Inferring Positive Selection in Large Viral Datasets
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.
Keywords
Viruses Positively selected amino acid sites Big dataNotes
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).
References
- 1.Twiddy, S.S., Woelk, C.H., Holmes, E.C.: Phylogenetic evidence for adaptive evolution of dengue viruses in nature. J. Gen. Virol. 83, 1679–1689 (2002). https://doi.org/10.1099/0022-1317-83-7-1679CrossRefGoogle Scholar
- 2.Woelk, C.H., Holmes, E.C.: Variable immune-driven natural selection in the attachment (G) glycoprotein of respiratory syncytial virus (RSV). J. Mol. Evol. 52, 182–192 (2001)CrossRefGoogle Scholar
- 3.Woelk, C.H., Jin, L., Holmes, E.C., Brown, D.W.G.: Immune and artificial selection in the haemagglutinin (H) glycoprotein of measles virus. J. Gen. Virol. 82, 2463–2474 (2001). https://doi.org/10.1099/0022-1317-82-10-2463CrossRefGoogle Scholar
- 4.Yang, Z.H.: PAML 4: Phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007). https://doi.org/10.1093/molbev/msm088CrossRefGoogle Scholar
- 5.Murrell, B., Moola, S., Mabona, A., Weighill, T., Sheward, D., Kosakovsky-Pond, S.L., Scheffler, K.: FUBAR: a fast, unconstrained bayesian approximation for inferring selection. Mol. Biol. Evol. 30, 1196–1205 (2013). https://doi.org/10.1093/molbev/mst030CrossRefGoogle Scholar
- 6.Shriner, D., Nickle, D.C., Jensen, M.A., Mullins, J.I.: Potential impact of recombination on sitewise approaches for detecting positive natural selection. Genet. Res. 81, 115–121 (2003)CrossRefGoogle Scholar
- 7.Anisimova, M., Nielsen, R., Yang, Z.H.: Effect of recombination on the accuracy of the likelihood method for detecting positive selection at amino acid sites. Genetics 164, 1229–1236 (2003)Google Scholar
- 8.Wilson, D.J., McVean, G.: Estimating diversifying selection and functional constraint in the presence of recombination. Genetics 172, 1411–1425 (2006). https://doi.org/10.1534/genetics.105.044917CrossRefGoogle Scholar
- 9.Reboiro-Jato, D., Reboiro-Jato, M., Fdez-Riverola, F., Vieira, C.P., Fonseca, N.A., Vieira, J.: ADOPS–Automatic Detection Of Positively Selected Sites. J. Integr. Bioinform. 9, 200 (2012). https://doi.org/10.2390/biecoll-jib-2012-200CrossRefGoogle Scholar
- 10.Vázquez, N., Vieira, C.P., Amorim, B.S.R., Torres, A., López-Fernández, H., Fdez-Riverola, F., Sousa, J.L.R., Reboiro-Jato, M., Vieira, J.: Large scale analyses and visualization of adaptive amino acid changes projects. Interdiscip. Sci. 10, 24–32 (2018). https://doi.org/10.1007/s12539-018-0282-7CrossRefGoogle Scholar
- 11.López-Fernández, H., Duque, P., Henriques, S., Vázquez, N., Fdez-Riverola, F., Vieira, C.P., Reboiro-Jato, M., Vieira, J.: Bioinformatics protocols for quickly obtaining large-scale data sets for phylogenetic inferences. Interdiscip. Sci. 11, 1–9 (2019). https://doi.org/10.1007/s12539-018-0312-5CrossRefGoogle Scholar
- 12.Bruen, T.C., Philippe, H., Bryant, D.: A simple and robust statistical test for detecting the presence of recombination. Genetics 172, 2665–2681 (2006). https://doi.org/10.1534/genetics.105.048975CrossRefGoogle Scholar
- 13.Kloepper, T.H., Huson, D.H.: Drawing explicit phylogenetic networks and their integration into SplitsTree. BMC Evol. Biol. 8, 22 (2008). https://doi.org/10.1186/1471-2148-8-22CrossRefGoogle Scholar
- 14.Edgar, R.C.: MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004). https://doi.org/10.1093/nar/gkh340CrossRefGoogle Scholar
- 15.Ronquist, F., Huelsenbeck, J.P.: MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003)CrossRefGoogle Scholar
- 16.Weaver, S., Shank, S.D., Spielman, S.J., Li, M., Muse, S.V., Pond, S.L.K.: Datamonkey 2.0: a modern web application for characterizing selective and other evolutionary processes. Mol. Biol. Evol. 35, 773–777 (2018). https://doi.org/10.1093/molbev/msx335CrossRefGoogle Scholar
- 17.Sievers, F., Higgins, D.G.: Clustal Omega for making accurate alignments of many protein sequences. Protein Sci. 27, 135–145 (2018). https://doi.org/10.1002/pro.3290CrossRefGoogle Scholar
- 18.Price, M.N., Dehal, P.S., Arkin, A.P.: FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010). https://doi.org/10.1371/journal.pone.0009490CrossRefGoogle Scholar
- 19.Liu, K., Linder, C.R., Warnow, T.: RAxML and FastTree: comparing two methods for large-scale maximum likelihood phylogeny estimation. PLoS ONE 6, e27731 (2011). https://doi.org/10.1371/journal.pone.0027731CrossRefGoogle Scholar