Inferring DNA sequences from mechanical unzipping data: the large-bandwidth case.

Valentina Baldazzi (1, 2) , Serena Bradde (1, 2) , Simona Cocco (1) , Enzo Marinari (2) , Remi Monasson (3)

1 LPS - Laboratoire de Physique Statistique de l'ENS
2 Dipartimento di Fisica
3 LPTENS - Laboratoire de Physique Théorique de l'ENS [École Normale Supérieure]

The complementary strands of DNA molecules can be separated when stretched apart by a force; the unzipping signal is correlated to the base conte nt of the sequence but is affected by thermal and instrumental noise. We consider here the ideal case where opening events are known to a very good time resolution (very large bandwidth), and study how the sequence can be reconstructed from the unzipping data. Our approach relies on the use of statistical Bayesian inference and of Viterbi decoding algorithm. Performances are studied numerically on Monte Carlo generated data, and analytically. We show how multiple unzippings of the same molecule may be exploited to improve the quality of the prediction, and calculate analytically the number of required unzippings as a function of the bandwidth, the sequence content, the elasticity parameters of the unzipped strands.

Domaines: Biologie structurale [q-bio.BM]; Mécanique statistique [cond-mat.stat-mech]

Valentina Baldazzi, Serena Bradde, Simona Cocco, Enzo Marinari, Remi Monasson. Inferring DNA sequences from mechanical unzipping data: the large-bandwidth case.. Physical Review E : Statistical, Nonlinear, and Soft Matter Physics, 2007, 75, pp.011904. ⟨hal-00142549⟩