A Transcript Perspective on Evolution

  • Yann Christinat
  • Bernard M. E. Moret
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7292)


Alternative splicing is now recognized as a major mechanism for transcriptome and proteome diversity in higher eukaryotes. Yet, its evolution is poorly understood. Most studies focus on the evolution of exons and introns at the gene level, while only few consider the evolution of transcripts.

In this paper, we present a framework for transcript phylogenies where ancestral transcripts evolve along the gene tree by gains, losses, and mutation. We demonstrate the usefulness of our method on a set of 805 genes and two different topics. First, we improve a method for transcriptome reconstruction from ESTs (ASPic), then we study the evolution of function in transcripts. The use of transcript phylogenies allows us to double the specificity of ASPic, whereas results on the functional study reveal that conserved transcripts are more likely to share protein domains than functional sites. These studies validate our framework for the study of evolution in large collections of organisms from the perspective of transcripts; we developed and provide a new tool, TrEvoR, for this purpose.


alternative splicing transcript evolution phylogeny protein domain transcriptome reconstruction 


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  1. 1.
    Kim, N., Shin, S., Lee, S.: ECgene: genome-based EST clustering and gene modeling for alternative splicing. Genome Research 15(4), 566–576 (2005)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Modrek, B., Lee, C.: A genomic view of alternative splicing. Nature Genetics 30(1), 13–19 (2002)CrossRefGoogle Scholar
  3. 3.
    Keren, H., Lev-Maor, G., Ast, G.: Alternative splicing and evolution: diversification, exon definition and function. Nature Reviews Genetics 11(5), 345–355 (2010)CrossRefGoogle Scholar
  4. 4.
    Artamonova, I.I., Gelfand, M.S.: Comparative genomics and evolution of alternative splicing: the pessimists’ science. Chemical Reviews 107(8), 3407–3430 (2007)CrossRefGoogle Scholar
  5. 5.
    Harr, B., Turner, L.M.: Genome-wide analysis of alternative splicing evolution among Mus subspecies. Molecular Ecology 19(suppl.1), 228–239 (2010)CrossRefGoogle Scholar
  6. 6.
    Nurtdinov, R.N.: Low conservation of alternative splicing patterns in the human and mouse genomes. Human Molecular Genetics 12(11), 1313–1320 (2003)CrossRefGoogle Scholar
  7. 7.
    Flicek, P., et al.: Ensembl 10th year. Nucleic Acids Research 38(suppl.1), D557–D562 (2010)CrossRefGoogle Scholar
  8. 8.
    Christinat, Y., Moret, B.: Inferring transcript phylogenies. In: Proc. of IEEE International Conference on Bioinformatics and Biomedecine, pp. 208–215 (2011)Google Scholar
  9. 9.
    Martelli, P., et al.: ASPicDB: a database of annotated transcript and protein variants generated by alternative splicing. Nucleic Acids Research 39(suppl.1), D80 (2011)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Pruitt, K., et al.: NCBI Reference Sequences: current status, policy and new initiatives. Nucleic Acids Research 37(suppl.1), D32–D36 (2009)CrossRefGoogle Scholar
  11. 11.
    Bonizzoni, P., et al.: Detecting alternative gene structures from spliced ESTs: a computational approach. Journal of Computational Biology 16(1), 43–66 (2009)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Eyras, E., et al.: ESTGenes: alternative splicing from ESTs in Ensembl. Genome Research 14(5), 976–987 (2004)CrossRefGoogle Scholar
  13. 13.
    Guttman, M., et al.: Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincrnas. Nature Biotechnology 28(5), 503–510 (2010)CrossRefGoogle Scholar
  14. 14.
    Trapnell, C., et al.: Transcript assembly and quantification by rna-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology 28(5), 511–515 (2010)CrossRefGoogle Scholar
  15. 15.
    Xing, Y., et al.: An expectation-maximization algorithm for probabilistic reconstructions of full-length isoforms from splice graphs. Nucleic Acids Research 34(10), 3150 (2006)CrossRefGoogle Scholar
  16. 16.
    Cline, M., et al.: The effects of alternative splicing on transmembrane proteins in the mouse genome. In: Pac. Symp. Biocomput. 2004, pp. 17–28 (2004)Google Scholar
  17. 17.
    Eden, E., et al.: GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10(1), 48 (2009)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Sankoff, D.: Minimal Mutation Trees of Sequences. SIAM Journal on Applied Mathematics 28(1), 35–42 (1975)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yann Christinat
    • 1
  • Bernard M. E. Moret
    • 1
  1. 1.Laboratory of Computational Biology and BioinformaticsEPFLLausanneSwitzerland

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