Short interfering RNAs (siRNAs) are a popular method for gene-knockdown, acting by degrading the target mRNA. Before performing experiments it is invaluable to locate and evaluate previous knockdown experiments for the gene of interest. The siRNA database provides a gene-centric view of siRNA experimental data, including siRNAs of known efficacy and siRNAs predicted to be of high efficacy by a combination of methods. Linked to these sequences is information such as siRNA thermodynamic properties and the potential for sequence-specific off-target effects.
Detection of potential cross-reaction between a short oligonucleotide sequence and a longer (unintended) sequence is crucial for many biological applications, such as high content screening (HCS), microarray nucleotide probes, or short interfering RNAs (siRNAs).
Post-transcriptional regulation of gene expression by small RNAs and RNA binding proteins is of fundamental importance in development of complex organisms, and dysregulation of regulatory RNAs can influence onset, progression and potentially be target for treatment of many diseases. Post-transcriptional regulation by small RNAs is mediated through partial complementary binding to messenger RNAs leaving nucleotide signatures or motifs throughout the entire transcriptome.
Although the observations concerning the factors which influence the siRNA efficacy give clues to the mechanism of RNAi, the quantitative prediction of the siRNA efficacy is still a challenge task. In this paper, we introduced a novel non-linear regression method: random forest regression (RFR), to quantitatively estimate siRNAs efficacy values. Compared with an alternative machine learning regression algorithm, support vector machine regression (SVR) and four other score-based algorithms [A. Reynolds, D. Leake, Q. Boese, S. Scaringe, W.S. Marshall, A.
Imprinted noncoding RNAs (ncRNAs) are expressed mono-allelically in a parent-of-origin-dependent manner, which is mainly evident in mammals. Lying at a crossroad between imprinted genes and ncRNAs, imprinted ncRNAs show distinct features. They are likely to function in nontraditional ways compared to non-imprinted ncRNAs, and are much more responsible for the mechanism of genomic imprinting compared to imprinted protein-coding genes. An increasing number of human diseases have been shown to be related to abnormalities in imprinted ncRNAs.
In plants, many trans-acting small interfering RNA (ta-siRNA) regulatory pathways have been identified as significant components of the gene networks involved in development, metabolism, responses to biotic and abiotic stresses and DNA methylation at the TAS locus.
We have developed an algorithm for the prediction of dual-targeting short interfering RNAs (siRNAs) in which both strands are deliberately designed to separately target different mRNA transcripts with complete complementarity. An advantage of this approach versus the use of two separate duplexes is that only two strands, as opposed to four, are competing for entry into the RNA-induced silencing complex.
Small interfering RNA (siRNA) technology has vast potential for functional genomics and development of therapeutics. However, it faces many obstacles predominantly instability of siRNAs due to nuclease digestion and subsequently biologically short half-life. Chemical modifications in siRNAs provide means to overcome these shortcomings and improve their stability and potency. Despite enormous utility bioinformatics resource of these chemically modified siRNAs (cm-siRNAs) is lacking.
Small RNAs play an important role in plant development, stress responses, and epigenetic regulation, primarily through their role in transcriptional and post-transcriptional silencing of specific target genes and loci. Most if not all plants utilize these small RNA signaling networks. We have developed a deep-sequencing based dataset of plant small RNAs, based on the hypothesis that comparisons among the complex pool of small RNAs from diverse plants will identify novel types of conserved, regulated, or species-specific molecules.
Recent studies have demonstrated the importance of long non-coding RNAs (lncRNAs) in chromatin remodeling, and in transcriptional and post-transcriptional regulation. However, only a few specific lncRNAs are well understood, whereas others are completely uncharacterized. To address this, there is a need for user-friendly platform to studying the putative regulatory functions of human lncRNAs.