Automated Collection and Sharing of Adaptive Amino Acid Changes Data

  • Noé Vázquez
  • Cristina P. Vieira
  • Bárbara S. R. Amorim
  • André Torres
  • Hugo López-Fernández
  • Florentino Fdez-Riverola
  • José L. R. Sousa
  • Miguel Reboiro-Jato
  • Jorge Vieira
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 616)

Abstract

When changes at few amino acid sites are the target of selection, adaptive amino acid changes in protein sequences can be identified using maximum-likelihood methods based on models of codon substitution (such as codeml). Such methods have been used numerous times using a variety of different organisms but the time needed to collect the data and prepare the input files means that tens or a couple of hundred coding regions are usually analyzed. Nevertheless, the recent availability of flexible and ease to use computer applications to collect the relevant data (such as BDBM), and infer positively selected amino acid sites (such as ADOPS) means that the whole process is easier and quicker than before, but the lack of a batch option in ADOPS, here reported, still precluded the analysis of hundreds or thousands of sequence files. Given the interest and possibility of running such large scale projects, we also developed a database where ADOPS projects can be stored. Therefore, here we also present B+ that is both a data repository and a convenient interface to look at the information contained in ADOPS projects without the need to download and unzip the corresponding ADOPS project file. The ADOPS projects available at B+ can also be downloaded, unzipped, and opened using the ADOPS graphical interface. The availability of such a database ensures results repeatability, promotes data reuse with significant savings on the time needed for preparing datasets, and allows further exploration of the data contained in ADOPS projects effortlessly.

Keywords

ADOPS Positive selection B+ database Open data 

Notes

Acknowledgements

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). This work has been also funded by the “Platform of integration of intelligent techniques for analysis of biomedical information” project (TIN2013-47153-C3-3-R) from Spanish Ministry of Economy and Competitiveness. SING group thanks CITI (Centro de Investigación, Transferencia e Innovación) from University of Vigo for hosting its IT infrastructure. H. López-Fernández is supported by a post-doctoral fellowship from Xunta de Galicia.

References

  1. 1.
    Yang, Z.H., Nielsen, R.: Synonymous and nonsynonymous rate variation in nuclear genes of mammals. J. Mol. Evol. 46(4), 409–418 (1998)CrossRefGoogle Scholar
  2. 2.
    Yang, Z.H.: PAML: a program package for phylogenetic analysis by maximum likelihood. Comput. Appl. Biosci. 13(5), 555–556 (1997)Google Scholar
  3. 3.
    Yang, Z.H.: PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24(8), 1586–1591 (2007)CrossRefGoogle Scholar
  4. 4.
    Li, M., Huang, L., Li, K.Q., Huo, Y.X., Chen, C.H., Wang, J.K., Liu, J.W., Luo, Z.W., Chen, C.S., Dong, Q., et al.: Adaptive evolution of interleukin-3 (IL3), a gene associated with brain volume variation in general human populations. Hum. Genet. 135(4), 377–392 (2016)CrossRefGoogle Scholar
  5. 5.
    Muto, Y., Guindon, S., Umemura, T., Kohidai, L., Ueda, H.: Adaptive evolution of formyl peptide receptors in mammals. J. Mol. Evol. 80(2), 130–141 (2015)CrossRefGoogle Scholar
  6. 6.
    Zhang, S., Gao, B., Zhu, S.: Target-driven evolution of scorpion toxins. Sci. Rep. 5 (2015). Article No: 14973, doi:10.1038/srep14973
  7. 7.
    Finseth, F.R., Dong, Y.Z., Saunders, A., Fishman, L.: Duplication and adaptive evolution of a key centromeric protein in Mimulus, a genus with female meiotic drive. Mol. Biol. Evol. 32(10), 2694–2706 (2015)CrossRefGoogle Scholar
  8. 8.
    Zhang, Y., Yu, Z.N.: The first evidence of positive selection in peptidoglycan recognition protein (PGRP) genes of Crassostrea gigas. Fish Shellfish Immun. 34(5), 1352–1355 (2013)CrossRefGoogle Scholar
  9. 9.
    Jiggins, F.M., Kim, K.W.: A screen for immunity genes evolving under positive selection in Drosophila. J. Evol. Biol. 20(3), 965–970 (2007)CrossRefGoogle Scholar
  10. 10.
    Morales-Hojas, R., Vieira, C.P., Reis, M., Vieira, J.: Comparative analysis of five immunity-related genes reveals different levels of adaptive evolution in the virilis and melanogaster groups of Drosophila. Heredity 102(6), 573–578 (2009)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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(2), 182–192 (2001)CrossRefGoogle Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    Shen, J., Kirk, B.D., Ma, J.P., Wang, Q.H.: Diversifying selective pressure on influenza B virus Hemagglutinin. J. Med. Virol. 81(1), 114–124 (2009)CrossRefGoogle Scholar
  15. 15.
    Yang, W., Bielawski, J.P., Yang, Z.H.: Widespread adaptive evolution in the human immunodeficiency virus type 1 genome. J. Mol. Evol. 57(2), 212–221 (2003)CrossRefGoogle Scholar
  16. 16.
    Gu, M., Liu, W.J., Xu, L.J., Cao, Y.Z., Yao, C.F., Hu, S.L., Liu, X.F.: Positive selection in the hemagglutinin-neuraminidase gene of Newcastle disease virus and its effect on vaccine efficacy. Virol. J. 8, 150 (2011)CrossRefGoogle Scholar
  17. 17.
    Emes, R.D., Yang, Z.H.: Duplicated paralogous genes subject to positive selection in the genome of Trypanosoma brucei. Plos One 3(5), e2295 (2008)CrossRefGoogle Scholar
  18. 18.
    Lu, J., Zheng, J.Z., Xu, Q.G., Chen, K.P., Zhang, C.Y.: Adaptive evolution of the vertebrate skeletal muscle sodium channel. Genet. Mol. Biol. 34(2), 323–328 (2011)CrossRefGoogle Scholar
  19. 19.
    Khan, M.M.G., Ryden, A.M., Chowdhury, M.S., Hasan, M.A., Kazi, J.U.: Maximum likelihood analysis of mammalian p53 indicates the presence of positively selected sites and higher tumorigenic mutations in purifying sites. Gene 483(1–2), 29–35 (2011)CrossRefGoogle Scholar
  20. 20.
    Sobrinho, I.S., de Brito, R.A.: Evidence for positive selection in the gene fruitless in Anastrepha fruit flies. BMC Evol. Biol. 10, 293 (2010)CrossRefGoogle Scholar
  21. 21.
    Metzger, K.J., Thomas, M.A.: Evidence of positive selection at codon sites localized in extracellular domains of mammalian CC motif chemokine receptor proteins. BMC Evol. Biol. 10, 139 (2010)CrossRefGoogle Scholar
  22. 22.
    Vieira, C.P., Charlesworth, D., Vieira, J.: Evidence for rare recombination at the gametophytic self-incompatibility locus. Heredity 91(3), 262–267 (2003)CrossRefGoogle Scholar
  23. 23.
    Nunes, M.D.S., Santos, R.A.M., Ferreira, S.M., Vieira, J., Vieira, C.P.: Variability patterns and positively selected sites at the gametophytic self-incompatibility pollen SFB gene in a wild self-incompatible Prunus spinosa (Rosaceae) population. New Phytol. 172(3), 577–587 (2006)CrossRefGoogle Scholar
  24. 24.
    Vieira, J., Morales-Hojas, R., Santos, R.A.M., Vieira, C.P.: Different positively selected sites at the gametophytic self-incompatibility pistil S-RNase gene in the Solanaceae and Rosaceae (Prunus, Pyrus, and Malus). J. Mol. Evol. 65(2), 175–185 (2007)CrossRefGoogle Scholar
  25. 25.
    Vieira, J., Santos, R.A.M., Ferreira, S.M., Vieira, C.P.: Inferences on the number and frequency of S-pollen gene (SFB) specificities in the polyploid Prunus spinosa. Heredity 101(4), 351–358 (2008)CrossRefGoogle Scholar
  26. 26.
    Anisimovam, M.: Darwin and Fisher meet at biotech: on the potential of computational molecular evolution in industry. BMC Evol. Biol. 15, 76 (2015)CrossRefGoogle Scholar
  27. 27.
    Reboiro-Jato, D., Reboiro-Jato, M., Fdez-Riverola, F., Fonseca, N.A., Vieira, J.: On the development of a pipeline for the automatic detection of positively selected sites. Adv. Intel. Soft Comput. 154, 225−+ (2012)CrossRefGoogle Scholar
  28. 28.
    Nickel, G.C., Tefft, D., Adams, M.D.: Human PAML browser: a database of positive selection on human genes using phylogenetic methods. Nucleic Acids Res. 36, D800–D808 (2008)CrossRefGoogle Scholar
  29. 29.
    Creevey, C.J., McInerney, J.O.: Clann: investigating phylogenetic information through supertree analyses. Bioinformatics 21(3), 390–392 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Noé Vázquez
    • 1
    • 2
  • Cristina P. Vieira
    • 3
    • 4
  • Bárbara S. R. Amorim
    • 3
    • 5
  • André Torres
    • 3
    • 5
  • Hugo López-Fernández
    • 1
    • 2
    • 6
  • Florentino Fdez-Riverola
    • 1
    • 2
  • José L. R. Sousa
    • 3
    • 4
  • Miguel Reboiro-Jato
    • 1
    • 2
  • Jorge Vieira
    • 3
    • 4
  1. 1.ESEI – Escuela Superior de Ingeniería InformáticaEdificio Politécnico Universidade de VigoOurenseSpain
  2. 2.CINBIO - Centro de Investigaciones BiomédicasUniversity of VigoVigoSpain
  3. 3.Instituto de Investigação e Inovação em Saúde (I3S)Universidade do PortoPortoPortugal
  4. 4.Instituto de Biologia Molecular e Celular (IBMC)PortoPortugal
  5. 5.Instituto Nacional de Engenharia Biomédica (INEB)PortoPortugal
  6. 6.UCIBIO-REQUIMTE, Departamento de Química, Faculdade de Ciências e TecnologiaUniversidade Nova de LisboaLisbonPortugal

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