CarboLogR: a Shiny/R application for statistical analysis of bacterial utilisation of carbon sources

Abstract

The Biolog Phenotype Microarray (PM) and Anaerobic MicroPlates (AN) 96-well plates utilise colorimetric redox reactions to rapidly screen bacteria for the ability to utilise different carbon sources and other metabolites. Measurement of substrate utilisation as bacterial growth curves typically involves extended data normalization, outlier detection, and statistical analysis. TheCarboLogR package streamlines this process with a Shiny application, guiding users from raw data generated from Biolog assays to growth profile comparison. We applied chemoinformatics approaches to define clusters of carbon sources, based on molecular similarities, increasing statistical power. Altogether, CarboLogR is a novel integrated tool providing automatic and high-level resolution for bacterial growth patterns and carbon source usage.Availability and Implementation: CarboLogR application can be downloaded and installed from Github repository https://github.com/kevinVervier/CarboLogR. Tutorial, data, and examples can be downloaded at https://github.com/kevinVervier/CarboLogR/vignettes.

Host adaptation in gut Firmicutes is associated with sporulation loss and altered colonisation patterns

“…For each isolate, 3-5 replicates run on different days from different starting colonies were used. Data was analysed using the CarboLogR application 72 .…”

AMiGA: Software for Automated Analysis of Microbial Growth Assays

“…Growth data for C. difficile analyzed in this article are available online; see https://github.com/firasmidani/amiga . Growth data for C. sedlakii ( 2 ), Pseudomonas aeruginosa ( 28 ), and Yersinia enterocolitica ( 7 ) were previously published and are available in public repositories listed in their corresponding articles. See https://github.com/dacuevas/PMAnalyzer for C. sedlakii data, https://github.com/lauradunphy/dunphy_yen_papin_supplement for P. aeruginosa data, and https://github.com/kevinVervier/CarboLogR for Y. enterocolitica data.…”

“…Today, automated platforms equipped with multiwell plate readers can rapidly generate large sets of microbial growth data. Several computational tools have been developed for the rapid analysis and interpretation of these growth data sets ( 2 – 7 ). However, the growth of clinical isolates, fastidious organisms, or microbes under various stressors often generates curves that do not follow standard logistic or sigmoidal shape.…”

AMiGA: software for automated Analysis of Microbial Growth Assays

“…Several computational tools have been adopted for the rapid analysis and interpretation of these growth data sets (2)(3)(4)(5)(6)(7). However, the growths of clinical isolates, fastidious organisms, or microbes under various stressors often generate curves that do not follow standard logistic or sigmoidal shape.…”

“…Today, there are many automated platforms equipped with multi-well plate readers that can rapidly generate large sets of microbial growth data. Several computational tools have been adopted for the rapid analysis and interpretation of these growth data sets (2–7). However, the growths of clinical isolates, fastidious organisms, or microbes under various stressors often generate curves that do not follow standard logistic or sigmoidal shape.…”