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Inge Jonassen's picture

Inge Jonassen

Head of Department, Head of Department of informatics
  • E-mailInge.Jonassen@uib.no
  • Phone+47 55 58 47 13+47 905 24 31690524316
  • Visitor Address
    Høyteknologisenteret i Bergen
    No5020 Bergen
    Room 
    409M3
  • 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) (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). 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). RASflow: an RNA-Seq analysis workflow with Snakemake. BMC Bioinformatics. 9 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). 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). Gene-methylation interactions: Discovering region-wise DNA methylation levels that modify SNP-associated disease risk. Clinical Epigenetics. 18 pages.
  • 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). End-to-End Data Management Toolkit for Norwegian Life Scientists.
  • 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).
  • Show author(s) (2019). dCOD 1.0: DECODING THE SYSTEMS TOXICOLOGY OF ATLANTIC COD (GADUS MORHUA) – HIGHLIGHTS SO FAR.
  • Show author(s) (2019). The chemical defensome of Atlantic cod (Gadus morhua).
  • Show author(s) (2019). THE CHEMICAL DEFENSOME OF ATLANTIC COD (GADUS MORHUA): HOW DOES IT DIFFER FROM DEFENSOME NETWORKS IN OTHER TELEOST SPECIES?
  • Show author(s) (2019). RASflow: An Easy-to-use RNA-Seq Analysis Workflow.
  • Show author(s) (2019). EFFECTS OF SELECTED ENVIRONMENTAL ESTROGENS ON EXPRESSION OF FIBROBLAST GROWTH FACTOR SIGNALING PATHWAY GENES IN THE LIVER OF ATLANTIC COD (GADUS MORHUA) IN VIVO AND EX VIVO.
  • Show author(s) (2019). A draft metabolic reconstruction of Atlantic cod (Gadus morhua) liver: how to and what for?
  • Show author(s) (2019). A draft metabolic reconstruction of Atlantic cod (Gadus morhua) liver .
  • Show author(s) (2019). A Comparative Analysis of Feature Selection Methods for Biomarker Discovery in Study of Toxicant-Treated Atlantic Cod (Gadus Morhua) Liver. 10 pages.

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