Abstract
We propose a new algorithm to estimate the 3d configuration of a chromatin chain from the contact frequency data provided by hi-c experiments. Since the data originate from a population of cells, we rather aim at obtaining a set of structures that are compatible with both the data and our prior knowledge. Our method overcomes some drawbacks presented by other state-of-the-art methods, including the problems related to the translation of contact frequencies into Euclidean distances. Indeed, such a translation always produces a geometrically inconsistent distance set. Our multiscale chromatin model and our probabilistic solution approach allow us to partition the problem, thus speeding up the solution, to include suitable constraints, and to get multiple feasible structures. Moreover, the density function we use to sample the solution space does not require any translation from contact frequencies into distances.
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Acknowledgements
This work has been funded by the Italian Ministry of Education, University and Research, and by the National Research Council of Italy, Flagship Project InterOmics, PB.P05. The authors are indebted to Luigi Bedini and Aurora Savino for helpful discussions.
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Caudai, C., Salerno, E., Zoppè, M., Tonazzini, A. (2015). A Statistical Approach to Infer 3d Chromatin Structure. In: Zazzu, V., Ferraro, M., Guarracino, M. (eds) Mathematical Models in Biology. Springer, Cham. https://doi.org/10.1007/978-3-319-23497-7_12
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DOI: https://doi.org/10.1007/978-3-319-23497-7_12
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