The miRNA Registry provides a service for the assignment of miRNA gene names prior to publication. A comprehensive and searchable database of published miRNA sequences is accessible via a web interface (http://www.sanger.ac.uk/Software/Rfam/mirna/), and all sequence and annotation data are freely available for download. Release 2.0 of the database contains 506 miRNA entries from six organisms.
MicroRNAs (miRNAs) are small noncoding RNA gene products about 22 nt long that are processed by Dicer from precursors with a characteristic hairpin secondary structure. Guidelines are presented for the identification and annotation of new miRNAs from diverse organisms, particularly so that miRNAs can be reliably distinguished from other RNAs such as small interfering RNAs. We describe specific criteria for the experimental verification of miRNAs, and conventions for naming miRNAs and miRNA genes.
The recent discoveries of microRNA (miRNA) genes and characterization of the first few target genes regulated by miRNAs in Caenorhabditis elegans and Drosophila melanogaster have set the stage for elucidation of a novel network of regulatory control. We present a computational method for whole-genome prediction of miRNA target genes. The method is validated using known examples.
MicroRNAs are small noncoding RNAs that recognize and bind to partially complementary sites in the 3' untranslated regions of target genes in animals and, by unknown mechanisms, regulate protein production of the target transcript. Different combinations of microRNAs are expressed in different cell types and may coordinately regulate cell-specific target genes. Here, we present PicTar, a computational method for identifying common targets of microRNAs.
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.
MicroRNAs (miRNAs) are approximately 22-nt RNA segments that are involved in the regulation of protein expression primarily by binding to one or more target sites on an mRNA transcript and inhibiting translation. MicroRNAs are likely to factor into multiple developmental pathways, multiple mechanisms of gene regulation, and underlie an array of inherited disease processes and phenotypic determinants. Several computational programs exist to predict miRNA targets in mammals, fruit flies, worms, and plants.
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.
MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes' expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA-target interactions.
Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds.