Bioinformatics – University of Copenhagen

Computational Biology and Bioinformatics

Biology has to a large extend become an information science. With the quantity of biological data being generated, for example by high-throughput sequencing techniques, data can only meaningfully be processed by computer.

The processing of the data and the subsequent search for patterns (sequence and structure) in DNA (genome), RNA and proteins help us to identify functional genomic components. However to do this, programs needs to be efficient so run times can be minimized.

The group has a general focus on animal genomics and in particular non-coding RNAs (ncRNAs) and analysis of high-throughput sequencing data. ncRNAs are rapidly becoming a central focus of genomic biology and given only ~1% of the (~3 billion base) mammalian genome encodes proteins, the potential for the genome to host many ncRNAs is large.

In our group we develop new computational methods (computational biology) as well as setting up pipelines for genome annotation (bioinformatics). We relate these findings to diseases and other phenotypes. We are addressing animal models for human disease, and we are studying bacteria used in industrial contexts as cell factories, with the aim of understanding production yield.

The group host Center for non-coding RNA in Technology and Health (see details at http://rth.dk) which take a whole new approach to disease studies by searching for ncRNA and structured RNAs as disease components and biomarkers through development of in silico search tools for ncRNA analysis complemented by experimental analysis and further functional studies. The disease focus is on inflammatory diseases and diabetes employing human and animal material.

Recent selected publications:

  • The identification and functional annotation of RNA structures conserved in vertebrates
    Seemann SE, Mirza AH, Hansen C, Bang-Berthelsen CH, Garde C, Christensen-Dalsgaard M, Torarinsson E, Yao Z, Workman CT, Pociot F, Nielsen H, Tommerup N, Ruzzo WL, Gorodkin J Genome Res. 2017 Aug;27(8):1371-1383
  • RIsearch2: suffix array-based large-scale prediction of RNA–RNA interactions and siRNA off-targets. Alkan F, Wenzel A, Palasca O, Kerpedjiev P, Rudebeck A, Stadler PF, Hofacker IL, Gorodkin J Nucleic Acids Res. 2017 May 5;45(8):e60.
  • RAIN: RNA–protein Association and Interaction Networks
    Junge A, Refsgaard JC, Garde C, Pan X, Santos A, Alkan F, Anthon C, von Mering C, Workman CT, Jensen LJ, Gorodkin J Database (Oxford). 2017 Jan 10;2017.
  • Structured RNAs and synteny regions in the pig genome. Anthon C, Tafer H, Havgaard JH, Thomsen B, Hedegaard J, Seemann SE, Pundhir S, Kehr S, Bartschat S, Nielsen M, Nielsen RO, Fredholm M, Stadler PF, Gorodkin J. BMC Genomics. 15:459., 2014
  • MicroRNA discovery by similarity search to a database of RNA-seq profiles. Pundhir S, Gorodkin J. Front Genet. 4:133., 2013
  • RNAsnp: efficient detection of local RNA secondary structure changes induced by SNPs. Sabarinathan R, Tafer H, Seemann SE, Hofacker IL, Stadler PF, Gorodkin J. Hum Mutat. 34:546-56., 2013
  • Analyses of pig genomes provide insight into porcine demography and evolution. Groenen MA, et al. Nature. 491:393-8., 2012