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Functional profiling of the Saccharomyces cerevisiae genome

Author

Listed:
  • Guri Giaever

    (Stanford Genome Technology Center, Stanford University)

  • Angela M. Chu

    (Stanford University School of Medicine)

  • Li Ni

    (Yale University
    Developmental Biology, Yale University)

  • Carla Connelly

    (Johns Hopkins University School of Medicine
    Genetics, Johns Hopkins University School of Medicine)

  • Linda Riles

    (Washington University Medical School)

  • Steeve Véronneau

    (McGill University)

  • Sally Dow

    (Rosetta Inpharmatics Inc.)

  • Ankuta Lucau-Danila

    (FYSA, Université catholique de Louvain)

  • Keith Anderson

    (Stanford Genome Technology Center, Stanford University)

  • Bruno André

    (Université Libre de Bruxelles, Laboratoire de Physiologie Cellulaire, IBMM CP300)

  • Adam P. Arkin

    (University of California
    Howard Hughes Medical Institute)

  • Anna Astromoff

    (Stanford University School of Medicine)

  • Mohamed El Bakkoury

    (IRMW, Université Libre de Bruxelles)

  • Rhonda Bangham

    (Yale University
    Developmental Biology, Yale University)

  • Rocio Benito

    (Instituto de Microbiologia y Bioquimica, CSIC/Universidad de Salamanca)

  • Sophie Brachat

    (Biozentrum, University of Basel)

  • Stefano Campanaro

    (University of Padova)

  • Matt Curtiss

    (Washington University Medical School)

  • Karen Davis

    (Stanford Genome Technology Center, Stanford University)

  • Adam Deutschbauer

    (Stanford University School of Medicine)

  • Karl-Dieter Entian

    (EUROSCARF, Johann Wolfgang Goethe-Universität, Institute of Microbiology)

  • Patrick Flaherty

    (University of California
    Howard Hughes Medical Institute
    University of California)

  • Francoise Foury

    (FYSA, Université catholique de Louvain)

  • David J. Garfinkel

    (Center for Cancer Research, National Cancer Institute at Frederick)

  • Mark Gerstein

    (Yale University)

  • Deanna Gotte

    (Center for Cancer Research, National Cancer Institute at Frederick)

  • Ulrich Güldener

    (Institut fur Mikrobiologie, Heinrich-Heine-Universitat Dusseldorf)

  • Johannes H. Hegemann

    (Institut fur Mikrobiologie, Heinrich-Heine-Universitat Dusseldorf)

  • Svenja Hempel

    (EUROSCARF, Johann Wolfgang Goethe-Universität, Institute of Microbiology)

  • Zelek Herman

    (Stanford Genome Technology Center, Stanford University)

  • Daniel F. Jaramillo

    (Stanford Genome Technology Center, Stanford University)

  • Diane E. Kelly

    (University of Wales)

  • Steven L. Kelly

    (University of Wales)

  • Peter Kötter

    (EUROSCARF, Johann Wolfgang Goethe-Universität, Institute of Microbiology)

  • Darlene LaBonte

    (Yale University
    Developmental Biology, Yale University)

  • David C. Lamb

    (University of Wales)

  • Ning Lan

    (Yale University)

  • Hong Liang

    (Stanford University School of Medicine)

  • Hong Liao

    (Yale University
    Developmental Biology, Yale University)

  • Lucy Liu

    (Yale University
    Developmental Biology, Yale University)

  • Chuanyun Luo

    (Yale University
    Developmental Biology, Yale University)

  • Marc Lussier

    (McGill University)

  • Rong Mao

    (Johns Hopkins University School of Medicine
    Genetics, Johns Hopkins University School of Medicine)

  • Patrice Menard

    (McGill University)

  • Siew Loon Ooi

    (Johns Hopkins University School of Medicine
    Genetics, Johns Hopkins University School of Medicine)

  • Jose L. Revuelta

    (Instituto de Microbiologia y Bioquimica, CSIC/Universidad de Salamanca)

  • Christopher J. Roberts

    (Rosetta Inpharmatics Inc.)

  • Matthias Rose

    (EUROSCARF, Johann Wolfgang Goethe-Universität, Institute of Microbiology)

  • Petra Ross-Macdonald

    (Yale University
    Developmental Biology, Yale University)

  • Bart Scherens

    (IRMW, Université Libre de Bruxelles)

  • Greg Schimmack

    (Rosetta Inpharmatics Inc.)

  • Brenda Shafer

    (Center for Cancer Research, National Cancer Institute at Frederick)

  • Daniel D. Shoemaker

    (Stanford University School of Medicine)

  • Sharon Sookhai-Mahadeo

    (Johns Hopkins University School of Medicine
    Genetics, Johns Hopkins University School of Medicine)

  • Reginald K. Storms

    (Concordia University)

  • Jeffrey N. Strathern

    (Center for Cancer Research, National Cancer Institute at Frederick)

  • Giorgio Valle

    (University of Padova)

  • Marleen Voet

    (Katholieke Universiteit Leuven, Laboratory of Gene Technology)

  • Guido Volckaert

    (Katholieke Universiteit Leuven, Laboratory of Gene Technology)

  • Ching-yun Wang

    (Center for Cancer Research, National Cancer Institute at Frederick)

  • Teresa R. Ward

    (Rosetta Inpharmatics Inc.)

  • Julie Wilhelmy

    (Washington University Medical School)

  • Elizabeth A. Winzeler

    (Stanford University School of Medicine)

  • Yonghong Yang

    (Yale University
    Developmental Biology, Yale University)

  • Grace Yen

    (Stanford University School of Medicine)

  • Elaine Youngman

    (Johns Hopkins University School of Medicine
    Genetics, Johns Hopkins University School of Medicine)

  • Kexin Yu

    (Johns Hopkins University School of Medicine
    Genetics, Johns Hopkins University School of Medicine)

  • Howard Bussey

    (McGill University)

  • Jef D. Boeke

    (Johns Hopkins University School of Medicine
    Genetics, Johns Hopkins University School of Medicine)

  • Michael Snyder

    (Yale University
    Developmental Biology, Yale University)

  • Peter Philippsen

    (Biozentrum, University of Basel)

  • Ronald W. Davis

    (Stanford Genome Technology Center, Stanford University
    Stanford University School of Medicine)

  • Mark Johnston

    (Washington University Medical School)

Abstract

Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed ‘molecular bar codes’ uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.

Suggested Citation

  • Guri Giaever & Angela M. Chu & Li Ni & Carla Connelly & Linda Riles & Steeve Véronneau & Sally Dow & Ankuta Lucau-Danila & Keith Anderson & Bruno André & Adam P. Arkin & Anna Astromoff & Mohamed El Ba, 2002. "Functional profiling of the Saccharomyces cerevisiae genome," Nature, Nature, vol. 418(6896), pages 387-391, July.
    • Handle: RePEc:nat:nature:v:418:y:2002:i:6896:d:10.1038_nature00935
    DOI: 10.1038/nature00935
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    Cited by:

    1. Tadamune Kaneko & Macoto Kikuchi, 2022. "Evolution enhances mutational robustness and suppresses the emergence of a new phenotype: A new computational approach for studying evolution," PLOS Computational Biology, Public Library of Science, vol. 18(1), pages 1-20, January.
    2. Allahyari, N. & Hosseiny, A. & Abedpour, N. & Jafari, G.R., 2024. "Analyzing the heterogeneous structure of the genes interaction network through the random matrix theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    3. Jin, Haiyan & Zhang, ChenXing & Ma, Mengzhou & Gong, Qianhua & Yu, Liang & Guo, Xingli & Gao, Lin & Wang, Bingbo, 2020. "Inferring essential proteins from centrality in interconnected multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).

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