Inge Jonassen's picture

Inge Jonassen

Head of Department, Professor
  • E-mailinge.jonassen@uib.no
  • Phone+47 55 58 47 1390524316
  • Visitor Address
    Høyteknologisenteret i Bergen
    No5020 Bergen
  • Postal Address
    Postboks 7803
    5020 Bergen

As Head of department I have the overall responsibility for the activities of the department inlcuding research and education. 

My own field of research is bioinformatics - development and application of informatics methods for the analysis of molecular biology data. My interests include methods for the automatic discovery of patterns, data analysis, algorithms and machine learning applied on molecular biology data. The research in my group includes tight collaboration with experimental groups wihtin different fields of biological and medical research. I work with analysis of different types of data including DNA sequences (including genomes), protein sequences and structures, gene expression data, and data generated usiing high-throughput technologies, e.g., next-generation sequencing. My group is also engaged in development and applicationof methods for integration of data and tools within bioinformatics.

I teach mostly bioinformatics courses, but I have also been teaching user courses in informatics and operating systems.

  • Show author(s) (2023). Toxicogenomics using precision-cut liver slice culture in fish .
  • Show author(s) (2023). Toxicogenomics using precision-cut liver slice culture in fish.
  • Show author(s) (2023). LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies. Bioinformatics Advances.
  • Show author(s) (2023). Integrative omics-analysis of lipid metabolism regulation by peroxisome proliferator-activated receptor a and b agonists in male Atlantic cod. Frontiers in Physiology. 17 pages.
  • Show author(s) (2023). Early response evaluation by single cell signaling profiling in acute myeloid leukemia. Nature Communications.
  • Show author(s) (2023). Author Correction: Early response evaluation by single cell signaling profiling in acute myeloid leukemia (Nature Communications, (2023), 14, 1, (115), 10.1038/s41467-022-35624-4). Nature Communications.
  • Show author(s) (2022). LiceBase- An organism-Specific Database for Functional Genomics of Salmon Louse.
  • Show author(s) (2022). Identifying predictors of survival in patients with leukemia using single-cell mass cytometry and machine learning. bioRxiv.
  • Show author(s) (2022). Early response evaluation by single cell signaling profiling in acute myeloid leukemia. Research Square.
  • Show author(s) (2021). The salmon louse genome: Copepod features and parasitic adaptations. Genomics. 3666-3680.
  • Show author(s) (2021). The chemical defensome of five model teleost fish. Scientific Reports. 1-13.
  • Show author(s) (2021). SeeCiTe: a method to assess CNV calls from SNP arrays using trio data. Bioinformatics. 1876-1883.
  • Show author(s) (2021). Repeated bronchoscopy in health and obstructive lung disease: is the airway microbiome stable? BMC Pulmonary Medicine. 1-12.
  • Show author(s) (2021). Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data.
  • Show author(s) (2021). Episode 2 - Inge Jonassen forklarer sammenheng mellom kunstig intelligens og proteinfolding.
  • Show author(s) (2021). Det er arbeidskrevende å gjøre data delbare! Khrono.no.
  • Show author(s) (2021). Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data. BMC Genomics. 15 pages.
  • Show author(s) (2021). A novel approach to co-expression network analysis identifies modules and genes relevant for moulting and development in the Atlantic salmon louse (Lepeophtheirus salmonis). BMC Genomics. 25 pages.
  • Show author(s) (2020). Using Deep Learning to Extrapolate Protein Expression Measurements. Proteomics.
  • Show author(s) (2020). Reconstructing ribosomal genes from large scale total RNA meta-transcriptomic data. Bioinformatics. 3365-3371.
  • Show author(s) (2020). ReCodLiver0.9: Overcoming Challenges in Genome-Scale Metabolic Reconstruction of a Non-model Species. Frontiers in Molecular Biosciences. 10 pages.
  • Show author(s) (2020). Quantitative transcriptomics, and lipidomics in evaluating ovarian developmental effects in Atlantic cod (Gadus morhua) caged at a capped marine waste disposal site. Environmental Research. 1-11.
  • Show author(s) (2020). På tide å sette av fem prosent av prosjektmidlene til datahåndtering? . Khrono.no.
  • Show author(s) (2020). Proteogenomics of Non-smoking Lung Cancer in East Asia Delineates Molecular Signatures of Pathogenesis and Progression. Cell. 226-244.e17.
  • Show author(s) (2020). Metagenome-assembled genome distribution and key functionality highlight importance of aerobic metabolism in Svalbard permafrost. FEMS Microbiology Ecology. 13 pages.
  • Show author(s) (2020). How open databases turn out to be crucial in the fight against Covid-19. NBS-nytt. 38-43.
  • Show author(s) (2020). Evaluation of a eukaryote phylogenetic microarray for environmental monitoring of marine sediments. Marine Pollution Bulletin. 1-9.
  • Show author(s) (2020). Common gene expression signatures in Parkinson’s disease are driven by changes in cell composition. Acta neuropathologica communications. 1-14.
  • Show author(s) (2020). Application of quantitative transcriptomics in evaluating the ex vivo effects of per- and polyfluoroalkyl substances on Atlantic cod (Gadus morhua) ovarian physiology. Science of the Total Environment. 1-11.
  • Show author(s) (2020). A multi-omics approach to study Ppar-mediated regulation of lipid metabolism in Atlantic cod (Gadus morhua).

More information in national current research information system (CRIStin)

For publications, see also my https://scholar.google.com/citations?user=RqvFRN4AAAAJ&hl=no&oi=ao