omniplate

omniplate analyzes time-series plate-reader data to correct for measurement artifacts and quantify microbial growth rates and fluorescence-based gene expression.

Key Features:

• Data correction and normalization: Corrects plate-reader data for autofluorescence, the non-linear relationship between optical density and cell count, and media effects, and normalizes datasets.
• Growth rate estimation: Estimates microbial growth rates as a function of time from time-series optical density data.
• Fluorescence per cell analysis: Calculates fluorescence per cell over time to quantify gene expression from fluorescent reporters.
• Error estimation and data export: Computes measurement errors and supports exporting processed data in multiple formats.
• Meta-analysis across plates: Combines and compares datasets from multiple plates to enable cross-plate meta-analysis.

Scientific Applications:

• Monod relationship analysis: Used to explore the Monod relationship in microbial growth.
• Carbon-source growth control studies: Applied to demonstrate that raffinose can serve as an effective carbon source to control yeast growth rates.
• Glucose transport and HXT regulation: Employed to analyze fluorescent tagging data in budding yeast to study glucose transport mechanisms and regulation of hexose transporter (HXT) genes, including bipartite regulation by high- and low-affinity glucose sensors.

Methodology:

Applies algorithms to correct raw plate-reader data for autofluorescence, OD–cell count nonlinearity and media effects, normalizes datasets, estimates time-resolved growth rates, computes per-cell fluorescence and measurement errors, and performs multi-plate meta-analysis.