The Vienna RNA Websuite is a comprehensive collection of tools for folding, design and analysis of RNA sequences. It provides a web interface to the most commonly used programs of the Vienna RNA package. Among them, we find folding of single and aligned sequences, prediction of RNA–RNA interactions, and design of sequences with a given structure. Additionally, we provide analysis of folding landscapes using the barriers program and structural RNA alignments using LocARNA. The web server together with software packages for download is freely accessible at http://rna.tbi.univie.ac.at/ .

The prediction of a consensus structure for a set of related RNAs is an important first step for subsequent analyses. RNAalifold, which computes the minimum energy structure that is simultaneously formed by a set of aligned sequences, is one of the oldest and most widely used tools for this task. In recent years, several alternative approaches have been advocated, pointing to several shortcomings of the original RNAalifold approach.

Through alternative polyadenylation, human mRNAs acquire longer or shorter 3' untranslated regions, the latter typically associated with higher transcript stability and increased protein production. To understand the dynamics of polyadenylation site usage, we performed transcriptome-wide mapping of both binding sites of 3' end processing factors CPSF-160, CPSF-100, CPSF-73, CPSF-30, Fip1, CstF-64, CstF-64τ, CF I(m)25, CF I(m)59, and CF I(m)68 and 3' end processing sites in HEK293 cells. We found that although binding sites of these factors generally cluster around the poly(A) sites most frequently used in cleavage, CstF-64/CstF-64τ and CFI(m) proteins have much higher positional specificity compared to CPSF components. Knockdown of CF I(m)68 induced a systematic use of proximal polyadenylation sites, indicating that changes in relative abundance of a single 3' end processing factor can modulate the length of 3' untranslated regions across the transcriptome and suggesting a mechanism behind the previously observed increase in tumor cell invasiveness upon CF I(m)68 knockdown.

Small nucleolar RNAs (snoRNAs) are a class of non-coding RNAs that guide the post-transcriptional processing of other non-coding RNAs (mostly ribosomal RNAs), but have also been implicated in processes ranging from microRNA-dependent gene silencing to alternative splicing. In order to construct an up-to-date catalog of human snoRNAs we have combined data from various databases, de novo prediction and extensive literature review. In total, we list more than 750 curated genomic loci that give rise to snoRNA and snoRNA-like genes. Utilizing small RNA-seq data from the ENCODE project, our study characterizes the plasticity of snoRNA expression identifying both constitutively as well as cell type specific expressed snoRNAs. Especially, the comparison of malignant to non-malignant tissues and cell types shows a dramatic perturbation of the snoRNA expression profile. Finally, we developed a high-throughput variant of the reverse-transcriptase-based method for identifying 2′-O-methyl modifications in RNAs termed RimSeq. Using the data from this and other high-throughput protocols together with previously reported modification sites and state-of-the-art target prediction methods we re-estimate the snoRNA target RNA interaction network. Our current results assign a reliable modification site to 83% of the canonical snoRNAs, leaving only 76 snoRNA sequences as orphan.