CoChat
Sign Up
Cited by 112Fungal and yeast genetics researchComputational Drug Discovery MethodsBioinformatics and Genomic Networks

The DOI matches its Crossref record; 75 of the 76 references it cites were found in a registry or an index.

Systematic analysis of genome-wide fitness data in yeast reveals novel gene function and drug action

Open access
Hillenmeyer, Maureen E, Ericson, Elke, Davis, Ronald W, Nislow, Corey, Koller, Daphne, Giaever, Guri·2010·Genome Biology
DOI 10.1186/gb-2010-11-3-r30PMC PMC2864570
Open this paper in CoChat — free accountOpen PDF

Abstract

We systematically analyzed the relationships between gene fitness profiles (co-fitness) and drug inhibition profiles (co-inhibition) from several hundred chemogenomic screens in yeast. Co-fitness predicted gene functions distinct from those derived from other assays and identified conditionally dependent protein complexes. Co-inhibitory compounds were weakly correlated by structure and therapeutic class. We developed an algorithm predicting protein targets of chemical compounds and verified its accuracy with experimental testing. Fitness data provide a novel, systems-level perspective on the cell.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/gb-2010-11-3-r30

Cites

75 of 75 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Predicting shared gene functions using co-fitness and other datasets. (a) Precision-recall curve for each of four high-throughput datasets, illustrating the prediction accuracy of each dataset to expert-curated reference interactions [13]. The optimal dataset has both high precision and high coverage (a point in the upper right corner). TP is the number of true positive interactions captured by the dataset, FP is the number of false positives, and FN is the number of false negatives. Synthetic lethality networks have only one value for precision and coverage because their links are binary. Correlation-based networks, including co-fitness, co-expression, and physical interactions, use an adjustable correlation threshold to define interactions: each point corresponds to one threshold. (b) Each cell in the matrix summarizes the precision that each dataset achieved for each function, ranging from low (black) to high (red), hierarchically clustered on both axes. (c-f) Individual precision-recall curves for four of the gene categories, from which the values for (b) were calculated. The remaining 28 categories are shown in Supplementary Figure 2 in Additional file 1 in Additional file 1.
Figure 1 Predicting shared gene functions using co-fitness and other datasets. (a) Precision-recall curve for each of four high-throughput datasets, illustrating the prediction accuracy of each dataset to expert-curated reference interactions [13]. The optimal dataset has both high precision and high coverage (a point in the upper right corner). TP is the number of true positive interactions captured by the dataset, FP is the number of false positives, and FN is the number of false negatives. Synthetic lethality networks have only one value for precision and coverage because their links are binary. Correlation-based networks, including co-fitness, co-expression, and physical interactions, use an adjustable correlation threshold to define interactions: each point corresponds to one threshold. (b) Each cell in the matrix summarizes the precision that each dataset achieved for each function, ranging from low (black) to high (red), hierarchically clustered on both axes. (c-f) Individual precision-recall curves for four of the gene categories, from which the values for (b) were calculated. The remaining 28 categories are shown in Supplementary Figure 2 in Additional file 1 in Additional file 1.CC BY · PubMed Central
Figure 2 Compound clusters, extracted from genome-wide two-way clustering on the complete dataset (using all genes and all compounds). (a) Antifungal azoles in the heterozygous data, with high structural similarity. All induce sensitivity in strains deleted for ERG11, an azole target, and related pleiotropic drug resistance (PDR) transport-related genes; fluconazole (inset) did not appear in this cluster, though it is also thought to target Erg11. (b) Psychoactive compounds that target dopamine, serotonin, and acetylcholine receptors in human; these compounds cluster in the heterozygous dataset based on inhibition of small ribosomal subunit genes and Cox17, potential targets in both yeast and human. (c) Examples of drugs with similar homozygous fitness profiles; the similarity is due to shared sensitivity of strains deleted for multi-drug resistance (MDR) genes with roles in vesicle-mediated transport.
Figure 2 Compound clusters, extracted from genome-wide two-way clustering on the complete dataset (using all genes and all compounds). (a) Antifungal azoles in the heterozygous data, with high structural similarity. All induce sensitivity in strains deleted for ERG11, an azole target, and related pleiotropic drug resistance (PDR) transport-related genes; fluconazole (inset) did not appear in this cluster, though it is also thought to target Erg11. (b) Psychoactive compounds that target dopamine, serotonin, and acetylcholine receptors in human; these compounds cluster in the heterozygous dataset based on inhibition of small ribosomal subunit genes and Cox17, potential targets in both yeast and human. (c) Examples of drugs with similar homozygous fitness profiles; the similarity is due to shared sensitivity of strains deleted for multi-drug resistance (MDR) genes with roles in vesicle-mediated transport.CC BY · PubMed Central
Figure 3 The limited correlation between Tanimoto structural similarity and co-fitness in the heterozygous and homozygous datasets suggests that chemical structure influences inhibition patterns but does not exclusively define them. Each point represents a pair of compounds; to allow for comparison between (a) heterozygous and (b) homozygous datasets, for this figure we used only pairs of compounds that were tested in both datasets.
Figure 3 The limited correlation between Tanimoto structural similarity and co-fitness in the heterozygous and homozygous datasets suggests that chemical structure influences inhibition patterns but does not exclusively define them. Each point represents a pair of compounds; to allow for comparison between (a) heterozygous and (b) homozygous datasets, for this figure we used only pairs of compounds that were tested in both datasets.CC BY · PubMed Central
Figure 4 The ability of co-inhibition to predict shared therapeutic use was higher for the homozygous than for the heterozygous dataset. As reference, we used a set of compound pairs with shared therapeutic use (WHO ATC level 3 code). As in Figure 3, we used only pairs of compounds that were tested in both the (a) heterozygous and (b) homozygous datasets.
Figure 4 The ability of co-inhibition to predict shared therapeutic use was higher for the homozygous than for the heterozygous dataset. As reference, we used a set of compound pairs with shared therapeutic use (WHO ATC level 3 code). As in Figure 3, we used only pairs of compounds that were tested in both the (a) heterozygous and (b) homozygous datasets.CC BY · PubMed Central
Figure 5 Drug target prediction accuracy (ten-fold cross validation) using one of four algorithms: log2 ratio fitness defect with a simple decision stump model (red); log2 ratio fitness defect with a richer random forest model (green); the chemical structure similarity features with the random forest model (blue); and all features with the random forest model (purple). Each point represents a threshold for the algorithm. For the decision stump, each point represents a single log2 ratio value, and for the random forest, each point represents the algorithm's decision as a mode of decision trees that use the available features (see Materials and methods). The accuracies of other algorithms are shown in Supplementary Figure 7 in Additional file 1. (a) Performance on the expert-curated reference set of compounds and their known interacting yeast proteins. (b) Performance on DrugBank protein-compound interactions (mostly human) mapped to yeast through protein homology.
Figure 5 Drug target prediction accuracy (ten-fold cross validation) using one of four algorithms: log2 ratio fitness defect with a simple decision stump model (red); log2 ratio fitness defect with a richer random forest model (green); the chemical structure similarity features with the random forest model (blue); and all features with the random forest model (purple). Each point represents a threshold for the algorithm. For the decision stump, each point represents a single log2 ratio value, and for the random forest, each point represents the algorithm's decision as a mode of decision trees that use the available features (see Materials and methods). The accuracies of other algorithms are shown in Supplementary Figure 7 in Additional file 1. (a) Performance on the expert-curated reference set of compounds and their known interacting yeast proteins. (b) Performance on DrugBank protein-compound interactions (mostly human) mapped to yeast through protein homology.CC BY · PubMed Central
Figure 6 Overexpression of Exo84 alleviates the sensitivity of the control to 27 μM nocodazole. The optical density at 595 nm over time for wild-type BY4743 cells harboring the Exo84 overexpression construct compared to that of controls (ctrl) transformed with plasmid lacking a gene insert (for details, see Materials and methods, and for replicates, see Supplementary Figure 10 in Additional file 1).
Figure 6 Overexpression of Exo84 alleviates the sensitivity of the control to 27 μM nocodazole. The optical density at 595 nm over time for wild-type BY4743 cells harboring the Exo84 overexpression construct compared to that of controls (ctrl) transformed with plasmid lacking a gene insert (for details, see Materials and methods, and for replicates, see Supplementary Figure 10 in Additional file 1).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 76 references

Cited by

Sorted by citations

Items 101–110 of 110

Related

See all

Metadata

Authors
Hillenmeyer, M. E., Ericson, E., Davis, R. W., Nislow, C., Koller, D., & Giaever, G.
Affiliations
  • Stanford University (US)
  • University of Toronto (CA)
Corresponding author
  • Koller, Daphne
  • Giaever, Guri
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2010-03-12
Volume
11
Issue
3
Pages
R30-R30
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/gb-2010-11-3-r30
PMC
PMC2864570
OpenAlex
W2121907028
Cited by
112
References
76
Citations by year
2026: 2 · 2025: 1 · 2024: 4 · 2023: 3 · 2022: 9 · 2021: 4 · 2020: 5 · 2019: 3 · 2018: 5 · 2017: 1 · 2016: 8 · 2015: 10 · 2014: 11 · 2013: 13 · 2012: 17
FWCI
5.87
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • TSpace (University of Toronto) (submittedVersion)
Funders
  • National Science Foundation (0345474)
  • University of Toronto
  • Canadian Institutes of Health Research (81340, (MO)P-81340, 84305)
  • National Human Genome Research Institute
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
gene fitness, drug inhibition, CO inhibition, Saccharomyces cerevisiae, gene function prediction, drug target prediction
MeSH terms
Copper Transport Proteins, Algorithms, Clozapine, Genes, Fungal, Membrane Proteins, Saccharomyces cerevisiae, Structure-Activity Relationship, Nocodazole, Drug Delivery Systems, Genome, Fungal, Gene Deletion, Molecular Chaperones, Genomics, Drug Resistance, Fungal, Cation Transport Proteins, Saccharomyces cerevisiae Proteins, Systems Biology, Genetic Fitness

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review