The group of Søren Besenbacher focuses on using statistical and computational approaches to study questions in human genomics.
One of the groups primary research interests is the human germline mutation process where we want to understand the rate, pattern and effects of new mutations entering the human population.
We are also interested in developing machine learning methods that can be used in precision medicine.
Modelling the human mutation rate
Mutation of the DNA molecule is a truly fundamental process in biology. It occurs in all species and is the ultimate source of all genetic variation.
It has been known for some time that the mutation varies across the genome, but previously it was hard to get an unbiased estimate of the human mutation rate and to study the causes of the rate variation. The advent of cheap Whole Genome Sequencing (WGS) has, however, alleviated this problem. By sequencing nuclear families with high coverage we can directly observe new mutations that are present in a child but absent in the parents. Using such new data sets of directly observed de novo mutations it is now possible to study the human germline mutation process without bias from selection and other confounding factors. We are involved in using such data sets to:
- Estimate the rate of mutations in humans and other primates.
- Finding genomic factors that affect the mutation rate.
- Build predictive models that can estimate the probability that a certain kind of mutation happens at a specific site in the human genome.
- Examine the evolution of the mutation rate and spectrum across and within species.
- S. Besenbacher, C. Hvilsom, T. Marques-Bonet, T. Mailund, M.H. Schierup. Direct estimation of mutations in great apes reveals significant recent human slowdown in the yearly mutation rate. bioRxiv, 287821 (2018)
- Maretty L, Jensen JM, Petersen B, Sibbesen JA, Liu S, Villesen P, Skov L, Belling K, Theil Have C, Izarzugaza JMG, Grosjean M, Bork-Jensen J, Grove J, Als TD, Huang S, Chang Y, Xu R, Ye W, Rao J, Guo X, Sun J, Cao H, Ye C, van Beusekom J, Espeseth T, Flindt E, Friborg RM, Halager AE, Le Hellard S, Hultman CM, Lescai F, Li S, Lund O, Løngren P, Mailund T, Matey-Hernandez ML, Mors O, Pedersen CNS, Sicheritz-Pontén T, Sullivan P, Syed A, Westergaard D, Yadav R, Li N, Xu X, Hansen T, Krogh A, Bolund L, Sørensen TIA, Pedersen O, R Gupta, S Rasmussen, S Besenbacher, A. D. Børglum, J Wang, H Eiberg, K Kristiansen, S Brunak, M. H. Schierup. Sequencing and de novo assembly of 150 genomes from Denmark as a population reference. Nature.; 548(7665):87–91. (2017)
- S. Besenbacher, P. Sulem, A. Helgason, H. Helgason, H. Kristjansson, A. Jonasdottir, A Jonasdottir, O. Magnusson, U. Thorsteinsdottir, G. Masson, A. Kong, D. Gudbjartsson, K. Stefansson, “Multi-nucleotide de novo Mutations in Humans, PLOS Genetics, vol. 12no. 11, p. e1006315, (2016)
- S. Besenbacher, S. Liu, J. Izarzugaza, J. Grove, K. Belling, J. Bork-Jensen, S. Huang, T. D. Als, S. Li, R. Yadav, A. Rubio-García, F. Lescai, D. Demontis, J. Rao, W. Ye, T. Mailund, R. M. Friborg, C. N. S. Pedersen, R. Xu, J. Sun, H. Liu, O. Wang, X. Chen, D. Flores, E. Rydza, K. Rapacki, J. D. Sørensen, P. Chmura, D. Westergaard, P. Dworzynski, T. I. A. Sørensen, O. Lund, T. Hansen, X. Xu, N. Li, L. Bolund, O. Pedersen, H. Eiberg, A. Krogh, A. D. Børglum, S. Brunak, K. Kristiansen, M. H. Schierup, J. Wang, R. Gupta, P. Villesen, S. Rasmussen, “ Novel variation and de novo mutation rates in population-wide de novo-assembled Danish trios”. Nature Communications, vol 6, no. 5969, (2015)
- A. Kong, M. L. Frigge, G. Masson, S. Besenbacher, P. Sulem, G. Magnusson, S. A. Gudjonsson, A. Sigurdsson, A. Jonasdottir, A. Jonasdottir, W. S. W. Wong, G. Sigurdsson, G. B. Walters, S. Steinberg, H. Helgason, G. Thorleifsson, D. F. Gudbjartsson, A. Helgason, O. T. Magnusson, U. Thorsteinsdottir, and K. Stefansson, “Rate of de novo mutations and the importance of father’s age to disease risk,” Nature, vol. 488, no. 7412, pp. 471–475, (2012)
Classification of cancer samples
Cancers of unknown primary (CUP)
Cancers are named based on their primary site: the type of tissue where the cancer originated. A lung cancer that has metastasised (spread) to the liver will for example still be labelled as a lung cancer and not a liver cancer. In 3-5% of cancer cases doctors only find a metastasis but fail to locate the original tumour; these are called “Cancers of Unknown Primary” (CUP). Because the standard treatment depends on the cancer type such CUP cases are generally harder to treat and consequently have significantly worse survival compared to the average cancer patient.
We are currently working on creating better methods that can use molecular data from the metastasis to predict the site of the primary tumor.
Apart from classification of CUP samples we are also interested in developing other kinds of classification methods based on molecular data from tumor samples that can help doctors make the right treatment choice and thus improve the clinical outcome.
- Søndergaard D, Nielsen S, Pedersen CNS, Besenbacher S. Prediction of Primary Tumors in Cancers of Unknown Primary. Journal of Integrative Bioinformtics. 7;14(2). (2017)
- Lindahl LM, Besenbacher S, Rittig AH, Celis P, Willerslev-Olsen A, Gjerdrum LMR, Krejsgaard T, Johansen C, Litman T, Woetmann A, Odum N, Iversen L. Prognostic miRNA classifier in early-stage mycosis fungoides: development and validation in a Danish nationwide study. Blood. Vol. 131, p. 759-770. (2018)