Quantitative analysis of fitness and genetic interactions in yeast on a genome scale
By Anastasia Baryshnikova, M. Costanzo, Yungil Kim, Huiming Ding, Judice L. Y. Koh, Kiana Toufighi, J. Youn, J. Ou, Bryan-Joseph San Luis, Sunayan Bandyopadhyay and 9 more. Nature Methods, 2010
373 citations recorded by the source corpus.
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.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/a39a03deca718e61740a1311946314b63177f884
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In Genetics
In Molecular Biology
- Trey Ideker
- Roded Sharan
- Anastasia Baryshnikova
- Michael Ku Yu
- Nevan J. Krogan
- Janusz Dutkowski
- Michael Kramer
- Jason F. Kreisberg
Citations in the corpus
- Systematic Functional Annotation and Visualization of Biological Networks (2016, methodology): “The quantitative genetic interaction data involving 13 members of the proteasome regulatory subunit were obtained and processed as described previously (Baryshnikova et al., 2010a; Baryshnikova et al., 2010b).”
- Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems (2016, methodology): “04); the maximum achievable correlation, as previously determined by experimental genetic interaction replicates (Baryshnikova et al., 2010), was r = 0.”