ArticleBMC bioinformatics2011
Extracting quantitative genetic interaction phenotypes from matrix combinatorial RNAi.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Scalable imaging-based profiling of CRISPR perturbations with protein barcodes.bioRxiv : the preprint server for biology · 2025Article
- Systematic epistatic mapping of cellular processes.Cell division · 2017Article
- The Deubiquitinase USP47 Stabilizes MAPK by Counteracting the Function of the N-end Rule ligase POE/UBR4 in Drosophila.PLoS biology · 2016Article
- A genetic interaction map of cell cycle regulators.Molecular biology of the cell · 2016Article
- Measuring genetic interactions in human cells by RNAi and imaging.Nature protocols · 2014Article
- TOPS: a versatile software tool for statistical analysis and visualization of combinatorial gene-gene and gene-drug interaction screens.BMC bioinformatics · 2014Article
- Functional analysis of the Drosophila embryonic germ cell transcriptome by RNA interference.PloS one · 2014Article
- Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping.Nature methods · 2013Article
- Systems genetics in "-omics" era: current and future development.Theory in biosciences = Theorie in den Biowissenschaften · 2013Review
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6 authors.
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Abstract
backgroundSystematic measurement of genetic interactions by combinatorial RNAi (co-RNAi) is a powerful tool for mapping functional modules and discovering components. It also provides insights into the role of epistasis on the way from genotype to phenotype. The interpretation of co-RNAi data requires computational and statistical analysis in order to detect interactions reliably and sensitively.
resultsWe present a comprehensive approach to the analysis of univariate phenotype measurements, such as cell growth. The method is based on a quantitative model and is demonstrated on two example Drosophila cell culture data sets. We discuss adjustments for technical variability, data quality assessment, model parameter fitting and fit diagnostics, choice of scale, and assessment of statistical significance.
conclusionsAs a result, we obtain quantitative genetic interactions and interaction networks reflecting known biological relationships between target genes. The reliable extraction of presence, absence, and strength of interactions provides insights into molecular mechanisms.
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