OpenStats

OpenStats performs reproducible statistical analysis of high-throughput phenotypic data to support discovery of genotype–phenotype associations.

Key Features:

• Reproducibility and Reliability: Provides a consistent framework for reproducible analysis of high-throughput phenotypic data to support robust genotype–phenotype inference.
• Scalability and Extensibility: Scales to large datasets and supports an extensible architecture suitable for large-scale phenotyping projects such as the International Mouse Phenotyping Consortium (IMPC).
• Performance Improvements: Demonstrates up to a 13-fold improvement in processing time compared with existing solutions.
• FAIR Data Principles: Reduces complexity and enhances transparency to align analyses with FAIR (Findable, Accessible, Interoperable, Reusable) principles.
• Statistical Scope: Handles diverse statistical scenarios inherent in large-scale phenotyping analyses, including genotype–phenotype association studies.

Scientific Applications:

• High-throughput phenotyping screens: Analyzes large-scale phenotyping screens to extract statistical signals from high-throughput phenotype datasets.
• Genotype–phenotype association analysis: Supports detection and characterization of genotype–phenotype associations.
• IMPC-scale dataset analysis: Applies to IMPC-scale datasets for systematic, large-cohort phenotyping analyses.

Methodology:

Implements reproducible statistical analysis workflows for high-throughput phenotypic data within a scalable, extensible computational framework.