Abstract
Data produced by Biolog Phenotype MicroArrays are longitudinal measurements of cells’ respiration on distinct substrates. We introduce a three-step pipeline to analyze phenotypic microarray data with novel procedures for grouping, normalization and effect identification. Grouping and normalization are standard problems in the analysis of phenotype microarrays defined as categorizing bacterial responses into active and non-active, and removing systematic errors from the experimental data, respectively. We expand existing solutions by introducing an important assumption that active and non-active bacteria manifest completely different metabolism and thus should be treated separately. Effect identification, in turn, provides new insights into detecting differing respiration patterns between experimental conditions, e.g. between different combinations of strains and temperatures, as not only the main effects but also their interactions can be evaluated. In the effect identification, the multilevel data are effectively processed by a hierarchical model in the Bayesian framework. The pipeline is tested on a data set of 12 phenotypic plates with bacterium Yersinia enterocolitica. Our pipeline is implemented in R language on the top of opm R package and is freely available for research purposes.
Highlights
Recent advances in measurement technology have produced an explosion of information on the genetic and phenotypic characteristics of bacteria
A logistic model appears appropriate for characterizing the metabolic profiles of Phenotype MicroArrays (PMs) data and in this article we demonstrate, how the logistic model can be utilized during the multi step analysis of PM data to reach the above-mentioned desiderata
Seven bacterial strains of the bacterium Yersinia enterocolitica were cultured on PM1, 2A, 3B and 4A and incubated for 48h at 28°C in the OmniLog Incubator/ Reader
Summary
Recent advances in measurement technology have produced an explosion of information on the genetic and phenotypic characteristics of bacteria. Inexpensive whole-genome sequencing has just begun to shape our detailed understanding of intercontinental transmission patterns and how rapidly bacterial populations can respond to environmental pressures resulting from vaccines and antibiotics. Biolog Phenotype MicroArrays (PMs) are commercially available microplates utilized in genomic research [1,2,3]. They are widely used in the characterization of metabolic activity. Metabolic alterations between diseased and healthy cell lines can be revealed using this technology [4]. One way to utilize PMs is to establish a link between a PLOS ONE | DOI:10.1371/journal.pone.0118392. One way to utilize PMs is to establish a link between a PLOS ONE | DOI:10.1371/journal.pone.0118392 March 18, 2015
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
