Advertisement

A Perspective on Comparative and Functional Genomics

  • Daniel Doerr
  • Jens StoyeEmail author
Chapter
Part of the Computational Biology book series (COBO, volume 29)

Abstract

Comparing genomes based on the order of genes provides insights into their evolutionary history and further allows to identify sets of genes with associated function. In the past two decades, many methods have been developed for identifying genomic regions that share homologous genes, which can be subsequently tested for functional associativity. As these methods are flexible by tolerating duplicate, missing, and intruding genes, we now study a case in which relationships between genes are established through a hierarchical relationship and thereby turn the problem of identifying regions with common functional associations inside out: We use a measure of dissimilarity between genes defined on a gene ontology hierarchy to identify collections of genomic regions with low functional dissimilarity.

Keywords

Gene order comparison Functional genomics Functional dissimilarity 

References

  1. 1.
    Arst, H.N., MacDonald, D.W.: A gene cluster in Aspergillus nidulans with an internally located cis-acting regulatory region. Nature 254(5495), 26–31 (1975)CrossRefGoogle Scholar
  2. 2.
    Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., Sherlock, G.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000)CrossRefGoogle Scholar
  3. 3.
    Beal, M., Bergeron, A., Corteel, S., Raffinot, M.: An algorithmic view of gene teams. Theor. Comput. Sci. 320(2–3), 395–418 (2004)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Conesa, A., Götz, S., García-Gómez, J.M., Terol, J., Talón, M., Robles, M.: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21(18), 3674–3676 (2005)CrossRefGoogle Scholar
  5. 5.
    Dash, S.K., Scholz, S.B., Herhut, S., Christianson, B.: A scalable approach to computing representative lowest common ancestor in directed acyclic graphs. Theor. Comput. Sci. 513(C), 25–37 (2013)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Díaz-Díaz, N., Aguilar-Ruiz, J.S.: GO-based functional dissimilarity of gene sets. BMC Bioinform. 12(1), 360 (2011)CrossRefGoogle Scholar
  7. 7.
    Doerr, D., Stoye, J., Böcker, S., Jahn, K.: Identifying gene clusters by discovering common intervals in indeterminate strings. BMC Gen. 15(Suppl 6), S2 (2014)CrossRefGoogle Scholar
  8. 8.
    Doerr, D., Thévenin, A., Stoye, J.: Gene family assignment-free comparative genomics. BMC Bioinform. 13(Suppl 19), S3 (2012)CrossRefGoogle Scholar
  9. 9.
    Ghiurcuta, C.G., Moret, B.M.E.: Evaluating synteny for improved comparative studies. Bioinformatics 30(12), i9–18 (2014)CrossRefGoogle Scholar
  10. 10.
    Jahn, K.: Efficient computation of approximate gene clusters based on reference occurrences. J. Comput. Biol. 18(9), 1255–1274 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kent, W.J., Sugnet, C.W., Furey, T.S., Roskin, K.M., Pringle, T.H., Zahler, A.M., Haussler, D.: The human genome browser at UCSC. Gen. Res. 12(6), 996–1006 (2002)CrossRefGoogle Scholar
  12. 12.
    Luc, N., Risler, J.L., Bergeron, A., Raffinot, M.: Gene teams: a new formalization of gene clusters for comparative genomics. Comput. Bio. Chem. 27(1), 59–67 (2003)CrossRefGoogle Scholar
  13. 13.
    Overbeek, R., Fonstein, M., D’Souza, M., Pusch, G.D., Maltsev, N.: The use of gene clusters to infer functional coupling. P. Natl. Acad. Sci. USA 96(6), 2896–2901 (1999)CrossRefGoogle Scholar
  14. 14.
    Proost, S., Fostier, J., De Witte, D., Dhoedt, B., Demeester, P., Van de Peer, Y., Vandepoele, K.: i-ADHoRe 3.0–fast and sensitive detection of genomic homology in extremely large data sets. Nucleic Acids Res. 40(2), e11–e11 (2012)CrossRefGoogle Scholar
  15. 15.
    Rödelsperger, C., Dieterich, C.: CYNTENATOR: progressive gene order alignment of 17 vertebrate genomes. PLoS ONE 5(1), e8861–e8861 (2010)CrossRefGoogle Scholar
  16. 16.
    Sankoff, D., Haque, L.: Power boosts for cluster tests. In: McLysaght, A., Huson, D.H. (eds.) Comparative Genomics, LNCS, vol. 3678, pp. 121–130. Springer, Berlin, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Thevenin, A., Ein-Dor, L., Ozery-Flato, M., Shamir, R.: Functional gene groups are concentrated within chromosomes, among chromosomes and in the nuclear space of the human genome. Nucleic Acids Res. 42(15), 9854–9861 (2014)CrossRefGoogle Scholar
  18. 18.
    Uno, T., Yagiura, M.: Fast algorithms to enumerate all common intervals of two permutations. Algorithmica 26(2), 290–309 (2000)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Wang, Y., Tang, H., Debarry, J.D., Tan, X., Li, J., Wang, X., Lee, T.h., Jin, H., Marler, B., Guo, H., Kissinger, J.C., Paterson, A.H.: MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40(7), e49–e49 (2012)CrossRefGoogle Scholar
  20. 20.
    Zhang, X., Ye, M., Moret, B.: Phylogenetic transfer of knowledge for biological networks. PeerJ PrePrints 2, e401v1 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Genome Informatics, Faculty of Technology and Center for BiotechnologyBielefeld UniversityBielefeldGermany

Personalised recommendations