Network-Free Inference of Knockout Effects in Yeast
Open accessAbstract
Perturbation experiments, in which a certain gene is knocked out and the expression levels of other genes are observed, constitute a fundamental step in uncovering the intricate wiring diagrams in the living cell and elucidating the causal roles of genes in signaling and regulation. Here we present a novel framework for analyzing large cohorts of gene knockout experiments and their genome-wide effects on expression levels. We devise clustering-like algorithms that identify groups of genes that behave similarly with respect to the knockout data, and utilize them to predict knockout effects and to annotate physical interactions between proteins as inhibiting or activating. Differing from previous approaches, our prediction approach does not depend on physical network information; the latter is used only for the annotation task. Consequently, it is both more efficient and of wider applicability than previous methods. We evaluate our approach using a large scale collection of gene knockout experiments in yeast, comparing it to the state-of-the-art SPINE algorithm. In cross validation tests, our algorithm exhibits superior prediction accuracy, while at the same time increasing the coverage by over 25-fold. Significant coverage gains are obtained also in the annotation of the physical network.
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The two-module organization is statistically significant (empirical p<10^-3).
“This organization, which is statistically significant (empirical p-value<10-3; Methods, Supplementary Fig. 10), is reminiscent to that observed previously in genetic circuits in yeast24,25.”
From Dynamic regulatory network controlling TH17 cell differentiation (2013) · cites this paper as [25]
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- Authors
- Peleg, T., Yosef, N., Ruppin, E., & Sharan, R.
- Affiliations
- Tel Aviv University (IL)
- Corresponding author
- Sharan, Roded
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-01-08
- Volume
- 6
- Issue
- 1
- Pages
- e1000635
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC2795781
- OpenAlex
- W2038236366
- Cited by
- 14
- References
- 31
- Citations by year
- 2025: 1 · 2019: 1 · 2018: 1 · 2016: 1 · 2015: 1 · 2014: 1 · 2013: 2 · 2012: 2
- FWCI
- 1.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Israel Science Foundation (385/06)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Keywords
- gene expression perturbation, yeast, gene clustering, genome-wide expression profiling, gene regulatory networks
- MeSH terms
- Algorithms, Genes, Fungal, Yeasts, Reproducibility of Results, Cluster Analysis, Linear Models, Systems Biology, Gene Regulatory Networks, Gene Knockout Techniques
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