DOI: 10.5061/dryad.4291s
title: Data from: A global genetic interaction network maps a wiring diagram of cellular function

description: INTRODUCTION: Genetic interactions occur when mutations in two or more
 genes combine to generate an unexpected phenotype. An extreme negative or
 synthetic lethal genetic interaction occurs when two mutations, neither
 lethal individually, combine to cause cell death. Conversely, positive
 genetic interactions occur when two mutations produce a phenotype that is
 less severe than expected. Genetic interactions identify functional
 relationships between genes and can be harnessed for biological discovery
 and therapeutic target identification. They may also explain a
 considerable component of the undiscovered genetics associated with human
 diseases. Here, we describe construction and analysis of a comprehensive
 genetic interaction network for a eukaryotic cell. RATIONALE: Genome
 sequencing projects are providing an unprecedented view of genetic
 variation. However, our ability to interpret genetic information to
 predict inherited phenotypes remains limited, in large part due to the
 extensive buffering of genomes, making most individual eukaryotic genes
 dispensable for life. To explore the extent to which genetic interactions
 reveal cellular function and contribute to complex phenotypes, and to
 discover the general principles of genetic networks, we used automated
 yeast genetics to construct a global genetic interaction network. RESULTS:
 We tested most of the ~6000 genes in the yeast Saccharomyces cerevisiae
 for all possible pairwise genetic interactions, identifying nearly 1
 million interactions, including ~550,000 negative and ~350,000 positive
 interactions, spanning ~90% of all yeast genes. Essential genes were
 network hubs, displaying five times as many interactions as nonessential
 genes. The set of genetic interactions or the genetic interaction profile
 for a gene provides a quantitative measure of function, and a global
 network based on genetic interaction profile similarity revealed a
 hierarchy of modules reflecting the functional architecture of a cell.
 Negative interactions connected functionally related genes, mapped core
 bioprocesses, and identified pleiotropic genes, whereas positive
 interactions often mapped general regulatory connections associated with
 defects in cell cycle progression or cellular proteostasis. Importantly,
 the global network illustrates how coherent sets of negative or positive
 genetic interactions connect protein complex and pathways to map a
 functional wiring diagram of the cell. CONCLUSION: A global genetic
 interaction network highlights the functional organization of a cell and
 provides a resource for predicting gene and pathway function. This network
 emphasizes the prevalence of genetic interactions and their potential to
 compound phenotypes associated with single mutations. Negative genetic
 interactions tend to connect functionally related genes and thus may be
 predicted using alternative functional information. Although less
 functionally informative, positive interactions may provide insights into
 general mechanisms of genetic suppression or resiliency. We anticipate
 that the ordered topology of the global genetic network, in which genetic
 interactions connect coherently within and between protein complexes and
 pathways, may be exploited to decipher genotype-to-phenotype
 relationships.
description: # Data from: A global genetic interaction network maps a wiring diagram of
 cellular function Dataset DOI: [10.5061/dryad.4291s](10.5061/dryad.4291s)
 ## Description of the data and file structure #### Data File S1. Raw
 genetic interaction datasets - Pair-wise interaction format This folder
 contains complete SGA genetic interaction data for the following: • A list
 of all query and array mutant strains represented in the genetic
 interaction network along with their corresponding fitness estimates •
 Nonessential x Nonessential network (NxN) • Essential x Essential network
 (ExE) • Essential x Nonessential network (ExN) • Genetic interactions
 involving DAmP alleles of essential genes • Genetic interactions for HSP90
 and corresponding controls The interaction datasets are provided in a
 tab-delimited format with 11 columns: • Query ORF • Query gene name 40 •
 Array ORF • Array gene name • Array Type (DMA or TSA) and Temperature (26o
 C or 30o C) • Genetic interaction score (ε) • P-value • Query single
 mutant fitness (SMF) • Array SMF • Double mutant fitness • Double mutant
 fitness standard deviation Data File S1_Raw genetic interaction datasets -
 Pair-wise interaction format.zip #### Data File S2. Raw genetic
 interaction datasets - Matrix format This file contains complete SGA
 genetic interaction data matrix for the following: • Nonessential x
 Nonessential network (NxN) • Essential x Essential network (ExE) •
 Combined essential and nonessential network (ExN) Data File S2_Raw genetic
 interaction datasets - Matrix format.zip #### Data File S3. Genetic
 interaction profile similarity matrices Matrix files containing genetic
 interaction profile similarity values (as measured by Pearson correlation)
 for every pair of mutant strains in the dataset. Similarity values were
 computed for essential (ExE), non-essential (NxN) and the global
 similarity network derived from a combined set of all genetic interactions
 (ExE, NxN, ExN) as described above (see "Constructing genetic
 interaction profile similarity networks"). Each matrix contains 2
 sets of row and column headers, providing a unique allele name for every
 mutant strain (row & column header #1) as well as a systematic ORF
 name (row & column header #2). Data File S3_Genetic interaction
 profile similarity matrices.zip #### Data File S4. GO bioprocess functions
 predicted by the nonessential and essential similarity networks using a
 K-nearest neighbor approach This file reports the performance of gene
 function prediction for non-essential or essential genes based on genetic
 interaction profiles. For both classes of genes (either nonessential or
 essential), the performance of a KNN classifier is reported as the
 Precision at 25% Recall based on interactions derived from TS queries
 (PR_TSQ) or nonessential deletion queries (PR25_SN). Although analyses
 were performed using complete genetic interaction profiles (e.g. negative
 and positive genetic interactions), similar prediction performance was
 obtained using genetic interaction profiles based on negative interactions
 alone. Data File S4_GO bioprocess functions predicted by the nonessential
 and essential similarity networks using a K-nearest neighbor approach.xlsx
 #### Data File S5. SAFE analysis\_Gene cluster identity and functional
 enrichments This file lists the results from SAFE analysis of the global
 genetic profile similarity network (Fig. 1 and Fig. 2). Functional terms
 enriched within specific network clusters associated with GO biological
 processes (14) and/or protein complexes (Data File S12). A list of genes
 comprising each bioprocess-enriched cluster shown on the global similarity
 network is also provided. Functional terms enriched within specific
 network clusters associated with cell compartments (17, 119) are all shown
 on Fig. 2B. Data File S5_SAFE analysis_Gene cluster identity and
 functional enrichments.xlsx #### Data File S6. Genetic profile
 similarity-based hierarchy analysis The first tab (“Gene to hierarchy
 cluster mapping”) lists the clusters identified at each level of the
 genetic interaction-based hierarchy and the deletion and TS allele array
 mutants assigned to each cluster. Examples of clusters described in the
 main text are highlighted. The subsequent 9 tabs indicate enrichment of
 clusters resolved at the specified profile similarity range for specific
 cell compartments (Cyclops_enrich), biological processes (GO BP_enrich),
 protein complexes (complex_enrich) and KEGG pathways (KEGG_enrich). The
 final tab in the file indicates the clusters used to map the functional
 distribution of negative and positive interactions shown in Fig. 5D. Data
 File S6_Genetic profile similarity-based hierarchy analysis.xlsx #### Data
 File S7. Pleiotropic gene analysis This file lists nonessential and
 essential query genes associated with high confidence pleiotropy scores
 based on their genetic interactions derived from the TSA (Essential
 derived pleiotropy) and DMA (Nonessential derived pleiotropy). The file
 also contains a second list of nonessential and essential query genes that
 participate in many genetic interactions but exhibited low pleiotropy
 scores indicating that these genes are more functionally specific. Data
 File S7_Pleiotropic gene analysis.xlsx #### Data File S8. Mass
 spectrometric evidence for Ipa1 interactions This file lists proteins
 identified with high confidence as specific physical interactors with
 strains expressing Ipa1-GFP from its endogenous locus or Ipa1-HA from a
 galactose-inducible plasmid. Data File S8_Mass spectrometric evidence for
 Ipa1 interactions.xlsx #### Data File S9. High and low interaction degree
 genes This file lists the negative and positive interaction degree
 associated with every nonessential deletion (sn#), essential TS (tsq#),
 and DAmP (damp#) query mutant strain screened against the DMA (“query
 degree X DMA” tab) and/or TSA (“query degree X TSA” tab). A subset of
 strains were found to carry a second, spontaneous suppressor mutation that
 affected fitness of the query mutant strain. Strains carrying a suppressor
 mutation mapped through SGA analysis are indicated (“-supp”). Query
 mutants comprising the 20% highest and lowest degree groups of strains are
 indicated. Furthermore, a “Co-batch signal” rank is provided for every
 query (see “Co-batch filtering of query mutant strains”). Low ranks
 correspond to evidence for lingering batch effects. Another column, “ Gene
 with correlated GI profiles that are co-annotated with the query gene
 (%)", provides the percent of correlated gene pairs that are
 co-annotated to the particular query. A low negative interaction degree
 (e.g. 20% lowest negative interaction degree) coupled with a low co-batch
 rank (e.g. &lt; ~0.2) and a low fraction of correlated pairs that share a
 similar functional annotation with a given query strain (e.g. &lt; ~0.15)
 may be indicative of a low confidence screen. However, these criteria
 should be considered as loose indicators and not definitive metrics of
 screen quality and thus, should not be used as strict filters on the
 global interaction dataset. Another list (“Queries removed - batch
 effects” tab) indicates ~300 query strains that exhibited severe
 systematic batch effects and thus were removed from the indicated data
 set. Finally, two additional tabs provide the negative and positive
 interaction degree associated with every nonessential (“nonessential array
 degree” tab) and essential (“essential array degree” tab) array mutant,
 respectively. Data File S9_High and low interaction degree genes.xls ####
 Data File S10. Correlation analysis of query strain GI degree As a
 complement to analysis of array strains (fig. S11-S12), GI degrees were
 calculated for query strains by counting negative interactions (tab 1,
 interactions with DMA strains; tab 2, interactions with TSA strains) and
 by counting positive interactions (tab 3, interactions with DMA strains;
 tab 4, interactions with TSA strains). Essential and nonessential queries
 were analyzed separately and results are labeled by grouped column
 headers. Wilcoxon rank-sum tests compared the GI degree in paired gene
 sets defined by absence and presence of each binary feature tested (top
 table). If the P-value is significant (&lt; 0.05), the “Test result”
 column describes the degree of the set of genes for which the listed
 binary feature is true (compared to the set for which the feature is
 false). Tests were not performed, indicated by “N/A”, if data were present
 for fewer than 50 strains; strains with missing data were excluded from
 the tests. Pearson’s correlation (column labeled “r”) was used to measure
 associations between GI degree and features that are continuous or counts
 (bottom table). Uncorrected P-values are shown. The features examined in
 this analysis are described above (see Methods section entitled, “ Genetic
 interaction degree and frequency analysis”). Given that analysis of
 different features required using different statistical tests and some
 features are not expected to be independent of each other, no multiple
 hypotheses correction procedures were used. We do note that 31 gene
 features were tested. Data File S10_Correlation analysis of query strain
 GI degree.xlsx #### Data File S11. Nonessential and essential GI hub
 functional enrichment analysis This file lists GO biological process,
 molecular function and cellular component terms that are enriched among of
 10% of array strains with the most negative and 10% of array strains with
 the most positive interactions identified in the ExE network. Enrichments
 are also included for the 5% of array strains with the most negative
 interactions and 5% of array strains with the most positive interactions
 in the NxN genetic interaction network. Data File S11_Nonessential and
 essential GI hub functional enrichment analysis.xlsx #### Data File S12.
 Protein complex standard This file provides a list of protein complexes
 compiled from two sources: Baryshnikova 2010 (5) and Benschop 2010 (117).
 Data File S12_Protein complex standard.xlsx #### Data File S13. Genetic
 interaction enrichment among protein complexes This file lists all
 possible pairs of protein complexes tested in the ExE, NxN and ExN
 networks. Enrichment for negative and positive interactions between genes
 in the same complex or between genes in different complexes is indicated.
 In addition, enrichment for genetic interaction in general (Combined
 interaction enrichment) regardless of the type is also indicated. Finally,
 Interaction Sign Bias indicates the distribution of interactions between
 genes within the same complex or between genes in different complexes. The
 interaction sign bias is computed as the mean over all interactions for
 within a given complex or between a pair of complexes. For example, an
 interaction sign bias of -1 indicates that all interactions identified
 between a set of genes encoding complexes members are negative, whereas a
 score of 1 indicates that only positive interactions were identified
 between a particular set of protein complex encoding genes. Rows
 highlighted in blue indicated complex-complex pairs enriched for negative
 interactions where greater than 75% of all interactions detected were
 negative. Rows highlighted in yellow indicated complex-complex pairs
 enriched for negative interactions where greater than 75% of all
 interactions detected were positive. The analysis shown in Fig. 7 is based
 on subset of complexes composed of 75% essential genes (i.e. considered
 essential complexes) or 75% nonessential genes (i.e. considered
 nonessential complexes). The complexes used for this analysis and their
 enrichment results are listed in the tabs labeled, “_filtered”. The tabs
 named “all” list within and between complex enrichment for all protein
 complexes without prior filtering of complexes composed of less than 75%
 essential or 75% nonessential genes. Data File S13_Genetic interaction
 enrichment among protein complexes.xlsx #### Data File S14. D.
 melanogaster S2 cell fitness This file provides D. melanogaster S2 cell
 fitness upon RNAi-mediated 26S proteasome depletion and Bortezomib
 treatment. Data File S14_D. melanogaster S2 cell fitness.xlsx #### Data
 File S15. Protein complex interaction enrichment and bias This file
 indicates fold enrichment and biases in positive vs. negative interaction
 frequency for protein complexes and is described in detail above (see
 ìAnalysis of protein complexes exhibiting a positive interaction
 enrichment biasî). Rows highlighted in yellow indicate protein complexes
 that show &gt; 1.5X enrichment for positive interactions (ìE_fold_pos)
 stronger enrichment for positive versus negative interactions when
 screened against the essential TSA. The file consists of the following
 columns: (A) Protein complex name (B) Number of complex member-encoding
 query genes screened against the DMA (ìqueries_vs_DMAî). (C) Number of
 complex member-encoding query genes screened against the TSA
 (ìqueries_vs_TSAî). (D) Nonessential-negative GI fold enrichment
 (ìN_fold_negî): negative interaction fold enrichment for a complex of
 interest with nonessential genes not in the complex. (E)
 Essential-negative GI fold enrichment (ìE_fold_negî): negative interaction
 fold enrichment for a complex of interest with essential genes not in the
 complex. (F) Nonessential-positive GI fold enrichment (ìN_fold_posî):
 positive interaction fold enrichment for a complex of interest with
 nonessential genes not in the complex. (G) Essential-positive GI fold
 enrichment (ìE_fold_posî): positive interaction fold enrichment for a
 complex of interest with essential genes not in the complex. Complexes
 with a positive GI enrichment &gt; 1.5X are highlighted in yellow. These
 values were used to generate Fig. 8C. (H) Positive GI bias with essential
 genes (ìposGI_bias_with_Eî): the relative positive:negative enrichment
 ratio of essential to nonessential genes for the complex of interest
 (calculated as [D/E]/[F/G]). Complexes with a positive GI enrichment &gt;
 1 and a positive GI bias &gt; 1.5 are highlighted in yellow. These values
 were used to generate Fig. 8D. (I) Positive GI bias with nonessential
 genes (posGI_bias_with_Nî): the relative positive:negative enrichment
 ratio of nonessential to essential genes for the complex of interest
 (calculated as [F/G]/[D/E]). Data File S15_Protein complex interaction
 enrichment and bias.xlsx #### Data File S16. Genetic suppression analysis
 This file includes raw data from spot dilution growth assays to identify
 positive interactions that can be classified as genetic suppression. The
 suppression score is based on visual assessment of double mutant strain
 growth relative to a wild type and single mutant control strains. The
 score reflects strength of suppression with a score of 4 indicative of a
 strong suppression interaction where double mutant growth exceeded growth
 of the sickest single mutant and a score of 0 indicates failure to confirm
 a suppression interaction. Data File S16_Genetic suppression analysis.xlsx
 #### Data File S17. YNL181W chemical genetics data Relative growth of a
 YNL181W-DAmP strain (CG score) measured in the presence of 92 different
 compounds. Data File S17_YNL181W chemical genetics data.xlsx ### Change
 Log June 9, 2025: Uploaded the file
 strain_ids_and_single_mutant_fitness.xlsx contained within an updated Data
 file S1 zip.