Finding Teams in Graphs and Its Application to Spatial Gene Cluster Discovery

  • Tizian Schulz
  • Jens Stoye
  • Daniel DoerrEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10562)


Gene clusters are sets of genes in a genome with associated functionality. Often, they exhibit close proximity to each other on the chromosome which can be beneficial for their common regulation. A popular strategy for finding gene clusters is to exploit the close proximity by identifying sets of genes that are consistently close to each other on their respective chromosomal sequences across several related species.

Yet, even more than gene proximity on linear DNA sequences, the spatial conformation of chromosomes may provide a pivotal indicator for common regulation and/or associated function of sets of genes.

We present the first gene cluster model capable of handling spatial data. Our model extends a popular computational model for gene cluster prediction, called \(\delta \) -teams, from sequences to general graphs. In doing so, \(\delta \)-teams are single-linkage clusters of a set of shared vertices between two or more undirected weighted graphs such that the largest link in the cluster does not exceed a given threshold \(\delta \) in any input graph.

We apply our model to human and mouse data to find spatial gene clusters, i.e., gene sets with functional associations that exhibit close neighborhood in the spatial conformation of the chromosome across species.


Spatial gene cluster Gene teams Single-linkage clustering Graph teams Hi-C data 



We are very grateful to Krister Swenson for kindly providing the Hi-C data used in this study and for his many valuable suggestions. We wish to thank Pedro Feijão for many fruitful discussions in the beginning of this project. This work was partially supported by DFG GRK 1906/1.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Technology and CeBiTecBielefeld UniversityBielefeldGermany
  2. 2.International Research Training Group 1906 “Computational Methods for the Analysis of the Diversity and Dynamics of Genomes”Bielefeld UniversityBielefeldGermany

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