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

Anastasia Baryshnikova; Michael Costanzo; Yungil Kim; Huiming Ding; Judice Koh; Kiana Toufighi; Ji Young Youn; Jiongwen Ou; Bryan Joseph San Luis; Sunayan Bandyopadhyay; Matthew Hibbs; David Hess; Anne Claude Gingras; Gary D. Bader; Olga G. Troyanskaya; Grant W. Brown; Brenda Andrews; Charles Boone; Chad L. Myers

doi:10.1038/nmeth.1534

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

Anastasia Baryshnikova, Michael Costanzo, Yungil Kim, Huiming Ding, Judice Koh, Kiana Toufighi, Ji Young Youn, Jiongwen Ou, Bryan Joseph San Luis, Sunayan Bandyopadhyay, Matthew Hibbs, David Hess, Anne Claude Gingras, Gary D. Bader, Olga G. Troyanskaya, Grant W. Brown, Brenda Andrews, Charles Boone, Chad L. Myers

Computer Science and Engineering

Research output: Contribution to journal › Article › peer-review

257 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	1017-1024
Number of pages	8
Journal	Nature Methods
Volume	7
Issue number	12
DOIs	https://doi.org/10.1038/nmeth.1534
State	Published - Dec 2010

Bibliographical note

Funding Information:
We thank R. Mani, F. Roth, B. Papp, T. Maeda and T. Hays for helpful discussions and critical comments. This work was supported by Genome Canada through the Ontario Genomics Institute (2004-OGI-3-01), the Canadian Institutes of Health Research (GSP-415-67; C.B. and B.A.) (MOP-79368; G.W.B.), the US National Institutes of Health (1R01HG005084-01A1; C.L.M., Y.K. and S.B.) and the National Science Foundation (DBI 0953881, MCB 0918908; C.L.M. and Y.K.) and partially supported by a seed grant from the Minnesota Supercomputing Institute (C.L.M. and Y.K.).

Access

10.1038/nmeth.1534

OpenUrl availability

Full text

Cite this

Baryshnikova, A., Costanzo, M., Kim, Y., Ding, H., Koh, J., Toufighi, K., Youn, J. Y., Ou, J., San Luis, B. J., Bandyopadhyay, S., Hibbs, M., Hess, D., Gingras, A. C., Bader, G. D., Troyanskaya, O. G., Brown, G. W., Andrews, B., Boone, C., & Myers, C. L. (2010). Quantitative analysis of fitness and genetic interactions in yeast on a genome scale. Nature Methods, 7(12), 1017-1024. https://doi.org/10.1038/nmeth.1534

Baryshnikova, A, Costanzo, M, Kim, Y, Ding, H, Koh, J, Toufighi, K, Youn, JY, Ou, J, San Luis, BJ, Bandyopadhyay, S, Hibbs, M, Hess, D, Gingras, AC, Bader, GD, Troyanskaya, OG, Brown, GW, Andrews, B, Boone, C & Myers, CL 2010, 'Quantitative analysis of fitness and genetic interactions in yeast on a genome scale', Nature Methods, vol. 7, no. 12, pp. 1017-1024. https://doi.org/10.1038/nmeth.1534

@article{2a9f55ac335044cca7676810c82cb9e4,

title = "Quantitative analysis of fitness and genetic interactions in yeast on a genome scale",

abstract = "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.",

author = "Anastasia Baryshnikova and Michael Costanzo and Yungil Kim and Huiming Ding and Judice Koh and Kiana Toufighi and Youn, {Ji Young} and Jiongwen Ou and {San Luis}, {Bryan Joseph} and Sunayan Bandyopadhyay and Matthew Hibbs and David Hess and Gingras, {Anne Claude} and Bader, {Gary D.} and Troyanskaya, {Olga G.} and Brown, {Grant W.} and Brenda Andrews and Charles Boone and Myers, {Chad L.}",

note = "Funding Information: We thank R. Mani, F. Roth, B. Papp, T. Maeda and T. Hays for helpful discussions and critical comments. This work was supported by Genome Canada through the Ontario Genomics Institute (2004-OGI-3-01), the Canadian Institutes of Health Research (GSP-415-67; C.B. and B.A.) (MOP-79368; G.W.B.), the US National Institutes of Health (1R01HG005084-01A1; C.L.M., Y.K. and S.B.) and the National Science Foundation (DBI 0953881, MCB 0918908; C.L.M. and Y.K.) and partially supported by a seed grant from the Minnesota Supercomputing Institute (C.L.M. and Y.K.).",

year = "2010",

month = dec,

doi = "10.1038/nmeth.1534",

language = "English (US)",

volume = "7",

pages = "1017--1024",

journal = "Nature Methods",

issn = "1548-7091",

publisher = "Nature Publishing Group",

number = "12",

}

TY - JOUR

T1 - Quantitative analysis of fitness and genetic interactions in yeast on a genome scale

AU - Baryshnikova, Anastasia

AU - Costanzo, Michael

AU - Kim, Yungil

AU - Ding, Huiming

AU - Koh, Judice

AU - Toufighi, Kiana

AU - Youn, Ji Young

AU - Ou, Jiongwen

AU - San Luis, Bryan Joseph

AU - Bandyopadhyay, Sunayan

AU - Hibbs, Matthew

AU - Hess, David

AU - Gingras, Anne Claude

AU - Bader, Gary D.

AU - Troyanskaya, Olga G.

AU - Brown, Grant W.

AU - Andrews, Brenda

AU - Boone, Charles

AU - Myers, Chad L.

N1 - Funding Information: We thank R. Mani, F. Roth, B. Papp, T. Maeda and T. Hays for helpful discussions and critical comments. This work was supported by Genome Canada through the Ontario Genomics Institute (2004-OGI-3-01), the Canadian Institutes of Health Research (GSP-415-67; C.B. and B.A.) (MOP-79368; G.W.B.), the US National Institutes of Health (1R01HG005084-01A1; C.L.M., Y.K. and S.B.) and the National Science Foundation (DBI 0953881, MCB 0918908; C.L.M. and Y.K.) and partially supported by a seed grant from the Minnesota Supercomputing Institute (C.L.M. and Y.K.).

PY - 2010/12

Y1 - 2010/12

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=78649705725&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78649705725&partnerID=8YFLogxK

U2 - 10.1038/nmeth.1534

DO - 10.1038/nmeth.1534

M3 - Article

C2 - 21076421

AN - SCOPUS:78649705725

SN - 1548-7091

VL - 7

SP - 1017

EP - 1024

JO - Nature Methods

JF - Nature Methods

IS - 12

ER -

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

Abstract

Bibliographical note

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this