Table 2.
Summary of data analysis workflows and methods available for Omnilog kinetic data.
| Name | Computing availability | Pros | Cons | References |
|---|---|---|---|---|
| Kinetic and Parametric Analysis | GUI* | – | Limited graphical and analysis. Only allows pair-wise testing. No noise correction. No normalization. | [1] |
| Grofit | R programming | Data derived from growth curves is fitted to different parametric models provides a model free spline method and bootstrapping for estimation of confidence intervals. | No noise correction. No normalization. Limited amount of metadata can be included |
[27] |
| PheMaDB | GUI*. Implementation for GNU/Linux and MacOS systems | Web-based relational database, which enables storage, retrieval and limited analysis of the Omnilog PM data Possibility of setting a threshold for noise. | Compares curves through graphical analysis as Biolog proprietary software. No normalization Limited noise correction |
[5] |
| opm | R programming | Customized input and plot functions. Possibility to add additional metadata |
Grofit wrapper. No noise correction. No normalization. | [54] |
| DuctApe | Implementation for GNU/Linux Systems | Noise correction through blank subtraction. Fits the curves to different parametric models (Richars, Logistic, Gompertz). Categorization of metabolic curves through AV index. Integration of genomic and phenomic data allowing metabolic network reconstruction as well as pan- and accessory genome calculation. |
Signal refinement may cause loss of information | [18] |
| R-Biolog | R programming and BUGS | 3 novel methods: Grouping of active/non-active profiles through a custom EM algorithm. Normalization and stabilization separately for active and non-active profiles: based on logistic and linear model. Effect identification of different experimental setups and their interactions over time through a Bayesian approach. |
Active profiles are fitted to logistic model only. | [56] |
| mcmc-pma | Implementation for GNU/Linux Systems | Bayesian approach using adaptive Markov Chain Monte Carlo (MCMC) algorithm to sample from the posterior distributions of the parameters from fitted data using Baranyi and custom Diauxic model. | No normalization | [21] |
| Biolog Decomposition | R programming | Novel algorithm to identify different metabolic cycles based on statistical decomposition of the time-series measurements into a set of growth models. | – | [46] |
| Micro4Food PM | R programming | Coupling of grouping and normalization/stabilization methods proposed by Vehkala et al. [56] and grofit free splines parameter estimation. Removal of common non-active profiles in switching mode | – | This review |
*
GUI: graphical user interface.