computational genomics

besenbacher group

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. In particular we are interested in developing new methods to detect whether an individual has cancer based on sequence data from cell-free DNA (cfDNA).

Søren Besenbacher at Google Scholar


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.

Bergeron LA et al. 2022, eLife11:e73577.

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.
Bethune J and Kleppe A et al. 2021. In press: bioRxiv.

Developing methods for analyzing cfDNA data

Cell-free DNA (or cfDNA) are DNA fragments floating in the bloodstream outside of cells. Usually, these fragments come from dead blood cells, but there are interesting exceptions.
In pregnant women, a fraction of the cells will originate from the fetus and thus provide a non-invasive opportunity for early detection of genetic abnormalities. In cancer patients, the presence of circulating tumor DNA (ctDNA) among the cfDNA offers a cheap and non-invasive strategy to detect and monitor cancer.
We are currently working several new methods to detect the presence ctDNA. This includes methods that detect ctDNA based on the presence of tumor mutations as well as methods that use “fragmentomics” features to detect ctDNA. The idea of fragmentomics is that cfDNA is fragmented in vivo by enzymes that cut the DNA in positions not bound by nucleosomes, which means that the length and position of cfDNA fragments provide information about the nucleosome position and chromatin organization of the cells they come from. This information can, in turn, reveal what types of cells the fragments come from and which genes and transcription factors were active in those cells.

Renauld G et al. 2022, eLife11:e71569.


kmerPaPa - Tool to calculate a "k-mer pattern partition" from position specific k-mer counts.

GeNovo - Identifying disease genes with de-novo mutations.

Group leader

Søren Besenbacher

Assoc.Prof. in bioinformatics, MSc, PhD, Group leader


Carmen Oroperv

PhD student, MSc

Ludvig Renbo Olsen

PhD student, MSc

Oliver Kjærlund Hansen

PhD student, MSc

Søren Besenbacher

Assoc.Prof. in bioinformatics, MSc, PhD, Group leader

Vinod Kumar Singh


Yixin Lin

PhD student, MSc


Novo Nordisk Fondens Data Science Collaborative Research Programme 2021

Mutational processes in spermatogenesis and their consequences for human health.

Danish National Center for Circulating Tumor DNA Guided Cancer Treatment (ctDNA Center)

The DCCC ctDNA Research Center is a national founded research center for ctDNA guided cancer treatment. We develop clinical protocols and provide critical infrastructure for conducting ctDNA guided treatments trials. The objective of the center is to provide a framework for faster clinical implementation of ctDNA guided treatment decisions.
The center is managed by Center Director Professor Claus Lindbjerg Andersen and Center Vice Director Professor Lars Dyrskjøt Andersen.

SELECTED Publications

Direct estimation of mutations in great apes reconciles phylogenetic dating. Besenbacher S, Hvilsom C, Marques-Bonet T, Mailund T, Schierup MH. Nat Ecol Evol. 2019 Feb;3(2):286-292. doi: 10.1038/s41559-018-0778-x. Epub 2019 Jan 21. Erratum in: Nat Ecol Evol. 2019 May;3(5):859. PubMed

Unsupervised detection of fragment length signatures of circulating tumor DNA using non-negative matrix factorization. Renaud G, Nørgaard M, Lindberg J, Grönberg H, De Laere B, Jensen JB, Borre M, Andersen CL, Sørensen KD, Maretty L, Besenbacher S.  Elife. 2022 Jul 27;11:e71569. doi: 10.7554/eLife.71569. PubMed

A method to build extended sequence context models of point mutations and indels. Bethune J, Kleppe A, Besenbacher S. Nat Commun. 2022 Dec 22;13(1):7884. doi: 10.1038/s41467-022-35596-5. PMID: 36550134; PMCID: PMC9780256. PubMed


Carmen Oroperv
A reference-free strategy for detecting circulating tumor DNA
Anika Gottschalk
A reference-free strategy for detecting circulating tumor DNA
Andrea Zauli
Detection of circulating tumor DNA using structural variants


skou Pedersen Group
computational genomics & transcriptomics
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Birkbak Group
cancer evolution
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