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To dissect the genotype-phenotype landscape of a cell, it is necessary to understand interactions between genes. Building on the digenic protein-protein interaction network, Kuzmin et al. created a trigenic landscape of yeast by using a synthetic genetic array (see the Perspective by Walhout). Triple-mutant analyses indicated that the majority of genes with trigenic associations functioned within the same biological processes. These converged on networks identified in the digenic interaction landscape. Although the overall effects were weaker for trigenic than for digenic interactions, trigenic interactions were more likely to bridge biological processes in the cell. Science , this issue p. eaao1729 ; see also p. 269 Trigenic interactions in yeast link bioprocesses are explored. To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double-mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes, and essential genes were hubs on the trigenic network. Despite their functional enrichment, trigenic interactions tended to link genes in distant bioprocesses and displayed a weaker magnitude than digenic interactions. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance, including the genotype-to-phenotype relationship.

Genetic interactions have been reported to underlie phenotypes in a variety of systems, but the extent to which they contribute to complex disease in humans remains unclear. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions, but existing methods for identifying them from GWAS data tend to focus on testing individual locus pairs, which undermines statistical power. Importantly, a global genetic network mapped for a model eukaryotic organism revealed that genetic interactions often connect genes between compensatory functional modules in a highly coherent manner. Taking advantage of this expected structure, we developed a computational approach called BridGE that identifies pathways connected by genetic interactions from GWAS data. Applying BridGE broadly, we discover significant interactions in Parkinson’s disease, schizophrenia, hypertension, prostate cancer, breast cancer, and type 2 diabetes. Our novel approach provides a general framework for mapping complex genetic networks underlying human disease from genome-wide genotype data. Genetic interactions may contribute to phenotypic traits but are challenging to decipher. Here, the authors develop BridGE, a computational approach for identifying pathways connected by genetic interactions from GWAS data.

Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning model to generate genome-wide gene essentiality predictions for C. albicans and expand the largest functional genomics resource in this pathogen (the GRACE collection) by 866 genes. Using this model and chemogenomic analyses, we define the function of three uncharacterized essential genes with roles in kinetochore function, mitochondrial integrity, and translation, and identify the glutaminyl-tRNA synthetase Gln4 as the target of N-pyrimidinyl-β-thiophenylacrylamide (NP-BTA), an antifungal compound. The analysis of essential genes in pathogens can be used to discover potential antimicrobial targets. Here, the authors use a machine learning model and chemogenomic analyses to generate genome-wide gene essentiality predictions for the fungal pathogen Candida albicans , define the function of three uncharacterized essential genes, and identify the target of a new antifungal compound.

Providing access to quantitative genomic data is key to ensure large-scale data validation and promote new discoveries. TheCellMap.org serves as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) experiments with the budding yeast Saccharomyces cerevisiae. In particular, TheCellMap.org allows users to easily access, visualize, explore, and functionally annotate genetic interactions, or to extract and reorganize subnetworks, using data-driven network layouts in an intuitive and interactive manner.

Analysis of genetic interaction networks often involves identifying genes with similar profiles, which is typically indicative of a common function. While several profile similarity measures have been applied in this context, they have never been systematically benchmarked. We compared a diverse set of correlation measures, including measures commonly used by the genetic interaction community as well as several other candidate measures, by assessing their utility in extracting functional information from genetic interaction data. We find that the dot product, one of the simplest vector operations, outperforms most other measures over a large range of gene pairs. More generally, linear similarity measures such as the dot product, Pearson correlation or cosine similarity perform better than set overlap measures such as Jaccard coefficient. Similarity measures that involve L2-normalization of the profiles tend to perform better for the top-most similar pairs but perform less favorably when a larger set of gene pairs is considered or when the genetic interaction data is thresholded. Such measures are also less robust to the presence of noise and batch effects in the genetic interaction data. Overall, the dot product measure performs consistently among the best measures under a variety of different conditions and genetic interaction datasets.

The characterization of functional redundancy and divergence between duplicate genes is an important step in understanding the evolution of genetic systems. Large‐scale genetic network analysis in Saccharomyces cerevisiae provides a powerful perspective for addressing these questions through quantitative measurements of genetic interactions between pairs of duplicated genes, and more generally, through the study of genome‐wide genetic interaction profiles associated with duplicated genes. We show that duplicate genes exhibit fewer genetic interactions than other genes because they tend to buffer one another functionally, whereas observed interactions are non‐overlapping and reflect their divergent roles. We also show that duplicate gene pairs are highly imbalanced in their number of genetic interactions with other genes, a pattern that appears to result from asymmetric evolution, such that one duplicate evolves or degrades faster than the other and often becomes functionally or conditionally specialized. The differences in genetic interactions are predictive of differences in several other evolutionary and physiological properties of duplicate pairs. Synopsis Gene duplication and divergence serves as a primary source for new genes and new functions, and as such has broad implications on the evolutionary process. Duplicate genes within S. cerevisiae have been shown to retain a high degree of similarity with regard to many of their functional properties (Papp et al , 2004 ; Guan et al , 2007 ; Wapinski et al , 2007 ; Musso et al , 2008 ), and perturbation of duplicate genes has been shown to result in smaller fitness defects than singleton genes (Gu et al , 2003 ; DeLuna et al , 2008 ; Dean et al , 2008 ; Musso et al , 2008 ). Individual genetic interactions between pairs of genes and profiles of such interactions across the entire genome provide a new context in which to examine the properties of duplicate compensation. In this study we use the most recent and comprehensive set of genetic interactions in yeast produced to date (Costanzo et al , 2010 ) to address questions of duplicate retention and redundancy. We show that the ability for duplicate genes to buffer the deletion of a partner has three main consequences. First it agrees with previous work demonstrating that a high proportion of duplicate pairs are synthetic lethal, a classic indication of the ability to buffer one another functionally (DeLuna et al , 2008 ; Dean et al , 2008 ; Musso et al , 2008 ). Second, it reduces the number of genetic interactions observed between duplicate genes and the rest of the genome by masking interactions relating to common function from experimental detection. Third, this buffering of common interactions serves to reduce profile similarity in spite of common function (Figure 1 ). The compensatory ability of functionally similar duplicates buffers genetic interactions related to their common function (reducing the number of genetic interactions overall), while allowing the measurement of interactions related to any divergent function. Thus, even functionally similar duplicates may have dissimilar genetic interaction profiles. As previously surmised (Ihmels et al , 2007 ), duplicate genes under selection for dosage amplification have differing profile characteristics. We show that dosage‐mediated duplicates have much higher genetic interaction profile similarity than do other duplicate pairs. Furthermore, we show in a comparison with local neighbors on a protein–protein interaction (PPI) network, that although dosage‐mediated duplicates more often have higher similarity to each other than they do to their neighbors, the reverse is true for duplicates in general. That is, slightly divergent duplicate genes more often exhibit a higher similarity with a common neighbor on the PPI network than they do with each other, and that observation is consistent with the idea that common interactions are buffered while interactions corresponding to divergent functions are observed. We then asked whether duplicates’ genetic interactions that are not buffered appear in a symmetric or an asymmetric fashion. Previous work has established asymmetric patterns with regard to PPI degree (Wagner, 2002 ; He and Zhang, 2005 ), sequence divergence (Conant and Wagner, 2003 ; Zhang et al , 2003 ; Kellis et al , 2004 ; Scannell and Wolfe, 2008 ) and expression patterns (Gu et al , 2002b ; Tirosh and Barkai, 2007 ). Although genetic interactions are further removed from mechanism than protein–protein interactions, for example, they do offer a more direct measurement of functional consequence and, thus, may give a better indication of the functional differences between a duplicate pair. We found that duplicates exhibit a strikingly asymmetric pattern of genetic interactions, with the ratio of interactions between sisters commonly exceeding 7:1 (Figure 4A ). The observations differ significantly from random simulations in which genetic interactions were redistributed between sisters with equal probability (Figure 4A ). Moreover, the directionality of this interaction asymmetry agrees with other physiological properties of duplicate pairs. For example, the sister with more genetic interactions also tends to have more protein–protein interactions and also tends to evolve at a slower rate (Figure 4B ). Genetic interaction degree and profiles can be used to understand the functional divergence of particular duplicates pairs. As a case example, we consider the whole‐genome‐duplication pair CIK1–VIK1 . Each of these genes encode proteins that form distinct heterodimeric complexes with the microtubule motor protein Kar3 (Manning et al , 1999 ). Although each of these proteins depend on a direct physical interaction with Kar3, Cik1 has a much higher profile similarity to Kar3 than does Vik1 ( r =0.5 and r =0.3, respectively). Consistent with its higher similarity, Δcik1 and Δkar3 exhibit several similar phenotypes, including abnormally short spindles, chromosome loss and delayed cell cycle progression (Page et al , 1994 ; Manning et al , 1999 ). In contrast, a Δvik1 mutant strain exhibits no overt phenotype (Manning et al , 1999 ). Duplicate genes show significantly fewer interactions than singleton genes, and functionally similar duplicates can exhibit dissimilar profiles because common interactions are ‘hidden’ due to buffering. Genetic interaction profiles provide insights into evolutionary mechanisms of duplicate retention by distinguishing duplicates under dosage selection from those retained because of some divergence in function. The genetic interactions of duplicate genes evolve in an extremely asymmetric way and the directionality of this asymmetry correlates well with other evolutionary properties of duplicate genes. Genetic interaction profiles can be used to elucidate the divergent function of specific duplicate pairs.

Synthetic genetic array (SGA) analysis automates yeast genetics, enabling high-throughput construction of ordered arrays of double mutants. Quantitative colony sizes derived from SGA analysis can be used to measure cellular fitness and score for genetic interactions, such as synthetic lethality. Here we show that SGA colony sizes also can be used to obtain global maps of meiotic recombination because recombination frequency affects double-mutant formation for gene pairs located on the same chromosome and therefore influences the size of the resultant double-mutant colony. We obtained quantitative colony size data for ~1.2 million double mutants located on the same chromosome and constructed a genome-scale genetic linkage map at ~5 kb resolution. We found that our linkage map is reproducible and consistent with previous global studies of meiotic recombination. In particular, we confirmed that the total number of crossovers per chromosome tends to follow a simple linear model that depends on chromosome size. In addition, we observed a previously unappreciated relationship between the size of linkage regions surrounding each centromere and chromosome size, suggesting that crossovers tend to occur farther away from the centromere on larger chromosomes. The pericentric regions of larger chromosomes also appeared to load larger clusters of meiotic cohesin Rec8, and acquire fewer Spo11-catalyzed DNA double-strand breaks. Given that crossovers too near or too far from centromeres are detrimental to homolog disjunction and increase the incidence of aneuploidy, our data suggest that chromosome size may have a direct role in regulating the fidelity of chromosome segregation during meiosis.

Building Information Modeling (BIM) has been rapidly transforming the Architectural, Engineering, & Construction (AEC) industry throughout the lifecycle of the building. BIM functions as a single database of fully integrated and interoperable information that can be used by all members of the design and construction team and ultimately, by operators throughout a facility's lifecycle (Holenss G.V.R 2008). The advantages of utilizing BIM in the Facility Management (FM) stage have been well documented in the literature (Stravoravdis and Ashworth 2015.), (Gokstorp 2012.) & (Becerik-Gerber et al. 2012.). The initial hypothesis for this study was that the use of BIM by our in-house FM staff would significantly reduce the amount of time needed to locate HVAC equipment in the building that needs maintenance or repair. However, currently, BIM is not as widely implemented in the FM stage as it is in the design stage (Akcamete et al 2010.). Lack of or restricted access to the BIM modelfor multiple existing buildings is the first major challenge to BIM adoption for FM. The lack of BIM software experience for FM personnel and the high purchase cost of the software also presents significant challenges. Most of the latest Building Management Systems (BMS) inform the FM personnel about the problematic equipment through their equipment IDs but fail to locate the coordinates of that equipment. The FM personnel mostly use paper-based drawings and spend hours locating problematic equipment in the building that is flagged by the BMS. For many building managers and design professionals, the visualization of building information for analysis, benchmarking, and diagnostics remains a time-intensive, do-it-yourself undertaking (Lehrer 2010.). Hence, the goal of the study is to provide FM personnel with a simple-to-use spatial visualization dashboard that would save them time and money. This study showcases a methodology to spatially visualize the performance of buildings' HVAC systems systematically. The proposed workflow is tested on an existing office building in Utah and as part of the scope of the study, only the performance of Variable Air Volume (VAV) boxes is spatially visualized. An automated dashboard is createdfor visualizing the VAVs' performance score on a color-coded map. As the dashboard is automated to updated regularly, FM personnel can track the performance and exact location of the problematic VAVs just by opening the dashboard. FM personnel may now infer correlations in the equipment failure, by integrating the time and spatial relationships on the dashboard. Even though previous studies have already dealt with visualizing the sensor data, like temperature, on floor plans, the novel idea of this study is to visualize equipment's performance on an ongoing basis. Just visualizing the sensor data would not help FM personnel diagnose the issue. Future studies may investigate ways to scale up the scope of the study to large buildings. Future studies may also provide preventive maintenance recommendations on the dashboard by analyzing real-time and historical data using machine learning techniques.

Systematic complex genetic interaction studies have provided insight into high-order functional redundancies and genetic network wiring of the cell. Here, we describe a method for screening and quantifying trigenic interactions from ordered arrays of yeast strains grown on agar plates as individual colonies. The protocol instructs users on the trigenic synthetic genetic array analysis technique, τ-SGA, for high-throughput screens. The steps describe construction of the double-mutant query strains and the corresponding single-mutant control query strains, which are screened in parallel in two replicates. The screening experimental set-up consists of sequential replica-pinning steps that enable automated mating, meiotic recombination and successive haploid selection steps for the generation of triple mutants, which are scored for colony size as a proxy for fitness, which enables the calculation of trigenic interactions. The procedure described here was used to conduct 422 trigenic interaction screens, which generated ~460,000 yeast triple mutants for trigenic interaction analysis. Users should be familiar with robotic equipment required for high-throughput genetic interaction screens and be proficient at the command line to execute the scoring pipeline. Large-scale screen computational analysis is achieved by using MATLAB pipelines that score raw colony size data to produce τ-SGA interaction scores. Additional recommendations are included for optimizing experimental design and analysis of smaller-scale trigenic interaction screens by using a web-based analysis system, SGAtools. This protocol provides a resource for those who would like to gain a deeper, more practical understanding of trigenic interaction screening and quantification methodology. This protocol describes procedures for high-throughput analysis of trigenic interactions in yeast. Triple-mutant strains generated in a series of automated replica-pinning steps are grown on agar plates as individual colonies, and interactions are quantified with the trigenic synthetic genetic array scoring method.

Essential genes have been effectively studied using temperature-sensitive alleles in yeast. Li et al . construct a large collection of temperature-sensitive yeast mutants and show how it enables high-throughput analyses of the function of essential genes. Conditional temperature-sensitive (ts) mutations are valuable reagents for studying essential genes in the yeast Saccharomyces cerevisiae . We constructed 787 ts strains, covering 497 (∼45%) of the 1,101 essential yeast genes, with ∼30% of the genes represented by multiple alleles. All of the alleles are integrated into their native genomic locus in the S288C common reference strain and are linked to a kanMX selectable marker, allowing further genetic manipulation by synthetic genetic array (SGA)–based, high-throughput methods. We show two such manipulations: barcoding of 440 strains, which enables chemical-genetic suppression analysis, and the construction of arrays of strains carrying different fluorescent markers of subcellular structure, which enables quantitative analysis of phenotypes using high-content screening. Quantitative analysis of a GFP-tubulin marker identified roles for cohesin and condensin genes in spindle disassembly. This mutant collection should facilitate a wide range of systematic studies aimed at understanding the functions of essential genes.