Title |
TOPS: a versatile software tool for statistical analysis and visualization of combinatorial gene-gene and gene-drug interaction screens
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Published in |
BMC Bioinformatics, April 2014
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DOI | 10.1186/1471-2105-15-98 |
Pubmed ID | |
Authors |
Markus K Muellner, Gerhard Duernberger, Florian Ganglberger, Claudia Kerzendorfer, Iris Z Uras, Andreas Schoenegger, Klaudia Bagienski, Jacques Colinge, Sebastian MB Nijman |
Abstract |
Measuring the impact of combinations of genetic or chemical perturbations on cellular fitness, sometimes referred to as synthetic lethal screening, is a powerful method for obtaining novel insights into gene function and drug action. Especially when performed at large scales, gene-gene or gene-drug interaction screens can reveal complex genetic interactions or drug mechanism of action or even identify novel therapeutics for the treatment of diseases.The result of such large-scale screen results can be represented as a matrix with a numeric score indicating the cellular fitness (e.g. viability or doubling time) for each double perturbation. In a typical screen, the majority of combinations do not impact the cellular fitness. Thus, it is critical to first discern true "hits" from noise. Subsequent data exploration and visualization methods can assist to extract meaningful biological information from the data. However, despite the increasing interest in combination perturbation screens, no user friendly open-source program exists that combines statistical analysis, data exploration tools and visualization. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Norway | 1 | 17% |
France | 1 | 17% |
Unknown | 4 | 67% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 50% |
Scientists | 2 | 33% |
Science communicators (journalists, bloggers, editors) | 1 | 17% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 2% |
United States | 1 | 2% |
Germany | 1 | 2% |
Brazil | 1 | 2% |
Unknown | 51 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 12 | 22% |
Researcher | 11 | 20% |
Student > Doctoral Student | 5 | 9% |
Student > Master | 3 | 5% |
Professor | 3 | 5% |
Other | 9 | 16% |
Unknown | 12 | 22% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 16 | 29% |
Biochemistry, Genetics and Molecular Biology | 9 | 16% |
Computer Science | 4 | 7% |
Medicine and Dentistry | 3 | 5% |
Arts and Humanities | 2 | 4% |
Other | 9 | 16% |
Unknown | 12 | 22% |