Biolog Phenotype MicroArray
     Serious Technology for Serious Microbiology April 2012
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  • Biolog/DSMZ Press Release:

    Researchers at DSMZ develop innovative software for Biolog phenotypic analyses to improve our understanding of the metabolic processes of microorganisms and other cells





    Phenotype MicroArray
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    This Newsletter Issue Summarizes Recent Developments in Computational and Statistical Analysis of Biolog PM Data:
    Scientists at the DSMZ have developed computational methods for the statistical analysis and display of Biolog Phenotype MicroArray data using The R Project, a free software environment for statistical computing. This newsletter highlights their recent publication in the journal PLoS ONE, where they present parameter estimation strategies for several parameters of PM kinetic curves, and data displays using 95% confidence intervals.
    phenotype microarray
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    Visualization and Curve-Parameter Estimation Strategies for Detailed Exploration of Phenotype MicroArray Kinetics
    The DSMZ authors were motivated to extract as much information as possible from the high throughput phenotyping provided by Biolog Phenotype MicroArrays. They hypothesized that multiple kinetic parameter estimation is necessary to capture the diversity of the biological response recorded over time. Therefore they adapted R functions and existing statistical methods for modeling kinetic curves into a package that is customized to work within the biological context of Biolog PM kinetic redox dye reduction curves.

    Data was created using Biolog's phenotypic test plates and the OmniLog® PM System. Using the R functions levelplot and xyplot, they implemented methods to display kinetic curves as thin lines to improve the display of multiple curves. Using R's add-on package grofit, they compared model fitting and free spline fitting approaches to parameter estimation. The DSMZ authors used technical replicate experimental design to evaluate the utility of parameter estimation and confidence interval display. They also used archetypal curve analysis and showed that two to four archetypes were necessary to represent the range of positive test reactions. These new methods of dimensional and noise reduction improve the ability to detect statistically significant differences in kinetic curves that are within the defined limits of biological reality. The authors suggest that these methods and tools may be used for automated post processing of Biolog PM Data.

    Link to PDF file of this publication

    Link to the fact sheet and first release of OPM R Package with tools for analyzing OmniLog® Phenotype MicroArray (PM) data including:

  • plotting
  • aggregating (estimating curve parameters)
  • comparing and discretizing PM data
  • creating phylogenetic formats and reports for taxonomic journals
  • integrating metadata, using the YAML format for the storage of data and metadata
  • batch conversion of large numbers of files
  • customized data frames for application of other R packages
  • A user-friendly manual is available from Dr. Markus Göker (markus.goeker@dsmz.de) or Dr. Johannes Sikorski (johannes.sikorski@dsmz.de) upon request.

     
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    Phenotype MicroArray Technology
    Biolog's Phentoype MicroArray technology enables researchers to evaluate nearly 2000 phenotypes of a microbial cell in a single experiment. This integrated system of cellular assays, instrumentation and bioinformatics software provides cellular knowledge that complements molecular information, helping you interpret and find the relevant aspects in massive amounts of gene expression or proteomics data. Through comprehensive and precise quantitation of phenotypes, researchers are able to obtain an unbiased perspective of the effect on cells of genetic differences, environmental change, exposure to chemicals or drugs, and more.
     
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    Applications for Phenotype MicroArrays
    Phenotype MicroArrays add a fresh cell-based perspective to a wide range of activities.

  • Discovery research
  • Cell characterization
  • Mutant and mutation characterization
  • Determination of gene function
  • Transporter substrate assignment
  • Chemical phenomics
  • Strain discrimination
  • Genome annotation
  • Metabolic modeling
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