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Bioinformatics. 2013 Oct 1;29(19):2380-6. doi: 10.1093/bioinformatics/btt404. Epub 2013 Jul 11.

Modeling nucleosome position distributions from experimental nucleosome positioning maps.

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Institute for Applied Computer Science, University of Applied Sciences Stralsund, Zur Schwedenschanze 15, Stralsund 18435, Germany and Deutsches Krebsforschungszentrum (DKFZ) & BioQuant, Im Neuenheimer Feld 280, Heidelberg 69120, Germany.



Recent experimental advancements allow determining positions of nucleosomes for complete genomes. However, the resulting nucleosome occupancy maps are averages of heterogeneous cell populations. Accordingly, they represent a snapshot of a dynamic ensemble at a single time point with an overlay of many configurations from different cells. To study the organization of nucleosomes along the genome and to understand the mechanisms of nucleosome translocation, it is necessary to retrieve features of specific conformations from the population average.


Here, we present a method for identifying non-overlapping nucleosome configurations that combines binary-variable analysis and a Monte Carlo approach with a simulated annealing scheme. In this manner, we obtain specific nucleosome configurations and optimized solutions for the complex positioning patterns from experimental data. We apply the method to compare nucleosome positioning at transcription factor binding sites in different mouse cell types. Our method can model nucleosome translocations at regulatory genomic elements and generate configurations for simulations of the spatial folding of the nucleosome chain.


Source code, precompiled binaries, test data and a web-based test installation are freely available at

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