The Algorithmic of Gene Teams

  • Anne Bergeron
  • Sylvie Corteel
  • Mathieu Raffinot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2452)


Comparative genomics is a growing field in computational biology, and one of its typical problem is the identification of sets of orthologous genes that have virtually the same function in several genomes. Many different bioinformatics approaches have been proposed to define these groups, often based on the detection of sets of genes that are “not too far” in all genomes. In this paper, we propose a unifying concept, called gene teams, which can be adapted to various notions of distance. We present two algorithms for identifying gene teams formed by n genes placed on m linear chromosomes. The first one runs in O(m 2 n 2) time, and follows a direct and simple approach. The second one is more tricky, but its running time is O(mnlog2(n)). Both algorithms require linear space. We also discuss extensions to circular chromosomes that achieve the same complexity.


Time Complexity Polynomial Algorithm Recursive Call Nucleic Acid Research Circular Chromosome 
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  1. 1.
    Arvind K. Bansal An automated comparative analysis of 17 complete microbial genomes. Bioinformatics, Vol 15, No. 11, p 900–908, (1999).CrossRefGoogle Scholar
  2. 2.
    Tristan Colombo, Alain Guénoche and Yves Quentin, Inférence fonctionnelle par l’analyse du contexte génétique: une application aux transporteurs ABC. Oral presentation, Journées ALBIO, Montpellier, March 2002.Google Scholar
  3. 3.
    Thomas Cormen, Charles Leiserson and Ronald Rivest. Introduction to Algorithms. The MIT Press, Cambridge Mass., Eighth Printing, 1028 pages (1992).Google Scholar
  4. 4.
    Thomas Dandekar, Berend Snel, Martijn Huynen M and Peer Bork. Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem Sci, Vol 23, No. 9, p 324–328, (1998).CrossRefGoogle Scholar
  5. 5.
    Wataru Fujibuchi, Hiroyuki Ogata, Hideo Matsuda and Minoru Kanahisa Automatic detection of conserved gene clusters in multiple genomes by graph comparison and P-quasi grouping. Nucleic Acids Research, Vol 28, No. 20, p 4029–4036, (2000).CrossRefGoogle Scholar
  6. 6.
    Steffen Heber and Jens Stoye. Finding all common intervals of k permutations, CPM 01 Proceedings, LNCS 2089, Springer-Verlag, Berlin: 207–218 (2001).Google Scholar
  7. 7.
    Martijn Huynen, Berend Snel, Warren Lathe III, and Peer Bork Predicting Protein Function by Genomic Context: Quantitative Evaluation and Qualitative Inferences. Genome Research, Vol 10, p 1024–1210, (2000).CrossRefGoogle Scholar
  8. 8.
    Anne Morgat. Synténies bactériennes. Oral presentation, Entretiens Jacques Cartier on Comparative Genomics, Lyon, December 2001.Google Scholar
  9. 9.
    Hiroyuki Ogata, Wataru Fujibuchi, Susumu Goto and Minoru Kanehisa A heuristic graph comparison algorithm and its application to detect functionally related enzyme clusters. Nucleic Acids Research, Vol 28, No. 20, p 4021–4028, (2000).CrossRefGoogle Scholar
  10. 10.
    Ross Overbeek, Michael Fonstein, Mark D’souza, Gordon D. Push and Natalia Maltsev The use of gene clusters to infer functional coupling. Proc. Natl. Acad. Sci. USA, Vol 96, p 2896–2901, (1999).Google Scholar
  11. 11.
    T. Uno and M. Yagiura. Fast algorithms to enumerate all common intervals of two permutations. Algorithmica 26(2), 290–309, (2000).zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Roman L. Tatusov, Michael Y. Galperin, Darren A. Natale and Eugene V. Koonin The COG database: a tool for genome-sacle analysis of protein functions and evolution. Nucleic Acids Research, Vol 28, No. 1, p 33–36, (2000).CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Anne Bergeron
    • 1
    • 2
  • Sylvie Corteel
    • 3
  • Mathieu Raffinot
    • 4
  1. 1.LaCIMUniversité du Québec à MontréalCanada
  2. 2.Institut Gaspard-MongeUniversité Marne-la-ValléeFrance
  3. 3.CNRS - Laboratoire PRiSMUniversité de VersaillesVersailles cedexFrance
  4. 4.CNRS - Laboratoire Génome et InformatiqueEvryFrance

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