coRNAi

coRNAi analyzes combinatorial cell-based RNA interference (RNAi) screens to measure and interpret genetic interactions for mapping functional modules and identifying components of biological processes.

Key Features:

• Quantitative model for co-RNAi: A quantitative model tailored to analyze data derived from combinatorial RNAi (co-RNAi) experiments and extract genetic interactions.
• Epistasis analysis: Detection and quantification of epistatic relationships between genes that influence phenotypic outcomes.
• Univariate phenotype support: Analysis based on univariate phenotype measurements such as cell growth.
• Technical variability adjustments: Adjustments for technical variability to ensure robustness in data interpretation.
• Data quality assessment: Rigorous data quality assessments to validate the reliability of input data.
• Model parameter fitting and diagnostics: Precise model parameter fitting with diagnostics to confirm the appropriateness of chosen models.
• Scale selection: Tools for selecting an appropriate scale for analysis.
• Statistical significance assessment: Assessment of statistical significance for inferred genetic interactions.
• Network construction: Extraction of quantitative genetic interactions and construction of interaction networks that reflect gene relationships.

Scientific Applications:

• Mapping functional modules: Mapping functional modules within biological systems using genetic interaction data.
• Identifying novel components: Identifying novel components involved in biological processes through combinatorial RNAi screens.
• Quantifying genotype–phenotype relationships: Quantifying how gene–gene interactions influence phenotypic outcomes measured by univariate assays such as cell growth.
• Drosophila cell culture analysis: Application to Drosophila cell culture data sets to extract quantitative genetic interactions and construct interaction networks.

Methodology:

Computational steps explicitly include a quantitative model for combinatorial RNAi data, adjustments for technical variability, rigorous data quality assessments, precise model parameter fitting with diagnostics, selection of an appropriate analysis scale, and assessment of statistical significance.