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Ribonucleic acids (RNAs), the “genomic dark matter”, have emerged as one of the most important of biomolecules. They play key roles in various aspects of the gene transcription and regulation processes, and are the focus of an ever-increasing interest in all subfields of biology. Deciphering the function of a non-protein coding RNA requires an intimate knowledge of its structure, motivating the development of structure-centric analyses. Underlying RNA structural workflows, one finds computational methods focusing on discrete abstractions for conformations, such as the secondary structure, which possibly include non-canonical interactions and pseudoknots. Such representations have led to efficient and accurate methods and algorithms, amenable to transcriptomic-scale studies. Recently, novel experimental techniques, based on next-generation sequencing, have led to an unprecedented deluge of soft structural data (chemical/enzymatic footprinting, FRET…). It is therefore one of the exciting challenges of computational biology, and the subject of much work within the RNA bioinformatics community, to contribute new paradigms and methods addressing the challenge of scalability for RNA structure-centric pipelines in the big-data era. This challenge not only originates in the sheer magnitude of produced transcriptomic data, but also in the combinatorial explosion of sequences and structures associated with a given transcript length. Successfully addressing this challenge will require a combination of highly efficient algorithms, mathematical models and versatile, yet compact, discrete representations and data structures.
Goals and topics. The workshop aims at bringing together researchers and students, contributing and using algorithms and methods for structure-centric analyses of RNA. It will provide a forum for the dissemination of state-of-the-art methods and tools using discrete representations of folding landscapes in the context of big data. It will contribute to establish best-practices towards a better support for large-scale data within structure-centric algorithms. The scope includes, but is not limited to:
- RNA folding prediction: Ab initio/comparative approaches, incorporation of experimental data (probing and enzymatic)…
- RNA/RNA, RNA/Protein interactions
- Structural clustering/indexation methods
- Mutational analysis
- Extended folding landscapes: Tertiary modules, non-canonical interactions, pseudoknots
- RNA Kinetics
- Structure-centric alignment and modeling
- RNA Design
- Algorithmic foundations: Dynamic Programming frameworks, optimizations, complexity results
- Mathematical models and tools for RNA structure analysis
- Fabrice Jossinet (Univ. Strasbourg, France)
- Yann Ponty (CNRS/Ecole Polytechnique, France)
- Jerôme Waldispühl (Univ. Mc Gill, Canada)