Abstract
Amino acid sequences are known to be very hard to compress. In this paper, we propose a lossless compressor for efficient compression of amino acid sequences (AC). The compressor uses a cooperation between multiple context and substitutional tolerant context models. The cooperation between models is balanced with weights that benefit the models with better performance, according to a forgetting function specific for each model. We have shown consistently better compression results than other approaches, using low computational resources. The compressor implementation is freely available, under license GPLv3, at https://github.com/pratas/ac.
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Acknowledgments
This work was partially funded by the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 305444 “RD-Connect: An integrated platform connecting registries, biobanks and clinical bioinformatics for rare disease research”. It was also funded by FEDER (Programa Operacional Factores de Competitividade - COMPETE) and by National Funds through the FCT - Foundation for Science and Technology, in the context of the UID/CEC/00127/2013 and PTCD/EEI-SII/6608/2014.
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Pratas, D., Hosseini, M., Pinho, A.J. (2019). Compression of Amino Acid Sequences. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., González, P. (eds) Practical Applications of Computational Biology and Bioinformatics, 12th International Conference. PACBB2018 2018. Advances in Intelligent Systems and Computing, vol 803. Springer, Cham. https://doi.org/10.1007/978-3-319-98702-6_13
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DOI: https://doi.org/10.1007/978-3-319-98702-6_13
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