Systematic interpretation of genetic interactions using protein networks

ABSTRACT Genetic interaction analysis,in which two mutations have a combined effect not exhibited by either mutation alone, is a powerful and widespread tool for establishing functional

linkages between genes. In the yeast _Saccharomyces cerevisiae_, ongoing screens have generated >4,800 such genetic interaction data. We demonstrate that by combining these data with

information on protein-protein, prote in-DNA or metabolic networks, it is possible to uncover physical mechanisms behind many of the observed genetic effects. Using a probabilistic model, we

found that 1,922 genetic interactions are significantly associated with either between- or within-pathway explanations encoded in the physical networks, covering ∼40% of known genetic

interactions. These models predict new functions for 343 proteins and suggest that between-pathway explanations are better than within-pathway explanations at interpreting genetic

interactions identified in systematic screens. This study provides a road map for how genetic and physical interactions can be integrated to reveal pathway organization and function. Access

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SIMILAR CONTENT BEING VIEWED BY OTHERS ASSOCIATION STUDY BASED ON TOPOLOGICAL CONSTRAINTS OF PROTEIN–PROTEIN INTERACTION NETWORKS Article Open access 01 July 2020 GAINING CONFIDENCE IN

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Molecular Biology–RECOMB_, 282–289 (ACM Press, New York, 2004) Google Scholar  Download references ACKNOWLEDGEMENTS We thank Jonathan Wang, Owen Ozier and Gopal Ramachandran for preliminary

investigations and Vineet Bafna, Ben Raphael and Vikas Bansal for insightful commentary. Craig Mak, Silpa Suthram and Taylor Sittler provided helpful reviews of the text. Funding was

provided by the National Institute of General Medical Sciences (GM070743-01) and the National Science Foundation (NSF 0425926). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Program in

Bioinformatics, University of California, San Diego, 9500 Gilman Dr., San Diego, 92093-0412, California, USA Ryan Kelley & Trey Ideker * Department of Bioengineering, University of

California, San Diego, 9500 Gilman Dr., San Diego, 92093-0412, California, USA Ryan Kelley & Trey Ideker Authors * Ryan Kelley View author publications You can also search for this

author inPubMed Google Scholar * Trey Ideker View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to Trey Ideker. ETHICS

DECLARATIONS COMPETING INTERESTS The authors declare no competing financial interests. SUPPLEMENTARY INFORMATION SUPPLEMENTARY FIG. 1 Direct overlaps between genetic and physical

interactions, while statistically significant, are limited in systematic data and probably biased. (PDF 192 kb) SUPPLEMENTARY FIG. 2 Influence of beta on result set. (PDF 179 kb)

SUPPLEMENTARY FIG. 3 Estimated prediction accuracy for naive and pathway-based within-pathway genetic predictions. (PDF 188 kb) SUPPLEMENTARY TABLE 1 Compounds excluded from the physical

interaction network (not used to connect two proteins in a metabolic interaction). (PDF 19 kb) SUPPLEMENTARY TABLE 2 The members of pathways identified in various searches. (PDF 68 kb)

SUPPLEMENTARY TABLE 3 The log-odds score associated with each network model identified in various searches. (PDF 62 kb) SUPPLEMENTARY TABLE 4 Results from reduced searches. (PDF 28 kb)

SUPPLEMENTARY TABLE 5 Functional enrichment. (PDF 23 kb) SUPPLEMENTARY TABLE 6 GO annotation predictions made with pathways obtained from various searches. (PDF 112 kb) SUPPLEMENTARY TABLE 7

Basis of annotation predictions. (PDF 21 kb) RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Kelley, R., Ideker, T. Systematic interpretation of genetic

interactions using protein networks. _Nat Biotechnol_ 23, 561–566 (2005). https://doi.org/10.1038/nbt1096 Download citation * Published: 05 May 2005 * Issue Date: 01 May 2005 * DOI:

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