Abstract
Species-specific DNA regions are segments that are unique or share high dissimilarity relatively to close species. Their discovery is important, because they allow the localization of evolutionary traits that are often related to novel functionalities and, sometimes, diseases.
We have detected distinct DNA regions specific in the modern human, when compared to a Neanderthal high-quality genome sequence obtained from a bone of a Siberian woman. The bone is around 50,000 years old and the DNA raw data totalizes more than 418 GB. Since the data size required for localizing efficiently such events is very high, it is not practical to store the model on a table or hash table. Thus, we propose a probabilistic method to map and visualize those regions. The time complexity of the method is linear. The computational tool is available at http://pratas.github.io/chester.
The results, computed in approximately two days using a single CPU core, show several regions with documented neanderthal absent regions, namely genes associated with the brain (neurotransmiters and synapses), hearing, blood, fertility and the immune system. However, it also shows several undocumented regions, that may express new functions linked with the evolution of the modern human.
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Acknowledgments
We thank Martin Kircher, for very helpful comments and explanations, and Cláudio Teixeira, for computational infrastructures. This work was 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 projects UID/CEC/00127/2013, UID/BIM/04501/2013, PTCD/EEI-SII/6608/2014 and the grant SFRH/BPD/111148/2015 to RMS.
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Pratas, D., Hosseini, M., Silva, R.M., Pinho, A.J., Ferreira, P.J.S.G. (2017). Visualization of Distinct DNA Regions of the Modern Human Relatively to a Neanderthal Genome. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_26
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DOI: https://doi.org/10.1007/978-3-319-58838-4_26
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