Center for non-coding RNA in Technology and Health (RTH)
The center aims at developing technologies, computational methods as well as experimental approaches for analysis of the mammalian genome for non-coding RNAs in relation to (inflammatory) diseases. The center will focus on developing these technologies to exploit them and the findings in relation to diabetes. The center consists of a number of national and international partners, with the core located at the Faculty for Health and Medical Sciences of University of Copenhagen .
The people in the center cover a range of expertises including computational biology, RNA bioinformatics, molecular models in diabetes, RNA biology, animal models, functional genomics and high-throughput sequence analysis..
Join us
We are always looking for motivated and talented young scientists as well as projects or colaborations within the areas of the center. Feel free to contact us with suggestions or to ask for more information.
News
CRISPR/Cas9 efficiency explored: binding energies and PAM context
30 May 2022. Researchers from RTH and Aarhus University describe the energy-based binding mechanisms of CRISPR/Cas9-gRNAs and use an energy model to assess the cleavage efficiency of Cas9-gRNAs. The model is further applied to describe the DNA "sliding" of Cas9 competing for overlapping PAMs. Read the article here or see the news flash on CRISPR medicine news.
Events
We are currently planning more seminars, stay tuned
Recent resources
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
CRISPRon
CRISPR-Cas9 gRNA efficiency prediction webserver based on data integration and deep learning
Bacillus subtilis PrsA RNAseq data
RNA-seq dataset for a study of the impact of PrsA over-expression on the Bacillus subtilis transcriptome
Research outset
The human genome, made up of DNA, consists of three billion building blocks (nucleotides) where some regions (stretches) are complete genes. We all carry variants of the genes and some cause diseases. Here, the goal is to investigate the specific class of genes, the non-coding RNA genes, in relation to diabetes. The non-coding RNA (ncRNA) genes can be the missing components in diseases that previously have been overlooked.
Our research goal is to develop technologies for ncRNA analysis and to search for functional ncRNAs in relation to diabetes and other (inflammatory) diseases.
Research in details .Recent publications
Patent data-driven analysis of literature associations with changing innovation trends
Geissler AS, Gorodkin J, Seemann SE* Front Res Metr Anal. 2024 Aug 1;9:1432673. eCollection 2024
[ PubMed | Paper ]
Clusters of mammalian conserved RNA structures in UTRs associate with RBP binding sites
Gadekar VP, Munk AW, Miladi M, Junge A, Backofen R, Seemann SE*,
[ PubMed | Paper ]
Comparative RNA Genomics
Backofen R, Gorodkin J, Hofacker IL, Stadler PF* Methods Mol Biol. 2024;2802:347-393
[ PubMed | Paper ]
Comparative RNA Genomics
Backofen R, Gorodkin J, Hofacker IL, Stadler PF* Comparative RNA Genomics In Comparative Genomics. Setubal JC, Stoye J, Stadler PF (Ed.), Methods Mol Biol. 2018;1704:363-400
[ PubMed | Chapter ]
Emerging tools for RNA structure analysis in polymorphic data
Sabarinathan R, Seemann SE, Gorodkin J* Biozoom 2:3, 2015
[ Paper ]
CUDA-Sankoff: Using GPU to Accelerate the Pairwise Structural RNA Alignment
Sundfeld D, Havgaard JH, Gorodkin J, De Melo AC* 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) 2017, CPAPER
[ Paper ]