Milad Miladi1, Eteri Sokhoyan1, Torsten Houwaart2, Steffen Heyne3, Fabrizio Costa4, Björn Grüning1,5, Rolf Backofen1,5,6. 1. Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany. 2. Institute of Medical Microbiology and Hospital Hygiene, University of Dusseldorf, Universitaetsstr. 1, 40225 Dusseldorf, Germany. 3. Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Stuebeweg 51, 79108 Freiburg, Germany. 4. Department of Computer Science, University of Exeter, North Park Road, EX4 4QF Exeter, UK. 5. ZBSA Centre for Biological Systems Analysis, University of Freiburg, Hauptstr. 1, 79104 Freiburg, Germany. 6. Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Schaenzlestr. 18, 79104 Freiburg, Germany.
Abstract
BACKGROUND: RNA plays essential roles in all known forms of life. Clustering RNA sequences with common sequence and structure is an essential step towards studying RNA function. With the advent of high-throughput sequencing techniques, experimental and genomic data are expanding to complement the predictive methods. However, the existing methods do not effectively utilize and cope with the immense amount of data becoming available. RESULTS: Hundreds of thousands of non-coding RNAs have been detected; however, their annotation is lagging behind. Here we present GraphClust2, a comprehensive approach for scalable clustering of RNAs based on sequence and structural similarities. GraphClust2 bridges the gap between high-throughput sequencing and structural RNA analysis and provides an integrative solution by incorporating diverse experimental and genomic data in an accessible manner via the Galaxy framework. GraphClust2 can efficiently cluster and annotate large datasets of RNAs and supports structure-probing data. We demonstrate that the annotation performance of clustering functional RNAs can be considerably improved. Furthermore, an off-the-shelf procedure is introduced for identifying locally conserved structure candidates in long RNAs. We suggest the presence and the sparseness of phylogenetically conserved local structures for a collection of long non-coding RNAs. CONCLUSIONS: By clustering data from 2 cross-linking immunoprecipitation experiments, we demonstrate the benefits of GraphClust2 for motif discovery under the presence of biological and methodological biases. Finally, we uncover prominent targets of double-stranded RNA binding protein Roquin-1, such as BCOR's 3' untranslated region that contains multiple binding stem-loops that are evolutionary conserved.
BACKGROUND: RNA plays essential roles in all known forms of life. Clustering RNA sequences with common sequence and structure is an essential step towards studying RNA function. With the advent of high-throughput sequencing techniques, experimental and genomic data are expanding to complement the predictive methods. However, the existing methods do not effectively utilize and cope with the immense amount of data becoming available. RESULTS: Hundreds of thousands of non-coding RNAs have been detected; however, their annotation is lagging behind. Here we present GraphClust2, a comprehensive approach for scalable clustering of RNAs based on sequence and structural similarities. GraphClust2 bridges the gap between high-throughput sequencing and structural RNA analysis and provides an integrative solution by incorporating diverse experimental and genomic data in an accessible manner via the Galaxy framework. GraphClust2 can efficiently cluster and annotate large datasets of RNAs and supports structure-probing data. We demonstrate that the annotation performance of clustering functional RNAs can be considerably improved. Furthermore, an off-the-shelf procedure is introduced for identifying locally conserved structure candidates in long RNAs. We suggest the presence and the sparseness of phylogenetically conserved local structures for a collection of long non-coding RNAs. CONCLUSIONS: By clustering data from 2 cross-linking immunoprecipitation experiments, we demonstrate the benefits of GraphClust2 for motif discovery under the presence of biological and methodological biases. Finally, we uncover prominent targets of double-stranded RNA binding protein Roquin-1, such as BCOR's 3' untranslated region that contains multiple binding stem-loops that are evolutionary conserved.
Authors: Milad Miladi; Alexander Junge; Fabrizio Costa; Stefan E Seemann; Jakob Hull Havgaard; Jan Gorodkin; Rolf Backofen Journal: Bioinformatics Date: 2017-07-15 Impact factor: 6.937
Authors: Alexander Mitrofanov; Marcus Ziemann; Omer S Alkhnbashi; Wolfgang R Hess; Rolf Backofen Journal: Bioinformatics Date: 2022-09-16 Impact factor: 6.931
Authors: Ioanna Kalvari; Eric P Nawrocki; Nancy Ontiveros-Palacios; Joanna Argasinska; Kevin Lamkiewicz; Manja Marz; Sam Griffiths-Jones; Claire Toffano-Nioche; Daniel Gautheret; Zasha Weinberg; Elena Rivas; Sean R Eddy; Robert D Finn; Alex Bateman; Anton I Petrov Journal: Nucleic Acids Res Date: 2021-01-08 Impact factor: 16.971