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Systematic epistatic mapping of cellular processes

Overview of attention for article published in Cell Division, January 2017
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Title
Systematic epistatic mapping of cellular processes
Published in
Cell Division, January 2017
DOI 10.1186/s13008-016-0028-z
Pubmed ID
Authors

Maximilian Billmann, Michael Boutros

Abstract

Genetic screens have identified many novel components of various biological processes, such as components required for cell cycle and cell division. While forward genetic screens typically generate unstructured 'hit' lists, genetic interaction mapping approaches can identify functional relations in a systematic fashion. Here, we discuss a recent study by our group demonstrating a two-step approach to first screen for regulators of the mitotic cell cycle, and subsequently guide hypothesis generation by using genetic interaction analysis. The screen used a high-content microscopy assay and automated image analysis to capture defects during mitotic progression and cytokinesis. Genetic interaction networks derived from process-specific features generate a snapshot of functional gene relations in those processes, which follow a temporal order during the cell cycle. This complements a recently published approach, which inferred directional genetic interactions reconstructing hierarchical relationships between genes across different phases during mitotic progression. In conclusion, this strategy leverages unbiased, genome-wide, yet highly sensitive and process-focused functional screening in cells.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 7%
Unknown 13 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 29%
Researcher 4 29%
Student > Bachelor 2 14%
Professor 1 7%
Student > Master 1 7%
Other 1 7%
Unknown 1 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 36%
Agricultural and Biological Sciences 5 36%
Computer Science 1 7%
Neuroscience 1 7%
Engineering 1 7%
Other 0 0%
Unknown 1 7%