionary conserved RNAs with distinctive secondary structures. The annotation of these putative ncRNAs, however, remains a difficult problem. Here we describe an SVM-based approach that, in conjunction with a non-stringent filter for consensus secondary structures, is capable of efficiently recognizing microRNA precursors in multiple sequence alignments.
Short interfering RNAs are used in functional genomics studies to knockdown a single gene in a reversible manner. The results of siRNA experiments are highly dependent on the choice of siRNA sequence. In order to evaluate siRNA design rules, we collected a database of 398 siRNAs of known efficacy from 92 genes. We used this database to evaluate previously proposed rules from smaller datasets, and to find a new set of rules that are optimal for the entire database. We also trained a regression tree with full cross-validation.
MicroRNAs (miRNAs) are an extensive class of tiny RNA molecules that regulate the expression of target genes by means of complementary base pair interactions. Although the first miRNAs were discovered in Caenorhabditis elegans, >300 miRNAs were recently documented in animals and plants, both by cloning methods and computational predictions. We present a genome-wide computational approach to detect miRNA genes in the Arabidopsis thaliana genome.
MicroRNAs are a class of small non-coding RNAs that regulate mRNA expression at the post - transcriptional level and thereby many fundamental biological processes. A number of methods, such as multiplex polymerase chain reaction, microarrays have been developed for profiling levels of known miRNAs. These methods lack the ability to identify novel miRNAs and accurately determine expression at a range of concentrations.