Background and activities
Professor in bioinformatics and Group Leader for Bioinformatics & Gene Regulation (BiGR): http://bigr.medisin.ntnu.no
NTNU Node Leader and Deputy Head for ELIXIR Norway: https://www.elixir-europe.org/about/elixir-norway
PubMed publications: http://www.ncbi.nlm.nih.gov/pubmed?term=drablos+f
SCOPUS Author ID: 8908254000
Trained in organic chemistry at University of Bergen (MSc 1983), including using computer-based expert system (SECS) and computational methods for strategies in organic synthesis. The computational aspects were basis for a PhD in chemometrics, also at University of Bergen. I was then recruited as a research scientist at SINTEF in Trondheim, where I did contract research on molecular modelling, in particular for pharmaceutical industry. This was gradually expanded into various methods in bioinformatics, in particular for sequence analysis and protein structure prediction. The SINTEF unit was closed down in 2002, and I was offered a position at NTNU as a research scientist. I got a qualification fellowship in 2004, and became full professor in bioinformatics in 2007. Since 2002 I have been strongly involved in building up bioinformatics as a research area at NTNU, initially as leader of Programme for Bioinformatics, later also as NTNU node leader for the FUGE technology platform and the ELIXIR Norway infrastructure for bioinformatics.
Focus of my research for the last 10 years has been on gene regulation, initially based on transcription factors, promoters and enhancers, but increasingly also including epigenetics, in particular histone modifications. The research group has established rigorous methods for benchmarking methods for motif discovery (BMC Bioinformatics 2007, 2008) and ChIP-seq data analysis (Nucleic Acids Res 2011). We have also developed novel methods for motif discovery (BMC Bioinformatics 2008), a tool for detailed analysis of regulatory regions (Bioinformatics 2010; BMC Bioinformatics 2013), and a tool for identification of ChIP-seq peaks (BMC Bioinformatics 2012). This has provided a basis for detailed analysis of gene regulation in several systems, both at the level of transcription factors (J Biol Chem 2010; BMC Genomics 2012; BMC Med 2013; PLoS One 2015), and with respect to histone modifications (PLoS One 2011; BMC Biology 2011; BMC Genomics 2014). This research is now done in collaboration with the international FANTOM consortium, leading to important papers where our main contribution has been on epigenetic regulation (BMC Genomics 2014; Nature 2014; Science 2015; Database 2015; NAR 2017).
For some years we have also built up a cancer-related research activity, in particular on colorectal cancer (CRC) and prostate cancer (PCa). For colorectal cancer we have mainly worked on identification of a specific mutation leading to increased risk for CRC (Familial Cancer 2015), whereas the research on prostate cancer has focused on identification of reliable gene sets for PCa prognosis (BMC Med Genomics 2014; PLoS ONE 2016; Oncotarget 2017).
Through the service and infrastructure activities the group has also been involved in several other projects, like research on prokaryotes, including oil well metagenomics, biodegradation, pathogenic bacteria, and characterization of novel bacterial genes.
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
- (2020) Enhanced identification of significant regulators of gene expression. BMC Bioinformatics. vol. 21.
- (2020) Robust Distance Measures for kNN Classification of Cancer Data. Cancer Informatics. vol. 19.
- (2020) DNA hypermethylation associated with upregulated gene expression in prostate cancer demonstrates the diversity of epigenetic regulation. BMC Medical Genomics. vol. 13 (1).
- (2020) Targeted sequencing of genes associated with the mismatch repair pathway in patients with endometrial cancer. PLOS ONE.
- (2020) LSD1 represses a neonatal/reparative gene program in adult intestinal epithelium. Science Advances. vol. 6 (37).
- (2019) DNA methylation data by sequencing: experimental approaches and recommendations for tools and pipelines for data analysis. Clinical Epigenetics. vol. 11:193.
- (2019) MACPET: model-based analysis for ChIA-PET. Biostatistics.
- (2018) Measures of co-expression for improved function prediction of long non-coding RNAs. BMC Bioinformatics. vol. 19 (1).
- (2018) The role of PCNA as a scaffold protein in cellular signaling is functionally conserved between yeast and humans. FEBS Open Bio. vol. 8 (7).
- (2018) Microbial community and metagenome dynamics during biodegradation of dispersed oil reveals potential key-players in cold Norwegian seawater. Marine Pollution Bulletin. vol. 129 (1).
- (2018) Cholesterol synthesis pathway genes in prostate cancer are transcriptionally downregulated when tissue confounding is minimized. BMC Cancer. vol. 18 (478).
- (2018) Norwegian e-Infrastructure for Life Sciences (NeLS). F1000 Research. vol. 7:968.
- (2018) Comparative Transcriptome Profiling Reveals a Potential Role of Type VI Secretion System and Fimbriae in Virulence of Non-O157 Shiga Toxin-Producing Escherichia coli. Frontiers in Microbiology. vol. 9.
- (2017) Update of the FANTOM web resource: high resolution transcriptome of diverse cell types in mammals. Nucleic Acids Research. vol. 45 (D1).
- (2017) Use of multigene-panel identifies pathogenic variants in several CRC-predisposing genes in patients previously tested for Lynch Syndrome. Clinical Genetics. vol. 92 (4).
- (2017) SFRP4 gene expression is increased in aggressive prostate cancer. Scientific Reports. vol. 7.
- (2017) GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome. GigaScience. vol. 6 (7).
- (2016) Gene regulation in the immediate-early response process. Advances in Biological Regulation. vol. 62.
- (2016) Feature-based classification of human transcription factors into hypothetical sub-classes related to regulatory function. BMC Bioinformatics. vol. 17 (459).
- (2016) TopoICSim: A new semantic similarity measure based on gene ontology. BMC Bioinformatics. vol. 17 (1).