PROPHECY--a database for high-resolution phenomics

Fernandez-Ricaud, Luciano; Warringer, Jonas; Ericson, Elke; Pylvänäinen, Ilona; Kemp, Graham; Nerman, Olle; Blomberg, Anders

doi:10.1093/nar/gki126

Cited by 34 publications

(37 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A number of alternative, 'high-end' approaches are becoming available that will extend the classical approaches described in this review. These include gene expression microarrays, high-throughput sequencing, meta-analyses of genetic studies [54], whole genome association studies [46 ] and genome-wide characterization of phenotypes caused by gene deletions (phenomics) [55]. Study quality will always be critical.…”

Section: Discussionmentioning

confidence: 99%

Gene–environmental interaction in asthma

Yang

Savarimuthu²,

Kim³

et al. 2007

Current Opinion in Allergy &Amp; Clinical Immunology

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Gene–environmental interaction in asthma

Yang

Savarimuthu²,

Kim³

et al. 2007

Current Opinion in Allergy &Amp; Clinical Immunology

View full text Add to dashboard Cite

show abstract

“…S. paradoxus harbors higher levels of ROS (Deregowska et al 2015), and our results suggest that this may lead to a higher susceptibility to oxidative stress. Many other large-scale population growth studies have been performed to differentiate biological function through population growth phenotypes in the yeast community, and we anticipate that future applications of B-GREAT will highlight additional results from these studies (Warringer et al 2003;Fernandez-Ricaud et al 2005).…”

Section: Cold Spring Harbor Laboratory Press On May 11 2018 -Publishmentioning

confidence: 99%

Detecting differential growth of microbial populations with Gaussian process regression

et al. 2016

View full text Add to dashboard Cite

Microbial growth curves are used to study differential effects of media, genetics, and stress on microbial population growth. Consequently, many modeling frameworks exist to capture microbial population growth measurements. However, current models are designed to quantify growth under conditions for which growth has a specific functional form. Extensions to these models are required to quantify the effects of perturbations, which often exhibit nonstandard growth curves. Rather than assume specific functional forms for experimental perturbations, we developed a general and robust model of microbial population growth curves using Gaussian process (GP) regression. GP regression modeling of high-resolution time-series growth data enables accurate quantification of population growth and allows explicit control of effects from other covariates such as genetic background. This framework substantially outperforms commonly used microbial population growth models, particularly when modeling growth data from environmentally stressed populations. We apply the GP growth model and develop statistical tests to quantify the differential effects of environmental perturbations on microbial growth across a large compendium of genotypes in archaea and yeast. This method accurately identifies known transcriptional regulators and implicates novel regulators of growth under standard and stress conditions in the model archaeal organism Halobacterium salinarum. For yeast, our method correctly identifies known phenotypes for a diversity of genetic backgrounds under cyclohexamide stress and also detects previously unidentified oxidative stress sensitivity across a subset of strains. Together, these results demonstrate that the GP models are interpretable, recapitulating biological knowledge of growth response while providing new insights into the relevant parameters affecting microbial population growth.

show abstract

“…We believe that this system could be successfully applied for a broad range of filamentous fungi, yeasts, and bacteria for large-scale screening based on quantitative changes in growth phenotypes under a wide variety of growth conditions. Thus, the method described herein paves the way for the development of a high-resolution quantitative phenomic approach for filamentous fungi, as is already available for yeast (18). …”

Section: Resultsmentioning

confidence: 99%

Laser nephelometry applied in an automated microplate system to study filamentous fungus growth

Joubert¹,

Calmes²,

Berruyer³

et al. 2010

BioTechniques

View full text Add to dashboard Cite

By contrast with photometry (i.e., the measurement of light transmitted through a particle suspension), nephelometry is a direct method of measuring light scattered by particles in suspension. Since the scattered light intensity is directly proportional to the suspended particle concentration, nephelometry is a promising method for recording microbial growth and especially for studying filamentous fungi, which cannot be efficiently investigated through spectrophotometric assays. We describe herein for the first time a filamentous fungi-tailored procedure based on microscale liquid cultivation and automated nephelometric recording of growth, followed by extraction of relevant variables (lag time and growth rate) from the obtained growth curves. This microplate reader technique is applicable for the evaluation of antifungal activity and for large-scale phenotypic profiling.

show abstract

PROPHECY--a database for high-resolution phenomics

Cited by 34 publications

References 8 publications

Gene–environmental interaction in asthma

Gene–environmental interaction in asthma

Detecting differential growth of microbial populations with Gaussian process regression

Laser nephelometry applied in an automated microplate system to study filamentous fungus growth

Contact Info

Product

Resources

About