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Automated Collection and Sharing of Adaptive Amino Acid Changes Data

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11th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 616))

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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.

N. Vázquez, C.P. Vieira and B.S.R. Amorim—These authors contributed equally to this work.

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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.

Author information

Authors and Affiliations

  1. ESEI – Escuela Superior de Ingeniería Informática, Edificio Politécnico Universidade de Vigo, Campus Universitario as Lagoas s/n, 32004, Ourense, Spain

    Noé Vázquez, Hugo López-Fernández, Florentino Fdez-Riverola & Miguel Reboiro-Jato

  2. CINBIO - Centro de Investigaciones Biomédicas, University of Vigo, Campus Universitario Lagoas-Marcosende, 36310, Vigo, Spain

    Noé Vázquez, Hugo López-Fernández, Florentino Fdez-Riverola & Miguel Reboiro-Jato

  3. Instituto de Investigação e Inovação em Saúde (I3S), Universidade do Porto, Rua Alfredo Allen, 208, 4200-135, Porto, Portugal

    Cristina P. Vieira, Bárbara S. R. Amorim, André Torres, José L. R. Sousa & Jorge Vieira

  4. Instituto de Biologia Molecular e Celular (IBMC), Rua Alfredo Allen, 208, 4200-135, Porto, Portugal

    Cristina P. Vieira, José L. R. Sousa & Jorge Vieira

  5. Instituto Nacional de Engenharia Biomédica (INEB), Rua Alfredo Allen, 208, 4200-135, Porto, Portugal

    Bárbara S. R. Amorim & André Torres

  6. UCIBIO-REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516, Lisbon, Portugal

    Hugo López-Fernández

Authors
  1. Noé Vázquez
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  2. Cristina P. Vieira
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  3. Bárbara S. R. Amorim
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  4. André Torres
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  5. Hugo López-Fernández
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  6. Florentino Fdez-Riverola
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  7. José L. R. Sousa
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  8. Miguel Reboiro-Jato
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  9. Jorge Vieira
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Corresponding author

Correspondence to Jorge Vieira .

Editor information

Editors and Affiliations

  1. Escuela Superior de Ingeniería Informática, Universidad de Vigo , Ourense, Spain

    Florentino Fdez-Riverola

  2. Faculty of Computing, Universiti Teknologi Malaysia , Johor, Johor, Malaysia

    Mohd Saberi Mohamad

  3. Department de Informática, Universidade do Minho , Braga, Portugal

    Miguel Rocha

  4. Departamento de Informática y Automática, Universidad de Salamanca , Salamanca, Spain

    Juan F. De Paz

  5. Departamento de Informática y Automática, Universidad de Salamanca , Salamanca, Spain

    Tiago Pinto

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Vázquez, N. et al. (2017). Automated Collection and Sharing of Adaptive Amino Acid Changes Data. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., Pinto, T. (eds) 11th International Conference on Practical Applications of Computational Biology & Bioinformatics. PACBB 2017. Advances in Intelligent Systems and Computing, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-60816-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-60816-7_3

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