Hvidsten research group


Courses



  • Introduction to Biotechnology and Chemistry (BIO101) [2012-2018]

  • Introduction to Biology - bioinformatics part (BIO140) [2018]


  • NORBIS: Bioinformatics for functional metagenomics [2017]
  • Selected topics in genomics (BIN310) [2013]
  • Mathematical Statistics (STAT250) [2012]


Umeå University

  • TC4F Research School [2011]
  • Basic Bioinformatics [2010 - 2011]
  • Computational Life Science [2009 - 2012]
  • Multivarate Data Analysis with Biological and Medical Applications [2009-2011]
  • Computer Programming Languages and Algorithms in Bioinformatics [2009-2010]
  • Functional Genomics [2009 - 2012, 2014| Slides 1 | Slides 2]


Uppsala University


Supervised theses

PhD theses


  1. Gareth Gillard. Evolution of gene expression following the whole genome duplication in salmonid fish. PhD thesis, NMBU, 2019.
  2. Lars Grønvold. Investigating evolution of gene regulation with cross-species comparative transcriptomics. PhD thesis, NMBU, 2017.
  3. David Sundell. Novel resources enableing comparative regulomics in forest tree species. PhD thesis, UmU, 2017.
  4. Niklas Mähler. Gene regulation and the emergence of phenotypes – a network approach. PhD thesis, NMBU, 2016.


      Co-supervisor

  1. Francesco Delogu. Back to basics: simplifying microbial communities to decrypt complex interactions. PhD thesis, NMBU, 2020.

  2. Tom Harvey. Towards a genome wide understanding og salmon lipid metabolism gene regulation across tissues and life stages. PhD thesis, NMBU, 2019.
  3. Marian Schubert. Evolution of adaptations to temperate climate in the grass subfamily Pooideae. PhD thesis, NMBU 2016.
  4. Ogonna Obudulu. Combined profiling in aspen: a systems biology approach. PhD thesis, SLU, 2015.
  5. Helena Strömbergsson. Chemogenomics: Models of Protein-Ligand Interaction Space. PhD thesis, UU, 2009.


Master theses


  1. Marius Strand. Exploring host-microbiome interactions in Norwegian Salmon via weighted network analysis. Master thesis, NMBU, 2019.
  2. Cathrine Horntvedt Kristiansen. The effect of chromatin structure on duplicate gene expression in Atlantic salmon. Master thesis, NMBU, 2019.
  3. Eivind Kjeka Broen. Comparison between gene expression and protein abundance in Populus tremula wood development. Master thesis, NMBU, 2019.
  4. Katrine Hånes Kirste. Optimizing Transcriptome Analysis using Short-Read RNA-Seq in Atlantic Salmon. Master thesis, NMBU, 2016.
  5. Ine Birgitta Hallsberg. Homeolog Regulation in Hexaploid Wheat. Master thesis, NMBU, 2016.
  6. Yonatan Ayalew Mekonnen. Evaluation of GWAS Method Performance Focusing on Population Stratification and Cryptic Relatedness. Master thesis, NMBU, 2015.
  7. Xiao Nie. Filtering the co-expression networks of populus trichocarpa. Master thesis, NMBU, 2014.
  8. Jonas Christoffer Lindstrøm. Comparative analysis of plant regulatory genomes. Master thesis, NMBU, 2014.
  9. Niklas Mähler. Computational prediction of gene regulation in Synechocystis sp. PCC6803. Master thesis, UmU, 2012. Paper in PLoS ONE.
  10. Patrik Björkholm. Method for recognizing local descriptors of protein structures using Hidden Markov Models, Master thesis, UU, 2008. Paper in Bioinformatics.
  11. Minyan Hong. Fold recognition using local descriptors of protein structure and Hidden Markov Models, Student project (10 points), 2006 and Master thesis, UU, 2007.
  12. Marta Luksza. A System for Predicting Protein Function from Structure, Master thesis, UU, 2005.


Other student theses


  1. Peter Boman and Daniel Decker. Visualisation of gene coexpression networks, Applied functional genomics, 7.5 ECTS, UmU, 2009.
  2. Johan Alexander Källberg Zvrskovec. Protein structure prediction by search among combinations of reoccurring structural components, Student project (10 points), UU, 2008.
  3. Min Jia. Proteochemometrics modeling of carbonic anhydrase-ligand interactions using rule-based and linear methods, Student project (5 points), UU, 2005.
  4. Anna Hennecke. Predicting protein function from its 3-dimensional structure - Biological validation of protein function predictions, Student project (10 points), UU, 2005.


Teaching links


Introduction to bioinformatics and machine learning


Introduction to rough sets and rule-based learning


The ROSETTA system


Rough sets and ROSETTA exercises (solutions)


A tutorial-based guide to the ROSETTA system


Perl tutorials


Learn R


Modern Statistics for Modern Biology


Docent lecture: Is biology more than stamp collecting?


Blog: DNAtur


Giving a seminar at

Institute of Biotechnology, Vilnius University.