Quantitative analysis of fitness and genetic interactions in yeast on a genome scale

被引:141
|
作者
Baryshnikova, Anastasia [1 ,2 ]
Costanzo, Michael [1 ]
Kim, Yungil [3 ,4 ]
Ding, Huiming [1 ]
Koh, Judice [1 ]
Toufighi, Kiana [1 ]
Youn, Ji-Young [1 ,2 ]
Ou, Jiongwen [5 ]
San Luis, Bryan-Joseph [1 ]
Bandyopadhyay, Sunayan [3 ]
Hibbs, Matthew [6 ]
Hess, David [7 ]
Gingras, Anne-Claude [8 ]
Bader, Gary D. [1 ,2 ]
Troyanskaya, Olga G. [9 ]
Brown, Grant W. [5 ]
Andrews, Brenda [1 ,2 ]
Boone, Charles [1 ,2 ]
Myers, Chad L. [3 ]
机构
[1] Univ Toronto, Banting & Best Dept Med Res, Terrence Donnelly Ctr Cellular & Biomol Res, Toronto, ON, Canada
[2] Univ Toronto, Terrence Donnelly Ctr Cellular & Biomol Res, Dept Mol Genet, Toronto, ON, Canada
[3] Univ Minnesota Twin Cities, Dept Comp Sci & Engn, Minneapolis, MN USA
[4] Univ Minnesota Twin Cities, Dept Elect & Comp Engn, Minneapolis, MN USA
[5] Univ Toronto, Dept Biochem, Terrence Donnelly Ctr Cellular & Biomol Res, Toronto, ON, Canada
[6] Jackson Lab, Bar Harbor, ME 04609 USA
[7] Santa Clara Univ, Dept Biol, Santa Clara, CA 95053 USA
[8] Mt Sinai Hosp, Samuel Lunenfeld Res Inst, Toronto, ON M5G 1X5, Canada
[9] Princeton Univ, Dept Comp Sci, Lewis Sigler Inst Integrat Genom, Carl Icahn Lab, Princeton, NJ 08544 USA
基金
美国国家科学基金会; 加拿大健康研究院; 美国国家卫生研究院;
关键词
SACCHAROMYCES-CEREVISIAE; INTERACTION NETWORKS; EPISTASIS; PATHWAY; MAP; CONSERVATION; LANDSCAPE; RIM101; DFG16;
D O I
10.1038/NMETH.1534
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single-and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles.
引用
收藏
页码:1017 / U110
页数:9
相关论文
共 50 条
  • [22] GENETIC INTERACTIONS INVOLVING THE YEAST CYTOSKELETON
    BOTSTEIN, D
    JOURNAL OF CELLULAR BIOCHEMISTRY, 1995, : 333 - 333
  • [23] Exploring genetic interactions and networks with yeast
    Charles Boone
    Howard Bussey
    Brenda J. Andrews
    Nature Reviews Genetics, 2007, 8 : 437 - 449
  • [24] A molecular interpretation of genetic interactions in yeast
    Stein, Amelie
    Aloy, Patrick
    FEBS LETTERS, 2008, 582 (08) : 1245 - 1250
  • [25] Exploring genetic interactions and networks with yeast
    Boone, Charles
    Bussey, Howard
    Andrews, Brenda J.
    NATURE REVIEWS GENETICS, 2007, 8 (06) : 437 - 449
  • [26] Bacterial–fungal interactions revealed by genome-wide analysis of bacterial mutant fitness
    Emily C. Pierce
    Manon Morin
    Jessica C. Little
    Roland B. Liu
    Joanna Tannous
    Nancy P. Keller
    Kit Pogliano
    Benjamin E. Wolfe
    Laura M. Sanchez
    Rachel J. Dutton
    Nature Microbiology, 2021, 6 : 87 - 102
  • [27] Large-scale analysis of the yeast genome by transposon tagging and gene disruption
    Ross-Macdonald, P
    Coelho, PSR
    Roemer, T
    Agarwal, S
    Kumar, A
    Jansen, R
    Cheung, KH
    Sheehan, A
    Symoniatis, D
    Umansky, L
    Heldtman, M
    Nelson, FK
    Iwasaki, H
    Hager, K
    Gerstein, M
    Miller, P
    Roeder, GS
    Snyder, M
    NATURE, 1999, 402 (6760) : 413 - 418
  • [28] Large-scale analysis of the yeast genome by transposon tagging and gene disruption
    Petra Ross-Macdonald
    Paulo S. R. Coelho
    Terry Roemer
    Seema Agarwal
    Anuj Kumar
    Ronald Jansen
    Kei-Hoi Cheung
    Amy Sheehan
    Dawn Symoniatis
    Lara Umansky
    Matthew Heidtman
    F. Kenneth Nelson
    Hiroshi Iwasaki
    Karl Hager
    Mark Gerstein
    Perry Miller
    G. Shirleen Roeder
    Michael Snyder
    Nature, 1999, 402 : 413 - 418
  • [29] A Quantitative Fitness Analysis Workflow
    Banks, A. P.
    Lawless, C.
    Lydall, D. A.
    JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, 2012, (66):
  • [30] MPH: fast REML for large-scale genome partitioning of quantitative genetic variation
    Jiang, Jicai
    BIOINFORMATICS, 2024, 40 (05)