Analysing and meta-analysing time-series data of microbial growth and gene expression from plate readers.

ABSTRACT: Responding to change is a fundamental property of life, making time-series data invaluable in biology. For microbes, plate readers are a popular, convenient means to measure growth and also gene expression using fluorescent reporters. Nevertheless, the difficulties of analysing the resulting data can be a bottleneck, particularly when combining measurements from different wells and plates. Here we present omniplate, a Python module that corrects and normalises plate-reader data, estimates growth rates and fluorescence per cell as functions of time, calculates errors, exports in different formats, and enables meta-analysis of multiple plates. The software corrects for autofluorescence, the optical density's non-linear dependence on the number of cells, and the effects of the media. We use o

SUBMITTER: Montano-Gutierrez LF 

PROVIDER: S-EPMC9176753 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature