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Sequencing and comparison of yeast species to identify genes and regulatory elements

Author

Listed:
  • Manolis Kellis

    (Whitehead/MIT Center for Genome Research, Nine Cambridge Center)

  • Nick Patterson

    (Whitehead/MIT Center for Genome Research, Nine Cambridge Center)

  • Matthew Endrizzi

    (Whitehead/MIT Center for Genome Research, Nine Cambridge Center)

  • Bruce Birren

    (Whitehead/MIT Center for Genome Research, Nine Cambridge Center)

  • Eric S. Lander

    (Whitehead/MIT Center for Genome Research, Nine Cambridge Center
    Massachusetts Institute of Technology)

Abstract

Identifying the functional elements encoded in a genome is one of the principal challenges in modern biology. Comparative genomics should offer a powerful, general approach. Here, we present a comparative analysis of the yeast Saccharomyces cerevisiae based on high-quality draft sequences of three related species (S. paradoxus, S. mikatae and S. bayanus). We first aligned the genomes and characterized their evolution, defining the regions and mechanisms of change. We then developed methods for direct identification of genes and regulatory motifs. The gene analysis yielded a major revision to the yeast gene catalogue, affecting approximately 15% of all genes and reducing the total count by about 500 genes. The motif analysis automatically identified 72 genome-wide elements, including most known regulatory motifs and numerous new motifs. We inferred a putative function for most of these motifs, and provided insights into their combinatorial interactions. The results have implications for genome analysis of diverse organisms, including the human.

Suggested Citation

  • Manolis Kellis & Nick Patterson & Matthew Endrizzi & Bruce Birren & Eric S. Lander, 2003. "Sequencing and comparison of yeast species to identify genes and regulatory elements," Nature, Nature, vol. 423(6937), pages 241-254, May.
    • Handle: RePEc:nat:nature:v:423:y:2003:i:6937:d:10.1038_nature01644
    DOI: 10.1038/nature01644
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    Citations

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    Cited by:

    1. Leelavati Narlikar & Raluca Gordân & Alexander J Hartemink, 2007. "A Nucleosome-Guided Map of Transcription Factor Binding Sites in Yeast," PLOS Computational Biology, Public Library of Science, vol. 3(11), pages 1-10, November.
    2. Harri Lähdesmäki & Alistair G Rust & Ilya Shmulevich, 2008. "Probabilistic Inference of Transcription Factor Binding from Multiple Data Sources," PLOS ONE, Public Library of Science, vol. 3(3), pages 1-24, March.
    3. Fabio Pardi & Nick Goldman, 2005. "Species Choice for Comparative Genomics: Being Greedy Works," PLOS Genetics, Public Library of Science, vol. 1(6), pages 1-1, December.
    4. Christian L Barrett & Bernhard O Palsson, 2006. "Iterative Reconstruction of Transcriptional Regulatory Networks: An Algorithmic Approach," PLOS Computational Biology, Public Library of Science, vol. 2(5), pages 1-10, May.
    5. Alexander Kawrykow & Gary Roumanis & Alfred Kam & Daniel Kwak & Clarence Leung & Chu Wu & Eleyine Zarour & Phylo players & Luis Sarmenta & Mathieu Blanchette & Jérôme Waldispühl, 2012. "Phylo: A Citizen Science Approach for Improving Multiple Sequence Alignment," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-9, March.
    6. Krishna B. S. Swamy & Hsin-Yi Lee & Carmina Ladra & Chien-Fu Jeff Liu & Jung-Chi Chao & Yi-Yun Chen & Jun-Yi Leu, 2022. "Proteotoxicity caused by perturbed protein complexes underlies hybrid incompatibility in yeast," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. J Roman Arguello & Carolina Sellanes & Yann Ru Lou & Robert A Raguso, 2013. "Can Yeast (S. cerevisiae) Metabolic Volatiles Provide Polymorphic Signaling?," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-12, August.
    8. Kemal Sonmez & Naunihal T Zaveri & Ilan A Kerman & Sharon Burke & Charles R Neal & Xinmin Xie & Stanley J Watson & Lawrence Toll, 2009. "Evolutionary Sequence Modeling for Discovery of Peptide Hormones," PLOS Computational Biology, Public Library of Science, vol. 5(1), pages 1-12, January.
    9. Eilon Sharon & Shai Lubliner & Eran Segal, 2008. "A Feature-Based Approach to Modeling Protein–DNA Interactions," PLOS Computational Biology, Public Library of Science, vol. 4(8), pages 1-17, August.
    10. Lit-Hsin Loo & Danai Laksameethanasan & Yi-Ling Tung, 2014. "Quantitative Protein Localization Signatures Reveal an Association between Spatial and Functional Divergences of Proteins," PLOS Computational Biology, Public Library of Science, vol. 10(3), pages 1-17, March.
    11. Valerie Storms & Marleen Claeys & Aminael Sanchez & Bart De Moor & Annemieke Verstuyf & Kathleen Marchal, 2010. "The Effect of Orthology and Coregulation on Detecting Regulatory Motifs," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-11, February.
    12. Robert K Bradley & Adam Roberts & Michael Smoot & Sudeep Juvekar & Jaeyoung Do & Colin Dewey & Ian Holmes & Lior Pachter, 2009. "Fast Statistical Alignment," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-15, May.
    13. Tao Song & Hong Gu, 2014. "Discriminative Motif Discovery via Simulated Evolution and Random Under-Sampling," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
    14. Rahul Siddharthan & Eric D Siggia & Erik van Nimwegen, 2005. "PhyloGibbs: A Gibbs Sampling Motif Finder That Incorporates Phylogeny," PLOS Computational Biology, Public Library of Science, vol. 1(7), pages 1-23, December.
    15. Alessandro L. V. Coradini & Christopher Ne Ville & Zachary A. Krieger & Joshua Roemer & Cara Hull & Shawn Yang & Daniel T. Lusk & Ian M. Ehrenreich, 2023. "Building synthetic chromosomes from natural DNA," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    16. Kenzie D MacIsaac & Ernest Fraenkel, 2006. "Practical Strategies for Discovering Regulatory DNA Sequence Motifs," PLOS Computational Biology, Public Library of Science, vol. 2(4), pages 1-10, April.
    17. Siewert Elizabeth A & Kechris Katerina J, 2009. "Prediction of Motifs Based on a Repeated-Measures Model for Integrating Cross-Species Sequence and Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-36, September.

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