SeqinR 1.0-2: A Contributed Package to the R Project for Statistical Computing Devoted to Biological Sequences Retrieval and Analysis

  • Delphine Charif
  • Jean R. Lobry
Part of the Biological and Medical Physics, Biomedical Engineering book series (BIOMEDICAL)

The seqinR package for the R environment is a library of utilities to retrieve and analyze biological sequences. It provides an interface between: (i) the R language and environment for statistical computing and graphics, and (ii) the ACNUC sequence retrieval system for nucleotide and protein sequence databases such as GenBank, EMBL, SWISS-PROT. ACNUC is very efficient in providing direct access to subsequences of biological interest (e.g., protein coding regions, tRNA, or rRNA coding regions) present in GenBank and in EMBL. Thanks to a simple query language, it is then easy under R to select sequences of interest and then use all the power of the R environment to analyze them. The ACNUC databases can be locally installed but they are more conveniently accessed through a web server to take advantage of centralized daily updates. The aim of this chapter is to provide a handout on basic sequence analyses under seqinR with a special focus on multivariate methods.


Genetic Code Codon Position Chlamydia Trachomatis Base Count Text Editor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    R. Ihaka, R.Gentleman, J. Comp. Graph. Stat. 3, 299 (1996)Google Scholar
  2. 2.
    R Development Core Team, R: A language and environment for statistical computing (ISBN 3-900051-00-3, 2004)
  3. 3.
    F. Leisch, Proceedings in Computational Statistics, 575 (2002) (ISBN 3-7908-1517-9)Google Scholar
  4. 4.
    K. Hornik, The R FAQ (ISBN 3-900051-08-9, 2005)
  5. 5.
    J. Keogh, Australian Patent Office application number AU 2001100012 A4 (2001).
  6. 6.
    J.R. Lobry, N. Sueoka, Genome Biology3, research0058.1(2002)
  7. 7.
    J. Buckheit, D.L. Donoho, in Wavelets and Statistics, ed. by A. Antoniadis (Springer, Berlin, New York, 1995)Google Scholar
  8. 8.
    D. Charif, J. Thioulouse, J.R. Lobry, G. Perrière, Bioinformatics 21, 545 (2005);
  9. 9.
    R. Rudner, J.D. Karkas, E. Chargaff, Proc. Natl. Acad. Sci. USA 63, 152 (1969)CrossRefADSGoogle Scholar
  10. 10.
    J.R. Lobry, Lecture Notes Comput. Sci. 3039, 679 (2004). http://pbil.
  11. 11.
    A.C. Frank, J.R. Lobry, Bioinformatics 16, 560 (2000)CrossRefGoogle Scholar
  12. 12.
    P. Mackiewicz, J. Zakrzewska-Czerwinska, A. Zawilak, M.R. Dudek, S. Cebrat, Nucleic Acids Res. 32, 3781 (2004)CrossRefGoogle Scholar
  13. 13.
    P. Legendre, Y. Desdevises, E. Bazin, Syst. Biol. 51, 217 (2002)CrossRefGoogle Scholar
  14. 14.
    N. Saitou, M. Nei, Mol. Biol. Evol. 4, 406 (1984)Google Scholar
  15. 15.
    T.H. Jukes, C.R. Cantor, in Mammalian Protein Metabolism, ed. by H.N. Munro (Academic, New York, 1969) pp. 21-132Google Scholar
  16. 16.
    M. Kimura, J. Mol. Evol. 16, 111 (1980)CrossRefGoogle Scholar
  17. 17.
    G. Perrière, J. Thioulouse, Nucleic Acids Res. 30, 4548 (2002)CrossRefGoogle Scholar
  18. 18.
    . C. Gautier, Ph.D. Thesis (1987), Université Claude Bernard - Lyon IGoogle Scholar
  19. 19.
    . J.R. Lobry, C. Gautier, Nucleic Acids Res. 22, 3174 (1994).
  20. 20.
    . J.R. Lobry, D. Chessel, J. Appl. Genet.44, 235(2003).
  21. 21.
    W.-H. Li, J. Mol. Evol. 36, 96 (1993)CrossRefGoogle Scholar
  22. 22.
    L.D. Hurst, Trends Genet. 18, 486 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Delphine Charif
    • 1
  • Jean R. Lobry
    • 2
  1. 1.Université Claude BernardVillerbanne CedexFrance
  2. 2.Laboratoire de Biométrie et Biologie Evolutive (UMR 5558)CNRS Univ.Villeurbanne CedexFrance

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