Direct updating of an RNA base-pairing probability matrix with marginal probability constraints

Modulation of MicroRNA Processing by Dicer via Its Associated dsRNA Binding Proteins

MicroRNAs (miRNAs) are small non-coding RNAs that are about 22 nucleotides in length. They regulate gene expression post-transcriptionally by guiding the effector protein Argonaute to its target mRNA in a sequence-dependent manner, causing the translational repression and destabilization of the target mRNAs. Both Drosha and Dicer, members of the RNase III family proteins, are essential components in the canonical miRNA biogenesis pathway. miRNA is transcribed into primary-miRNA (pri-miRNA) from genomic DNA. Drosha then cleaves the flanking regions of pri-miRNA into precursor-miRNA (pre-miRNA), while Dicer cleaves the loop region of the pre-miRNA to form a miRNA duplex. Although the role of Drosha and Dicer in miRNA maturation is well known, the modulation processes that are important for regulating the downstream gene network are not fully understood. In this review, we summarized and discussed current reports on miRNA biogenesis caused by Drosha and Dicer. We also discussed the modulation mechanisms regulated by double-stranded RNA binding proteins (dsRBPs) and the function and substrate specificity of dsRBPs, including the TAR RNA binding protein (TRBP) and the adenosine deaminase acting on RNA (ADAR).

Toward a next-generation atlas of RNA secondary structure

RNA structure plays a crucial role in gene maturation, regulation and function. Determining the form and frequency of RNA folds is essential for a better understanding of how RNA exerts its functions. Low-throughput studies have focused on RNA primary sequences and expression levels, but with an emphasis on relatively small numbers of transcripts. However, with the recent advent of high-throughput technologies, it is realistic to begin analyzing RNA secondary structures on a genome-wide scale. Here, we review genome-wide RNA secondary structure profiles as well as advances in computational structure predictions. We further discuss the novel characteristics of RNA secondary structure across messenger RNAs. Probing RNA secondary structure by high-throughput sequencing will enable us to build atlases of RNA secondary structures, an important step in helping us to understand the versatility of RNA functions in diverse cellular processes.

RNA secondary structure prediction from multi-aligned sequences

It has been well accepted that the RNA secondary structures of most functional non-coding RNAs (ncRNAs) are closely related to their functions and are conserved during evolution. Hence, prediction of conserved secondary structures from evolutionarily related sequences is one important task in RNA bioinformatics; the methods are useful not only to further functional analyses of ncRNAs but also to improve the accuracy of secondary structure predictions and to find novel functional RNAs from the genome. In this review, I focus on common secondary structure prediction from a given aligned RNA sequence, in which one secondary structure whose length is equal to that of the input alignment is predicted. I systematically review and classify existing tools and algorithms for the problem, by utilizing the information employed in the tools and by adopting a unified viewpoint based on maximum expected gain (MEG) estimators. I believe that this classification will allow a deeper understanding of each tool and provide users with useful information for selecting tools for common secondary structure predictions.

The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective

Any method for RNA secondary structure prediction is determined by four ingredients. The architecture is the choice of features implemented by the model (such as stacked basepairs, loop length distributions, etc.). The architecture determines the number of parameters in the model. The scoring scheme is the nature of those parameters (whether thermodynamic, probabilistic, or weights). The parameterization stands for the specific values assigned to the parameters. These three ingredients are referred to as "the model." The fourth ingredient is the folding algorithms used to predict plausible secondary structures given the model and the sequence of a structural RNA. Here, I make several unifying observations drawn from looking at more than 40 years of methods for RNA secondary structure prediction in the light of this classification. As a final observation, there seems to be a performance ceiling that affects all methods with complex architectures, a ceiling that impacts all scoring schemes with remarkable similarity. This suggests that modeling RNA secondary structure by using intrinsic sequence-based plausible "foldability" will require the incorporation of other forms of information in order to constrain the folding space and to improve prediction accuracy. This could give an advantage to probabilistic scoring systems since a probabilistic framework is a natural platform to incorporate different sources of information into one single inference problem.

CentroidAlign-Web: A Fast and Accurate Multiple Aligner for Long Non-Coding RNAs

Due to the recent discovery of non-coding RNAs (ncRNAs), multiple sequence alignment (MSA) of those long RNA sequences is becoming increasingly important for classifying and determining the functional motifs in RNAs. However, not only primary (nucleotide) sequences, but also secondary structures of ncRNAs are closely related to their function and are conserved evolutionarily. Hence, information about secondary structures should be considered in the sequence alignment of ncRNAs. Yet, in general, a huge computational time is required in order to compute MSAs, taking secondary structure information into account. In this paper, we describe a fast and accurate web server, called CentroidAlign-Web, which can handle long RNA sequences. The web server also appropriately incorporates information about known secondary structures into MSAs. Computational experiments indicate that our web server is fast and accurate enough to handle long RNA sequences. CentroidAlign-Web is freely available from http://centroidalign.ncrna.org/.