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2 - Computational paradigms for analyzing genetic interaction networks

Published online by Cambridge University Press:  05 July 2015

Carles Pons
Affiliation:
University of Minnesota-Twin Cities
Michael Costanzo
Affiliation:
University of Toronto
Charles Boone
Affiliation:
University of Toronto
Chad L. Myers
Affiliation:
University of Minnesota-Twin Cities
Florian Markowetz
Affiliation:
Cancer Research UK Cambridge Institute
Michael Boutros
Affiliation:
German Cancer Research Center, Heidelberg
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Summary

The advent of sequencing technologies has revolutionized our understanding and approach to studying biological systems. Indeed, whole-genome sequencing projects have already targeted many different species, enabling the identification of most genes in those organisms. However, observed phenotypes cannot be explained by genes alone, but rather by the interactions that their products establish under some environmental conditions (Waddington 1957). Thus, it is through the analysis of these interaction net-works (e.g. regulatory, metabolic, molecular, or genetic) that we can better understand the genotype-to-phenotype relationship, the complexity and evolution of organisms, or the differences among individuals of the same species. The topology and dynamics of these biological networks can be unveiled by systematic perturbation of their nodes (i.e. genes). For instance, upon single-gene deletions in Saccharomyces cerevisiae under standard laboratory conditions, most genes (∼80%) were not found to be essential for cell viability (Giaever et al. 2002). Though many of these genes may be required for growth in other environments (Hillenmeyer et al. 2008), this result suggests extensive functional redundancy among genes. Such functional buffering confers robustness to biological networks and shields the cellular machinery from genetic perturbations (Hartman et al. 2001). Additionally, the small effect on phenotype that many gene deletions exhibit (see Figure 2.1) evidences that single perturbations alone cannot capture the complexity of the genotype-to-phenotype relationship. Therefore, a combinatorial approach to gene perturbations is best suited to elucidate biological systems and can enable a better characterization of genes and cellular functioning.

Definition of genetic interaction

Genetic interactions reveal functional relations between genes that contribute to a pheno-typic trait. William Bateson first introduced the term, formerly known as epistasis (see Phillips [1998] for a description on the origin and evolution of the definition), to refer to an allele at one locus preventing a variant at another from manifesting its effect (Bateson 1909).

Type
Chapter
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Systems Genetics
Linking Genotypes and Phenotypes
, pp. 12 - 35
Publisher: Cambridge University Press
Print publication year: 2015

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References

Ashburner, M., Ball, C., Blake, J., Botstein, D., Butler, H., et al. (2000), ‘Gene Ontology: tool for the unification of biology’, Nature Genetics 25 (1), 25–29.CrossRefGoogle Scholar
Avery, L., & Wasserman, S., (1992), ‘Ordering gene function: the interpretation of epistasis in regulatory hierarchies’, Trends in Genetics 8 (9), 312–316.CrossRefGoogle Scholar
Babu, M., Daz-Meja, J. J., Vlasblom, J., Gagarinova, A., Phanse, S., et al. (2011), ‘Genetic interaction maps in Escherichia coli reveal functional crosstalk among cell envelope biogenesis pathways’, PLoS Genetics 7 (11), e1002377.CrossRefGoogle Scholar
Bandyopadhyay, S., Kelley, R., Krogan, N. J. & Ideker, T., (2008), ‘Functional maps of protein complexes from quantitative genetic interaction data’, PLoS Computational Biology 4 (4).CrossRefGoogle Scholar
Barabási, A. -L., & Oltvai, Z. N. (2004), ‘Network biology: understanding the cell's functional organization’, Nature Review Genetics 5 (2), 101–113.CrossRefGoogle Scholar
Barabási, A. -L.Gulbahce, N., & Loscalzo, J., (2011), ‘Network medicine: a network-based approach to human disease’, Nature Review Genetics 12 (1), 56–68.CrossRefGoogle Scholar
Baryshnikova, A., Costanzo, M., Dixon, S., Vizeacoumar, F. J., Myers, C. L. et al. (2010a<), Synthetic genetic array (SGA) analysis in Saccharomyces cerevisiae and Schizosaccharomyces pombe, Methods in Enzymology 470, 145–179.Google ScholarPubMed
Baryshnikova, A., Costanzo, M., Kim, Y., Ding, H., Koh, J., et al. (2010b), ‘Quantitative analysis of fitness and genetic interactions in yeast on a genome scale’, Nature Methods 7 (12), 1017–1024.CrossRefGoogle Scholar
Bateson, W., (1909), Mendel's principles of heredity, Cambridge University Press.CrossRefGoogle Scholar
Battle, A., Jonikas, M. C., Walter, P., Weissman, J. S. & Koller, D., (2010), ‘Automated identification of pathways from quantitative genetic interaction data’, Molecular Systems Biology 6, 379.CrossRefGoogle Scholar
Bean, G. J. & Ideker, T., (2012), ‘Differential analysis of high-throughput quantitative genetic interaction data’, Genome Biology 13 (12), R123.CrossRefGoogle Scholar
Bellay, J., Atluri, G., Sing, T. L., Toufighi, K., Costanzo, M., et al. (2011), ‘Putting genetic interactions in context through a global modular decomposition’, Genome Research 21 (8), 1375–1387.CrossRefGoogle Scholar
Ben-Aroya, S., Coombes, C., Kwok, T., O'Donnell, K. A., Boeke, J. D. et al. (2008), ‘Toward a comprehensive temperature-sensitive mutant repository of the essential genes of Saccharomyces cerevisiae’, Molecular Cell 30 (2), 248–258.CrossRefGoogle Scholar
Botstein, D., & Fink, G. R. (2011), ‘Yeast: an experimental organism for 21st century biology’, Genetics 189 (3), 695–704.CrossRefGoogle Scholar
Breslow, D. K., Cameron, D. M., Collins, S. R., Schuldiner, M., Stewart-Ornstein, J., et al. (2008), ‘A comprehensive strategy enabling high-resolution functional analysis of the yeast genome’, Nature Methods 5 (8), 711–718.CrossRefGoogle Scholar
Butland, G., Babu, M., Daz-Meja, J. J., Bohdana, F., Phanse, S., et al. (2008), ‘eSGA: E. coli synthetic genetic array analysis’, Nature Methods 5 (9), 789–795.CrossRefGoogle Scholar
Byrne, A. B., Weirauch, M. T., Wong, V., Koeva, M., Dixon, S. J. et al. (2007), ‘A global analysis of genetic interactions in Caenorhabditis elegans’, Journal of Biology 6 (3), 8.CrossRefGoogle Scholar
Collins, S., Miller, K., Maas, N., Roguev, A., Fillingham, J., et al. (2007), ‘Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map’, Nature 446(7137), 806–810.CrossRefGoogle Scholar
Collins, S. R., Schuldiner, M., Krogan, N. J. & Weissman, J. S. (2006), ‘A strategy for extracting and analyzing large-scale quantitative epistatic interaction data’, Genome Biology 7 (7), R63.CrossRefGoogle Scholar
Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E., et al. (2010), ‘The genetic landscape of a cell’, Science 327(5964), 425.CrossRefGoogle Scholar
Davierwala, A. P., Haynes, J., Li, Z., Brost, R. L., Robinson, M. D. et al. (2005), ‘The synthetic genetic interaction spectrum of essential genes’, Nature Genetics 37 (10), 1147–1152.CrossRefGoogle Scholar
Dean, E. J., Davis, J. C., Davis, R. W. & Petrov, D. A. (2008), ‘Pervasive and persistent redundancy among duplicated genes in yeast’, PLoS Genetics 4 (7), e1000113.CrossRefGoogle Scholar
Decourty, L., Saveanu, C., Zemam, K., Hantraye, F., Frachon, E., et al. (2008), ‘Linking functionally related genes by sensitive and quantitative characterization of genetic interaction profiles’, Proceedings of the National Academy of Sciences of the USA 105 (15), 5821–5826.CrossRefGoogle Scholar
DeLuna, A., Vetsigian, K., Shoresh, N., Hegreness, M., Coln-Gonzlez, M., et al. (2008), ‘Exposing the fitness contribution of duplicated genes’, Nature Genetics 40 (5), 676–681.CrossRefGoogle Scholar
Deshpande, R., Vandersluis, B., & Myers, C. L. (2013), ‘Comparison of profile similarity measures for genetic interaction networks’, PloS One 8 (7), e68664.CrossRefGoogle Scholar
Deutscher, D., Meilijson, I., Kupiec, M., & Ruppin, E., (2006), ‘Multiple knockout analysis of genetic robustness in the yeast metabolic network’, Nature Genetics 38 (9), 993–998.CrossRefGoogle Scholar
Dixon, S. J., Fedyshyn, Y., Koh, J. L. Y., Prasad, T. S. K., Chahwan, C., et al. (2008), ‘Significant conservation of synthetic lethal genetic interaction networks between distantly related eukaryotes’, Proceedings of the National Academy of Sciences of the USA 105 (43), 16 653–16 658.CrossRefGoogle Scholar
Drees, B. L., Thorsson, V., Carter, G. W., Rives, A. W., Raymond, M. Z. et al. (2005), ‘Derivation of genetic interaction networks from quantitative phenotype data’, Genome Biology 6 (4), R38.CrossRefGoogle Scholar
Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D., (1998), ‘Cluster analysis and display of genome-wide expression patterns’, Proceedings of the National Academy of Sciences of the USA 95 (25), 14 863–14 868.CrossRefGoogle Scholar
Fisher, R. A. (1918), ‘The correlations between relatives on the supposition of Mendelian inheritance’, Transactions of the Royal Society Edinburgh 52, 399–433.Google Scholar
Förster, J., Famili, I., Palsson, B. O. & Nielsen, J., (2003), ‘Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae’, Omics 7 (2), 193–202.CrossRefGoogle Scholar
Frost, A., Elgort, M. G., Brandman, O., Ives, C., Collins, S. R. et al. (2012), ‘Functional repurposing revealed by comparing S. pombe and S. cerevisiae genetic interactions’, Cell 149 (6), 1339–1352.CrossRefGoogle Scholar
Giaever, G., Chu, A. M., Ni, L., Connelly, C., Riles, L., et al. (2002), ‘Functional profiling of the Saccharomyces cerevisiae genome’, Nature 418(6896), 387–391.CrossRefGoogle Scholar
Goffeau, A., Barrell, B. G., Bussey, H., Davis, R. W., Dujon, B., et al. (1996), ‘Life with 6000 genes’, Science 274(5287), 546, 563–567.CrossRefGoogle Scholar
Guarente, L., (1993), ‘Synthetic enhancement in gene interaction: a genetic tool come of age’, Trends in Genetics 9 (10), 362–366.CrossRefGoogle Scholar
Hartman, J. L. t., Garvik, B., & Hartwell, L., (2001), ‘Principles for the buffering of genetic variation’, Science 291(5506), 1001–1004.CrossRefGoogle Scholar
Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999), ‘From molecular to modular cell biology’, Nature 402(6761 Suppl), C47–52.CrossRefGoogle Scholar
He, X., Qian,W.,Wang, Z., Li, Y., & Zhang, J., (2010), ‘Prevalent positive epistasis in Escherichia coli and Saccharomyces cerevisiae metabolic networks’, Nature Genetics 42 (3), 272–276.CrossRefGoogle Scholar
Hillenmeyer, M. E., Fung, E., Wildenhain, J., Pierce, S. E., Hoon, S., et al. (2008), ‘The chemical genomic portrait of yeast: uncovering a phenotype for all genes’, Science 320(5874), 362–365.CrossRefGoogle Scholar
Horn, T., Sandmann, T., Fischer, B., Axelsson, E., Huber, W., et al. (2011), ‘Mapping of signaling networks through synthetic genetic interaction analysis by RNAi’, Nature Methods 8 (4), 341–346.CrossRefGoogle Scholar
Ideker, T., & Krogan, N. J. (2012), ‘Differential network biology’, Molecular Systems Biology 8, 565.CrossRefGoogle Scholar
Jasnos, L., & Korona, R., (2007), ‘Epistatic buffering of fitness loss in yeast double deletion strains’, Nature Genetics 39 (4), 550–554.CrossRefGoogle Scholar
Jonikas, M. C., Collins, S. R., Denic, V., Oh, E., Quan, E. M. et al. (2009), ‘Comprehensive characterization of genes required for protein folding in the endoplasmic reticulum’, Science 323(5922), 1693–1697.CrossRefGoogle Scholar
Kelley, R., & Ideker, T., (2005), ‘Systematic interpretation of genetic interactions using protein networks’, Nature Biotechnology 23 (5), 561–566.CrossRefGoogle Scholar
Kim, D. -U.Hayles, J., Kim, D., Wood, V., Park, H.,-O. et al. (2010), ‘Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe’, Nature Biotechnology 28 (6), 617–623.Google Scholar
Koch, E. N., Costanzo, M., Bellay, J., Deshpande, R., Chatfield-Reed, K., et al. (2012), ‘Conserved rules govern genetic interaction degree across species’, Genome Biology 13 (7), R57.CrossRefGoogle Scholar
Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., et al. (2010), ‘Tackling the widespread and critical impact of batch effects in high-throughput data’, Nature Reviews Genetics 11 (10), 733–739.CrossRefGoogle Scholar
Lehner, B., Crombie, C., Tischler, J., Fortunato, A., & Fraser, A. G. (2006), ‘Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways’, Nature Genetics 38 (8), 896–903.CrossRefGoogle Scholar
Li, Z., Vizeacoumar, F. J., Bahr, S., Li, J., Warringer, J., et al. (2011), ‘Systematic exploration of essential yeast gene function with temperature-sensitive mutants’, Nature Biotechnology 29 (4), 361–367.CrossRefGoogle Scholar
Lucchesi, J. C. (1968), ‘Synthetic lethality and semi-lethality among functionally related mutants of Drosophila melanogaster’, Genetics 59 (1), 37–44.Google Scholar
Mani, R., St Onge, R., Hartman, J., Giaever, G., & Roth, F., (2008), ‘Defining genetic interaction’, Proceedings of the National Academy of Sciences of the USA 105 (9), 3461–3466.CrossRefGoogle Scholar
Matthews, L. R., Vaglio, P., Reboul, J., Ge, H., Davis, B. P. et al. (2001), ‘Identification of potential interaction networks using sequence-based searches for conserved protein–protein interactions or “interologs”’, Genome Research 11 (12), 2120–2126.CrossRefGoogle Scholar
McLellan, J., O'Neil, N., Tarailo, S., Stoepel, J., Bryan, J., et al. (2009), ‘Synthetic lethal genetic interactions that decrease somatic cell proliferation in Caenorhabditis elegans identify the alternative RFC CTF18 as a candidate cancer drug target’, Molecular Biology of the Cell 20 (24), 5306–5313.CrossRefGoogle Scholar
Michaut, M., Baryshnikova, A., Costanzo, M., Myers, C. L., Andrews, B. J. et al. (2011), ‘Protein complexes are central in the yeast genetic landscape’, PLoS Computational Biology 7 (2), e1001092.CrossRefGoogle Scholar
Musso, G., Costanzo, M., Huangfu, M., Smith, A. M., Paw, J., et al. (2008), ‘The extensive and condition-dependent nature of epistasis among whole-genome duplicates in yeast’, Genome Research 18 (7), 1092–1099.CrossRefGoogle Scholar
Ohya, Y., Sese, J., Yukawa, M., Sano, F., Nakatani, Y., et al. (2005), ‘High-dimensional and largescale phenotyping of yeast mutants’, Proceedings of the National Academy of Sciences of the USA 102 (52), 19 015–19 020.CrossRefGoogle Scholar
Orth, J. D., Thiele, I., & Palsson, B., (2010), ‘What is flux balance analysis?’, Nature Biotechnology 28 (3), 245–248.CrossRefGoogle Scholar
Pan, X., Yuan, D. S., Xiang, D., Wang, X., Sookhai-Mahadeo, S., et al. (2004), ‘A robust toolkit for functional profiling of the yeast genome’, Molecular Cell 16 (3), 487–496.CrossRefGoogle Scholar
Phillips, P. C. (1998), ‘The language of gene interaction’, Genetics 149 (3), 1167–1171.Google Scholar
Qi, Y., Suhail, Y., Lin, Y., Boeke, J. D. & Bader, J. S. (2008), ‘Finding friends and enemies in an enemies-only network: a graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions’, Genome Research 18 (12), 1991–2004.CrossRefGoogle Scholar
Roguev, A., Bandyopadhyay, S., Zofall, M., Zhang, K., Fischer, T., et al. (2008), ‘Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast’, Science 322(5900), 405–410.CrossRefGoogle Scholar
Roguev, A., Wiren, M., Weissman, J. S. & Krogan, N. J. (2007), ‘High-throughput genetic interaction mapping in the fission yeast Schizosaccharomyces pombe’, Nature Methods 4 (10), 861–866.CrossRefGoogle Scholar
Ryan, C., Greene, D., Cagney, G., & Cunningham, P., (2010), ‘Missing value imputation for epistatic MAPs’, BMC Bioinformatics 11, 197.CrossRefGoogle Scholar
Ryan, C., Greene, D., Gunol, A., van Attikum, H., Krogan, N. J. et al. (2011), ‘Improved functional overview of protein complexes using inferred epistatic relationships’, BMC Systems Biology 5, 80.CrossRefGoogle Scholar
Ryan, C. J., Roguev, A., Patrick, K., Xu, J., Jahari, H., et al. (2012), ‘Hierarchical modularity and the evolution of genetic interactomes across species’, Molecular Cell 46 (5), 691–704.CrossRefGoogle Scholar
Schuldiner, M., Collins, S. R., Thompson, N. J., Denic, V., Bhamidipati, A., et al. (2005), ‘Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile’, Cell 123 (3), 507–519.CrossRefGoogle Scholar
Segre, D., Deluna, A., Church, G. M. & Kishony, R., (2005), ‘Modular epistasis in yeast metabolism’, Nature Genetics 37 (1), 77–83.CrossRefGoogle Scholar
Snitkin, E. S. & Segre, D., (2011), ‘Epistatic interaction maps relative to multiple metabolic phenotypes’, PLoS Genetics 7 (2), e1001294.CrossRefGoogle Scholar
St Onge, R. P., Mani, R., Oh, J., Proctor, M., Fung, E., et al. (2007), ‘Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions’, Nature Genetics 39 (2), 199–206.Google Scholar
Stark, C., Breitkreutz, B. -J.Reguly, T., Boucher, L., Breitkreutz, A., et al. (2006), ‘BioGRID: a general repository for interaction datasets’, Nucleic Acids Research 34(Database issue), D535–539.CrossRefGoogle Scholar
Sunnerhagen, P., (2002), ‘Prospects for functional genomics in Schizosaccaromyces pombe’, Current Genetics 42, 73–84.CrossRefGoogle Scholar
Suter, B., Auerbach, D., & Stagljar, I., (2006), ‘Yeast-based functional genomics and proteomics technologies: the first 15 years and beyond’, BioTechniques 40 (5), 625–644.CrossRefGoogle Scholar
Szappanos, B., Kovacs, K., Szamecz, B., Honti, F., Costanzo, M., et al. (2011), ‘An integrated approach to characterize genetic interaction networks in yeast metabolism’, Nature Genetics 43 (7), 656–662.CrossRefGoogle Scholar
Tischler, J., Lehner, B., Chen, N., & Fraser, A. G. (2006), ‘Combinatorial RNA interference in Caenorhabditis elegans reveals that redundancy between gene duplicates can be maintained for more than 80 million years of evolution’, Genome Biology 7 (8), R69.CrossRefGoogle Scholar
Tischler, J., Lehner, B., & Fraser, A. G. (2008), ‘Evolutionary plasticity of genetic interaction networks’, Nature Genetics 40 (4), 390–391.CrossRefGoogle Scholar
Tong, A. H., Evangelista, M., Parsons, A. B., Xu, H., Bader, G. D. et al. (2001), ‘Systematic genetic analysis with ordered arrays of yeast deletion mutants’, Science 294(5550), 2364–2368.CrossRefGoogle Scholar
Tong, A. H., Lesage, G., Bader, G. D., Ding, H., Xu, H., et al. (2004), ‘Global mapping of the yeast genetic interaction network’, Science 303(5659), 808–813.CrossRefGoogle Scholar
Tucker, C. L. & Fields, S., (2003), ‘Lethal combinations’, Nature Genetics 35 (3), 204–205.CrossRefGoogle Scholar
Typas, A., Nichols, R. J., Siegele, D. A., Shales, M., Collins, S. R. et al. (2008), ‘High-throughput, quantitative analyses of genetic interactions’ in E. coli, Nature Methods 5 (9), 781–787.CrossRefGoogle Scholar
Ulitsky, I., & Shamir, R., (2007), ‘Pathway redundancy and protein essentiality revealed in the Saccharomyces cerevisiae interaction networks’, Molecular Systems Biology 3, 104.CrossRefGoogle Scholar
Ulitsky, I., Krogan, N. J. & Shamir, R., (2009), ‘Towards accurate imputation of quantitative genetic interactions’, Genome Biology 10 (12), R140.CrossRefGoogle Scholar
van Dam, T. J. P. & Snel, B., (2008), ‘Protein complex evolution does not involve extensive network rewiring’, PLoS Computational Biology 4 (7), e1000132.CrossRefGoogle ScholarPubMed
Van Driessche, N., Demsar, J., Booth, E. O., Hill, P., Juvan, P., et al. (2005), ‘Epistasis analysis with global transcriptional phenotypes’, Nature Genetics 37 (5), 471–477.CrossRefGoogle Scholar
VanderSluis, B., Bellay, J., Musso, G., Costanzo, M., Papp, B., et al. (2010), ‘Genetic interactions reveal the evolutionary trajectories of duplicate genes’, Molecular Systems Biology 6, 429.CrossRefGoogle Scholar
Vizeacoumar, F. J., Chong, Y., Boone, C., & Andrews, B. J. (2009), ‘A picture is worth a thousand words: genomics to phenomics in the yeast Saccharomyces cerevisiae’, FEBS Letters 583 (11), 1656–1661.CrossRefGoogle Scholar
Vizeacoumar, F. J., Van Dyk, N., Vizeacoumar, F. S., Cheung, V., Li, J., et al. (2010), ‘Integrating high-throughput genetic interaction mapping and high-content screening to explore yeast spindle morphogenesis’, Journal of Cell Biology 188 (1), 69–81.CrossRefGoogle Scholar
Waddington, C. H. (1957), The strategy of the genes, Allen & Unwin, London.Google Scholar
Walhout, A. J., Sordella, R., Lu, X., Hartley, J. L., Temple, G. F. et al. (2000), ‘Protein interaction mapping in C. elegans using proteins involved in vulval development’, Science 287(5450), 116–122.CrossRefGoogle Scholar
Winzeler, E. A., Shoemaker, D. D., Astromoff, A., Liang, H., Anderson, K., et al. (1999), ‘Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis’, Science 285(5429), 901–906.CrossRefGoogle Scholar
Wong, S. L., Zhang, L. V., Tong, A. H. Y., Li, Z., Goldberg, D. S. et al. (2004), ‘Combining biological networks to predict genetic interactions’, Proceedings of the National Academy of Sciences of the USA 101 (44), 15 682–15 687.CrossRefGoogle Scholar
Wood, V., (2006), ‘Schizosaccharomyces pombe comparative genomics: from sequence to systems’, Topics in Current Genetics 15, 233–285.Google Scholar
Ye, P., Peyser, B. D., Pan, X., Boeke, J. D., Spencer, F. A. et al. (2005), ‘Gene function prediction from congruent synthetic lethal interactions in yeast’, Molecular Systems Biology 1, 2005.0026.CrossRefGoogle Scholar
Zhong, W., & Sternberg, P. W. (2006), ‘Genome-wide prediction of C. elegans genetic interactions’, Science 311(5766), 1481–1484.CrossRefGoogle Scholar

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