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The Saccharomyces genome-deletion project created >5900 'molecularly barcoded' yeast knockout mutants (YKO mutants). The YKO mutant collections have facilitated large-scale analyses of a multitude of mutant phenotypes. For example, both synthetic genetic array (SGA) and synthetic-lethality analysis by microarray (SLAM) methods have been used for synthetic-lethality screens. Global analysis of synthetic lethality promises to identify cellular pathways that 'buffer' each other biologically. The combination of global synthetic-lethality analysis, together with global protein-protein interaction analyses, mRNA expression profiling and functional profiling will, in principle, enable construction of a cellular 'wiring diagram' that will help frame a deeper understanding of human biology and disease.

Comprehensive collections of open reading frame (ORF) deletion mutant strains exist for the budding yeast Saccharomyces cerevisiae. With great prescience, these strains were designed with short molecular bar codes or TAGs that uniquely mark each deletion allele, flanked by shared priming sequences. These features have enabled researchers to handle yeast mutant collections as complex pools of approximately 6000 strains. The presence of any individual mutant within a pool can be assessed indirectly by measuring the relative abundance of its corresponding TAG(s) in genomic DNA prepared from the pool. This is readily accomplished by wholesale polymerase chain reaction (PCR) amplification of the TAGs using fluorescent oligonucleotide primers that recognize the common flanking sequences, followed by hybridization of the labeled PCR products to a TAG oligonucleotide microarray. Here we describe a method-diploid-based synthetic lethality analysis by microarray (dSLAM)-whereby such pools can be manipulated to rapidly construct and assess the fitness of 6000 double-mutant strains in a single experiment. Analysis of double-mutant strains is of growing importance in defining the spectrum of essential cellular functionalities and in understanding how these functionalities interrelate.

Background In a genetic interaction, the phenotype of a double mutant differs from the combined phenotypes of the underlying single mutants. When the single mutants have no growth defect, but the double mutant is lethal or exhibits slow growth, the interaction is termed synthetic lethality or synthetic fitness. These genetic interactions reveal gene redundancy and compensating pathways. Recently available large-scale data sets of genetic interactions and protein interactions in Saccharomyces cerevisiae provide a unique opportunity to elucidate the topological structure of biological pathways and how genes function in these pathways. Results We have defined congruent genes as pairs of genes with similar sets of genetic interaction partners and constructed a genetic congruence network by linking congruent genes. By comparing path lengths in three types of networks (genetic interaction, genetic congruence, and protein interaction), we discovered that high genetic congruence not only exhibits correlation with direct protein interaction linkage but also exhibits commensurate distance with the protein interaction network. However, consistent distances were not observed between genetic and protein interaction networks. We also demonstrated that congruence and protein networks are enriched with motifs that indicate network transitivity, while the genetic network has both transitive (triangle) and intransitive (square) types of motifs. These results suggest that robustness of yeast cells to gene deletions is due in part to two complementary pathways (square motif) or three complementary pathways, any two of which are required for viability (triangle motif). Conclusion Genetic congruence is superior to genetic interaction in prediction of protein interactions and function associations. Genetically interacting pairs usually belong to parallel compensatory pathways, which can generate transitive motifs (any two of three pathways needed) or intransitive motifs (either of two pathways needed).

We predicted gene function using synthetic lethal genetic interactions between null alleles in Saccharomyces cerevisiae. Phenotypic and protein interaction data indicate that synthetic lethal gene pairs function in parallel or compensating pathways. Congruent gene pairs, defined as sharing synthetic lethal partners, are in single pathway branches. We predicted benomyl sensitivity and nuclear migration defects using congruence; these phenotypes were uncorrelated with direct synthetic lethality. We also predicted YLL049W as a new member of the dynein-dynactin pathway and provided new supporting experimental evidence. We performed synthetic lethal screens of the parallel mitotic exit network (MEN) and Cdc14 early anaphase release pathways required for late cell cycle. Synthetic lethal interactions bridged genes in these pathways, and high congruence linked genes within each pathway. Synthetic lethal interactions between MEN and all components of the Sin3/Rpd3 histone deacetylase revealed a novel function for Sin3/Rpd3 in promoting mitotic exit in parallel to MEN. These in silico methods can predict phenotypes and gene functions and are applicable to genomic synthetic lethality screens in yeast and analogous RNA interference screens in metazoans.

Saccharomyces cerevisiae knockout collection TAG microarrays are an emergent platform for rapid, genome-wide functional characterization of yeast genes. TAG arrays report abundance of unique oligonucleotide ‘TAG’ sequences incorporated into each deletion mutation of the yeast knockout collection, allowing measurement of relative strain representation across experimental conditions for all knockout mutants simultaneously. One application of TAG arrays is to perform genome-wide synthetic lethality screens, known as synthetic lethality analyzed by microarray (SLAM). We designed a fully defined spike-in pool to resemble typical SLAM experiments and performed TAG microarray hybridizations. We describe a method for analyzing two-color array data to efficiently measure the differential knockout strain representation across two experimental conditions, and use the spike-in pool to show that the sensitivity and specificity of this method exceed typical current approaches.