Abstract
Referential compression is one of the fundamental operations for storing and analyzing DNA data. The models that incorporate relative compression, a special case of referential compression, are being steadily improved, namely those which are based on Markov models. In this paper, we propose a new model, the substitutional tolerant Markov model (STMM), which can be used in cooperation with regular Markov models to improve compression efficiency. We assessed its impact on synthetic and real DNA sequences, showing a substantial improvement in compression, while only slightly increasing the computation time. In particular, it shows high efficiency in modeling species that have split less than 40 million years ago.
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Acknowledgments
This work was partially funded by FEDER (POFC-COMPETE) and by National Funds through the FCT - Foundation for Science and Technology, in the context of the projects UID/CEC/00127/2013 and PTCD/EEI-SII/6608/2014.
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Pratas, D., Hosseini, M., Pinho, A.J. (2017). Substitutional Tolerant Markov Models for Relative Compression of DNA Sequences. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., Pinto, T. (eds) 11th International Conference on Practical Applications of Computational Biology & Bioinformatics. PACBB 2017. Advances in Intelligent Systems and Computing, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-60816-7_32
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DOI: https://doi.org/10.1007/978-3-319-60816-7_32
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