Advertisement

Cross-Species Comparison Using Expression Data

  • Gaëlle Lelandais
  • Stéphane Le Crom

Abstract

Molecular evolution, which is classically assessed by the comparison of individual proteins or genes between species, can now be studied by comparing coexpressed functional groups of genes. This approach, which better reflects the functional constraints on the evolution of organisms, can exploit the large amount of data generated by overall, genome-wide expression analyses. To optimize cross-species comparison, particular caution must be used in the selection of expression data, using, for example, the most related experimental conditions between species. In addition, defining gene pairs having interspecies correspondence is also a critical step that can create misleading relations between genes and that could benefit from international annotation efforts like the Gene Ontology (GO) Consortium.

In this chapter, we describe methodologies based on global approaches or gene-centered methods that can be used to answer precise biological questions. Finally, through a set of examples, we show that expression profile comparison between species can help to discover functional annotation for unknown genes and improve orthology links between organisms.

Key Words

Microarrays transcriptome cross-species comparison gene ontology orthologs paralogs 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bernal A, Ear U, Kyrpides N. Genomes OnLine Database (GOLD): a monitor of genome projects world-wide. Nucleic Acids Res 2001;29:126–127.PubMedCrossRefGoogle Scholar
  2. 2.
    Frazer KA, Elnitski L, Church DM, et al. Cross-species sequence comparisons: a review of methods and available resources. Genome Res 2003;13:1–12.PubMedCrossRefGoogle Scholar
  3. 3.
    Schena M, Shalon D, Davis RW, et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 1995;270:467–470.PubMedCrossRefGoogle Scholar
  4. 4.
    Eisen MB, Brown PO. DNA arrays for analysis of gene expression. Methods Enzymol 1999;303:179–205.PubMedCrossRefGoogle Scholar
  5. 5.
    Zhou XJ, Gibson G. Cross-species comparison of genome-wide expression patterns. Genome Biol 2004;5:232.PubMedCrossRefGoogle Scholar
  6. 6.
    Marc P, Devaux F, Jacq C. yMGV: a database for visualization and data mining of published genome-wide yeast expression data. Nucleic Acids Res 2001;29:E63–3.PubMedCrossRefGoogle Scholar
  7. 7.
    Lelandais G, Marc P, Vincens P, et al. MiCoViTo: a tool for gene-centric comparison and visualization of yeast transcriptome states. BMC Bioinformatics 2004;5:20.PubMedCrossRefGoogle Scholar
  8. 8.
    Stuart JM, Segal E, Koller D, et al. A gene-coexpression network for global discovery of conserved genetic modules. Science 2003;302:249–255.PubMedCrossRefGoogle Scholar
  9. 9.
    Bergmann S, Ihmels J, Barkai N. Similarities and differences in genome-wide expression data of six organisms. PLoS Biol 2004;2:E9.PubMedCrossRefGoogle Scholar
  10. 10.
    Lefebvre C, Aude JC, Glemet E, et al. Balancing protein similarity and gene co-expression reveals new links between genetic conservation and developmental diversity in invertebrates. Bioinformatics 2005;21:1550–1558.PubMedCrossRefGoogle Scholar
  11. 11.
    McCarroll SA, Murphy CT, Zou S, et al. Comparing genomic expression patterns across species identifies shared transcriptional profile in aging. Nat Genet 2004;36:197–204.PubMedCrossRefGoogle Scholar
  12. 12.
    Alter O, Brown PO, Botstein D. Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms. Proc Natl Acad Sci USA 2003;100:3351–3356.PubMedCrossRefGoogle Scholar
  13. 13.
    Hughes TR, Marton MJ, Jones AR et al. Functional discovery via a compendium of expression profiles. Cell 2000;102:109–126.PubMedCrossRefGoogle Scholar
  14. 14.
    Kim SK, Lund J, Kiraly M, et al. A gene expression map for Caenorhabditis elegans. Science 2001;293:2087–2092.PubMedCrossRefGoogle Scholar
  15. 15.
    Carter DE, Robinson JF, Allister EM, et al. Quality assessment of microarray experiments. Clin Biochem 2005;38:639–642.PubMedCrossRefGoogle Scholar
  16. 16.
    Jordan BR. How consistent are expression chip platforms? Bioessays 2004;26:1236–1242.PubMedCrossRefGoogle Scholar
  17. 17.
    Marshall E. Getting the noise out of gene arrays. Science 2004;306:630–631.PubMedCrossRefGoogle Scholar
  18. 18.
    Bammler T, Beyer RP, Bhattacharya S, et al. Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2005;2:351–356.PubMedCrossRefGoogle Scholar
  19. 19.
    Irizarry RA, Warren D, Spencer F, et al. Multiple-laboratory comparison of microarray platforms. Nat Methods 2005;2:345–350.PubMedCrossRefGoogle Scholar
  20. 20.
    Larkin JE, Frank BC, Gavras H, Sultana R, Quackenbush J. Independence and reproducibility across microarray platforms. Nat Methods 2005;2:337–344.PubMedCrossRefGoogle Scholar
  21. 21.
    Brazma A, Hingamp P, Quackenbush J, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 2001;29:365–371.PubMedCrossRefGoogle Scholar
  22. 22.
    Kellis M, Patterson N, Endrizzi M, et al. Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature 2003;423:241–254.PubMedCrossRefGoogle Scholar
  23. 23.
    Dujon B, Sherman D, Fischer G, et al. Genome evolution in yeasts. Nature 2004;430:35–44.PubMedCrossRefGoogle Scholar
  24. 24.
    Remm M, Storm CE, Sonnhammer EL. Automatic clustering of orthologs and in-paralogs from pairwise species comparisons. J Mol Biol 2001;314:1041–1052.PubMedCrossRefGoogle Scholar
  25. 25.
    Fitch WM. Homology a personal view on some of the problems. Trends Genet 2000;16:227–231.PubMedCrossRefGoogle Scholar
  26. 26.
    Storm CE, Sonnhammer EL. Automated ortholog inference from phylogenetic trees and calculation of orthology reliability. Bioinformatics 2002;18:92–99.PubMedCrossRefGoogle Scholar
  27. 27.
    Li L, Stoeckert CJ, Jr., Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 2003;13:2178–2189.PubMedCrossRefGoogle Scholar
  28. 28.
    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990;215:403–410.PubMedGoogle Scholar
  29. 29.
    O’Brien KP, Remm M, Sonnhammer EL. Inparanoid: a comprehensive database of eukaryotic orthologs. Nucleic Acids Res 2005;33 Database Issue:D476–D480.PubMedCrossRefGoogle Scholar
  30. 30.
    Sonnhammer EL, Koonin EV. Orthology, paralogy and proposed classification for paralog subtypes. Trends Genet 2002;18:619–620.PubMedCrossRefGoogle Scholar
  31. 31.
    He X, Zhang J. Gene complexity and gene duplicability. Curr Biol 2005;15:1016–1021.PubMedCrossRefGoogle Scholar
  32. 32.
    Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000;25:25–29.PubMedCrossRefGoogle Scholar
  33. 33.
    Harris MA, Clark J, Ireland A, et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 2004;32:D258–261.PubMedCrossRefGoogle Scholar
  34. 34.
    Ogren PV, Cohen KB, Hunter L. Implications of compositionality in the gene ontology for its curation and usage. Pac Symp Biocomput 2005:174–185.Google Scholar
  35. 35.
    Lewis SE. Gene ontology: looking backwards and forwards. Genome Biol 2005;6:103.PubMedCrossRefGoogle Scholar
  36. 36.
    Mata J, Lyne R, Burns G, et al. The transcriptional program of meiosis and sporulation in fission yeast. Nat Genet 2002;32:143–147.PubMedCrossRefGoogle Scholar
  37. 37.
    Chen D, Toone WM, Mata J, et al. Global transcriptional responses of fission yeast to environmental stress. Mol Biol Cell 2003;14:214–229.PubMedCrossRefGoogle Scholar
  38. 38.
    Chu S, DeRisi J, Eisen M, et al. The transcriptional program of sporulation in budding yeast. Science 1998;282:699–705.PubMedCrossRefGoogle Scholar
  39. 39.
    Gasch AP, Spellman PT, Kao CM, et al. Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell 2000;11:4241–4257.PubMedGoogle Scholar
  40. 40.
    Mata J, Bahler J. Correlations between gene expression and gene conservation in fission yeast. Genome Res 2003;13:2686–2690.PubMedCrossRefGoogle Scholar
  41. 41.
    Zeeberg BR, Feng W, Wang G, et al. GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol 2003;4:R28.PubMedCrossRefGoogle Scholar
  42. 42.
    Lelandais G, Le Crom S, Devaux F, et al. yMGV: a cross-species expression data mining tool. Nucleic Acids Res 2004;32 Database issue:D323–D325.PubMedCrossRefGoogle Scholar
  43. 43.
    Wood V. Schizosaccharomyces pombe comparative genomics: from sequence to systems. In: Comparative genomics using fungi as models (Sunnerhagen P, Piskur J, eds.), vol. 15, pp. 233–285. New York: Springer; 2006.Google Scholar
  44. 44.
    Gasch AP, Eisen MB. Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biol 2002;3:RESEARCH0059.Google Scholar
  45. 45.
    Lelandais G, Vincens A, Badel-Chagnon S, et al. Comparing gene expression networks in a multi-dimensional space to extract similarities and differences between organisms. Bioinformatics 2006;22(11):1359–1366.PubMedCrossRefGoogle Scholar
  46. 46.
    Enault F, Suhre K, Claverie JM. Phydbac “Gene Function Predictor”: a gene annotation tool based on genomic context analysis. BMC Bioinformatics 2005;6:247PubMedCrossRefGoogle Scholar
  47. 47.
    Christie KR, Weng S, Balakrishnan R, et al. Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms. Nucleic Acids Res 2004;32 Database issue:D311–D314.PubMedCrossRefGoogle Scholar
  48. 48.
    Lang T, Schaeffeler E, Bernreuther D, et al. Aut2p and Aut7p, two novel microtubule-associated proteins are essential for delivery of autophagic vesicles to the vacuole. EMBO J 1998;17:3597–3607.PubMedCrossRefGoogle Scholar
  49. 49.
    Hertz-Fowler C, Peacock CS, Wood V, et al. GeneDB: a resource for prokaryotic and eukaryotic organisms. Nucleic Acids Res 2004;32 Database issue:D339–D343.PubMedCrossRefGoogle Scholar
  50. 50.
    Poirot O, O’Toole E, Notredame C. Tcoffee@igs: A Web server for computing, evaluating and combining multiple sequence alignments. Nucleic Acids Res 2003;31:3503–3506.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press Inc. 2007

Authors and Affiliations

  • Gaëlle Lelandais
    • 1
  • Stéphane Le Crom
    • 2
  1. 1.CNRS UMR 8541Ecole Normale SupérieureParisFrance
  2. 2.INSERM U368Ecole Normale SupérieureParisFrance

Personalised recommendations