Abstract
A large amount of viral nucleotide sequences is available in databases that can be used to identify positively selected amino acid sites, and thus make inferences on which sites are important for immune system escape and adaptation to their host. Nevertheless, the software pipelines needed to analyse such large datasets usually imply long running times. Moreover, their power to identify positively selected amino acid sites may not be similar. Therefore, here we first analyse, under a variety of conditions, the performance of different software applications and then propose a protocol for the analysis of large datasets.
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Acknowledgments
This article is a result of the project Norte-01-0145-FEDER-000008 - Porto Neurosciences and Neurologic Disease Research Initiative at I3S, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). The SING group thanks the CITI (Centro de Investigación, Transferencia e Innovación) from the University of Vigo for hosting its IT infrastructure. This work was partially supported by the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding ED431C2018/55-GRC Competitive Reference Group. H. López-Fernández is supported by a post-doctoral fellowship from Xunta de Galicia (ED481B 2016/068-0).
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López-Fernández, H. et al. (2020). Inferring Positive Selection in Large Viral Datasets. In: Fdez-Riverola, F., Rocha, M., Mohamad, M., Zaki, N., Castellanos-Garzón, J. (eds) Practical Applications of Computational Biology and Bioinformatics, 13th International Conference. PACBB 2019. Advances in Intelligent Systems and Computing, vol 1005 . Springer, Cham. https://doi.org/10.1007/978-3-030-23873-5_8
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