The short- and long-term goals we pursue.
Current areas of research we focus on. Note that we are a purely computational lab .
Recent computational methods we developed.
Funding agencies that support our research.
One-stop shop for all of our computational methods and corresponding web-servers.
Our publications and pre-prints, including short summaries.
Information on how to join us as prospective researcher or new collaborator.
Who we are, how to get in touch and directions how to find us .
Discover new biological classes of trans RNA-RNA interactions.
Investigate their functional roles, in particular during infection in host-pathogen settings.
Identify targets for therapeutic applications that prevent or enable particular trans RNA-RNA interactions.
Direct trans RNA-RNA interactions between two transcripts can readily influence the expression of transcripts on RNA level. It is only since a few years that we can probe the trans and cis RNA-RNA interactome of cells in a transcriptome-wide manner in a way that rely on a protein-specific interaction. We are thus only starting to explore the functional roles that trans RNA-RNA interactions play in vivo. We have a long-standing interest in trans RNA-RNA interactions and hypothesise that many functional classes of these interactions remain to be discovered, particular in the setting of pathogen-host interactions, see e.g. pdf and pdf. As trans RNA-RNA interactions are comparatively easy to engineer (compared to interactions that require a protein to bind a stretch of RNA), they offer ample potential to be artificially engineered, optimised and/or prevented.
Transcripts in vivo start folding into transient RNA secondary structure features as soon as transcription starts. This so-called co-transcriptional folding pathway helps the transcript efficiently fold into its functional RNA secondary structure in the cellular environment. Many commonly used computational methods for RNA secondary structure prediction, however, silently assume that the input transcript is (1) already fully synthesized, (2) in thermodynamic equilibrium and (3) that it has no trans interaction partners that may impact its folding. These assumptions generally do not hold for transcripts in vivo.
Transcripts of protein-coding genes are often depicted as linear sticks, e.g. in many text book illustrations of the central dogma of biology. The expression of protein-coding transcripts in vivo, however, may be finely regulated via RNA secondary structure features which are exquisitive sensors of the particular cellular environment. Depending on the cellular environment in vivo, a transcript may respond by expressing one of several potential RNA structures that are all encoded in the primary sequence of the transcript. This is a concept that we have introduced as alternative RNA structure expression, see pdf. We have shown that key processes such as alternative splicing and translation can be regulated by RNA secondary features that are directly encoded into protein-coding transcripts and that regulate these processes in response to the particulars of the in vivo environment.
Given an RNA with a known structure, discover transient RNA structure features that may influence the co-transcriptional formation of the reference structure, see pdf, CoBold web-server and CoBold visualization web-server.
Given an RNA, predict the minimum-free energy structure while taking the overall effect of co-transcriptional folding into account, see pdf and CoFold web-server.
Given an RNA and a corresponding multiple-sequence alignment, identify evolutionarily conserved helices (consecutive stretches of base-pairs) including helices from transient, pseudo-knotted and alternative RNA structures, see pdf and Transat web-server.
Given an RNA and a set of evolutionarily related sequences (which are not yet aligned), simultaneously predict the best evolutionarily conserved RNA secondary structure (including pseudo-knots), the best structural multiple sequence alignment as well as the evolutionary tree linking the input sequences, see pdf and SimulFold web-server.
Predict evolutionarily conserved RNA structures that are fully or partly contained in known protein-coding regions. Requires as input a multiple-sequence alignment, see pdf, pdf and RNA-Decoder web-server.
Visualise complex RNA structures and trans RNA-RNA, RNA-DNA and DNA-DNA interactions alongside quantitative data, with evolutionary information (multiple sequence alignments) and in a comparative way, see pdf, pdf and R-Chie web-server (including download of corresponding R code).
Search for circular RNAs in transcriptome databases by indexing backsplice junctions, see pdf and BIQ web-server.
One-stop shop for all of our web-servers and software.
We thank the following funding agencies for generously supporting our research.