snoRNA-LBME-db, a comprehensive database of human H/ACA and C/D box snoRNAs

The snoRNA-LBME-db is a dedicated database containing human C/D box and H/ACA box small nucleolar RNAs (snoRNAs), and small Cajal body-specific RNAs (scaRNAs). C/D box and H/ACA box snoRNAs are part of ribonucleoparticles that guide 2′-O-ribose methylation and pseudouridilation, respectively, of selected residues of 28S, 18S or 5.8S rRNAs or of the spliceosomal U6 RNA. Similarly, scaRNAs guide modifications of the spliceosomal RNAs transcribed by RNA polymerase II (U1, U2, U4, U5 and U12) and are often composed of both C/D box and H/ACA box domains. However, some snoRNAs do not function as modification guide RNAs, but rather as RNA chaperones during the maturation of pre-rRNA. The database was built by a compilation of the literature, and comprises human sno/scaRNAs that were experimentally verified, as well as the human orthologs of snoRNAs that were cloned in other vertebrate species, and some snoRNAs that are predicted by bioinformatics search in loci submitted to genomic imprinting, but have not all been experimentally verified. For each entry, the database identifies the modified nucleotide(s) in the target RNA(s), indicates the corresponding predicted base pairing, gives a few pertinent references and provides a link to the position of the sno/scaRNA on the UCSC Genome Browser. The ‘Find guide RNA’ function allows one to find the sno/scaRNAs predicted to guide the modification of a particular nucleotide in the rRNA and spliceosomal RNA sequences. The ‘Browse’ function allows one to download the sequences of selected sno/scaRNAs in the FASTA format. The database is available online at . It can also be accessed from the human UCSC Genome Browser via the sno/miRNA track

Sequence–structure relationships in RNA loops: establishing the basis for loop homology modeling

The specific function of RNA molecules frequently resides in their seemingly unstructured loop regions. We performed a systematic analysis of RNA loops extracted from experimentally determined three-dimensional structures of RNA molecules. A comprehensive loop-structure data set was created and organized into distinct clusters based on structural and sequence similarity. We detected clear evidence of the hallmark of homology present in the sequence–structure relationships in loops. Loops differing by <25% in sequence identity fold into very similar structures. Thus, our results support the application of homology modeling for RNA loop model building. We established a threshold that may guide the sequence divergence-based selection of template structures for RNA loop homology modeling. Of all possible sequences that are, under the assumption of isosteric relationships, theoretically compatible with actual sequences observed in RNA structures, only a small fraction is contained in the Rfam database of RNA sequences and classes implying that the actual RNA loop space may consist of a limited number of unique loop structures and conserved sequences. The loop-structure data sets are made available via an online database, RLooM. RLooM also offers functionalities for the modeling of RNA loop structures in support of RNA engineering and design efforts

Identification of novel components of Trypanosoma brucei editosomes

The editosome is a multiprotein complex that catalyzes the insertion and deletion of uridylates that occurs during RNA editing in trypanosomatids. We report the identification of nine novel editosome proteins in Trypanosoma brucei. They were identified by mass spectrometric analysis of functional editosomes that were purified by serial ion exchange/gel permeation chromatography, immunoaffinity chromatography specific to the TbMP63 editosome protein, or tandem affinity purification based on a tagged RNA editing ligase. The newly identified proteins have ribonuclease and/or RNA binding motifs suggesting nuclease function for at least some of these. Five of the proteins are interrelated, as are two others, and one is related to four previously identified editosome proteins. The implications of these findings are discussed

Database for bacterial group II introns

The Database for Bacterial Group II Introns (http://webapps2.ucalgary.ca/~groupii/index.html#) provides a catalogue of full-length, non-redundant group II introns present in bacterial DNA sequences in GenBank. The website is divided into three sections. The first section provides general information on group II intron properties, structures and classification. The second and main section lists information for individual introns, including insertion sites, DNA sequences, intron-encoded protein sequences and RNA secondary structure models. The final section provides tools for identification and analysis of intron sequences. These include a step-by-step guide to identify introns in genomic sequences, a local BLAST tool to identify closest intron relatives to a query sequence, and a boundary-finding tool that predicts 5′ and 3′ intron–exon junctions in an input DNA sequence. Finally, selected intron data can be downloaded in FASTA format. It is hoped that this database will be a useful resource not only to group II intron and RNA researchers, but also to microbiologists who encounter these unexpected introns in genomic sequences

decodeRNA-predicting non-coding RNA functions using guilt-by-association

Although the long non-coding RNA (lncRNA) landscape is expanding rapidly, only a small number of lncRNAs have been functionally annotated. Here, we present decodeRNA (http://www.decoderna.org), a database providing functional contexts for both human lncRNAs and microRNAs in 29 cancer and 12 normal tissue types. With state-of-the-art data mining and visualization options, easy access to results and a straightforward user interface, decodeRNA aims to be a powerful tool for researchers in the ncRNA field

Effect of Non-Coding RNA on Post-Transcriptional Gene Silencing of Alzheimer Disease

A large amount of hidden biological information is contained in the human genome, which is not expressed or revealed in the form of proteins; the usual end product form of gene expression. Instead, most of such information is in the form of non-coding RNAs (ncRNAs). ncRNAs correspond to genes that are transcribed, but do not get translated into proteins. This part of the genome was, till recently, considered as &#x2018;junk&#x2019;. The term &#x2018;junk&#x2019; implied lack of any discernible function of these RNA. More than 98% of the human genomic size encompasses these non-coding RNAs. But, recent research has evidently brought out the indispensible contribution of non-coding RNA in controlling and regulating gene expression. ncRNA such as siRNAs and microRNAs have been reported to greatly help in causing post-transcriptional gene silencing (PTGS) in cells through RNA interference (RNAi) pathway. In this work, we have investigated the possibility of using siRNAs and microRNAs to aid in gene silencing of early onset Alzheimer&#x2019;s disease genes. &#xd;&#xa;Alzheimer&#x2019;s disease specific mutations and their corresponding positions in mRNA have been identified for six genes; Presenilin-1, Presenilin-2, APP (amyloid beta precursor protein), APBB3, BACE-1 and PSENEN. &#xd;&#xa;&#xd;&#xa;Small interfering RNAs (siRNAs) that can cause PTGS through RNA interference pathway have been designed. RNA analysis has been done to verify complementarity of antisense siRNA sequence with target mRNA sequence. Interaction studies have been done computationally between these antisense siRNA strands and seven Argonaute proteins. From the interaction studies, only one of the seven Argonaute proteins; 1Q8K, was found to have interaction with the siRNAs indicating the importance and uniqueness of this particular protein in RISC (RNA induced silencing complex). &#xd;&#xa;&#xd;&#xa;The interaction studies have been carried out for the microRNAs also. Out of the 700 mature human microRNAs collected, 394 microRNAs have been identified to show partial complementarity with their target sequence on PSEN-1 mRNA. Of these 394, five microRNAs have shown partial complementarity to early onset Alzheimer&#x2019;s disease specific mutations in PSEN-1 mRNA. Interaction studies have been done between these microRNAs and Argonaute proteins. Thus, design, characterization and analysis of ncRNAs that contribute to post transcriptional gene silencing of Alzheimer&#x2019;s disease have been achieved.&#xd;&#xa

Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case

A genome modular classification that associates cellular processes to modules could lead to a method to quantify the differences in gene expression levels in different cellular stages or conditions: the transcriptogram, a powerful tool for assessing cell performance, would be at hand. Here we present a computational method to order genes on a line that clusters strongly interacting genes, defining functional modules associated with gene ontology terms. The starting point is a list of genes and a matrix specifying their interactions, available at large gene interaction databases. Considering the Saccharomyces cerevisiae genome we produced a succession of plots of gene transcription levels for a fermentation process. These plots discriminate the fermentation stage the cell is going through and may be regarded as the first versions of a transcriptogram. This method is useful for extracting information from cell stimuli/responses experiments, and may be applied with diagnostic purposes to different organisms