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.
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.
Serarching for miRNAs by their transcriptomic profiles
2013-07-29: RTH publish a new type of search for microRNA genes. In constrast to more traditional search strategies using the composition of the genomic DNA sequence, the strategy here directly aiming at exploiting the characteristic "finger print" microRNAs leave transcriptomic (RNA-seq) data.
For previous news, see here.
We expect to have more seminars in the near future. Stay tuned.
For previous events, see here.
Compare RNA-seq data to a database of pre-compiled miRNA RNA-seq profiles
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.
Automated identification of RNA 3D modules with discriminative power in RNA structural alignments