Dataset Information

A tool named Iris for versatile high-throughput phenotyping in microorganisms.

ABSTRACT: Advances in our ability to systematically introduce and track controlled genetic variance in microorganisms have, in the past decade, fuelled high-throughput reverse genetics approaches. When coupled to quantitative readouts, such approaches are extremely powerful at elucidating gene function and providing insights into the underlying pathways and the overall cellular network organization. Yet, until now, all efforts to quantify microbial macroscopic phenotypes have been restricted to monitoring growth in a small number of model microorganisms. We have developed an image analysis software named Iris, which allows for systematic exploration of a number of orthogonal-to-growth processes, including biofilm formation, colony morphogenesis, envelope biogenesis, sporulation and reporter activity. In addition, Iris provides more sensitive growth measurements than currently available software and is compatible with a variety of different microorganisms, as well as with endpoint or kinetic data. We used Iris to reanalyse existing chemical genomics data in Escherichia coli and to perform proof-of-principle screens on colony biofilm formation and morphogenesis of different bacterial species and the pathogenic fungus Candida albicans. We thereby recapitulated existing knowledge but also identified a plethora of additional genes and pathways involved in both processes.

SUBMITTER: Kritikos G

PROVIDER: S-EPMC5464397 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A tool named Iris for versatile high-throughput phenotyping in microorganisms.

Kritikos George G Banzhaf Manuel M Herrera-Dominguez Lucia L Koumoutsi Alexandra A Wartel Morgane M Zietek Matylda M Typas Athanasios A

Nature microbiology 20170217

Advances in our ability to systematically introduce and track controlled genetic variance in microorganisms have, in the past decade, fuelled high-throughput reverse genetics approaches. When coupled to quantitative readouts, such approaches are extremely powerful at elucidating gene function and providing insights into the underlying pathways and the overall cellular network organization. Yet, until now, all efforts to quantify microbial macroscopic phenotypes have been restricted to monitoring ...[more]

PMID: 28211844

Similar Datasets

Project description:Microbial arrays, with a large number of different strains on a single plate printed with robotic precision, underpin an increasing number of genetic and genomic approaches. These include Synthetic Genetic Array analysis, high-throughput Quantitative Trait Loci (QTL) analysis and 2-hybrid techniques. Measuring the growth of individual colonies within these arrays is an essential part of many of these techniques but is useful for any work with arrays. Measurement is typically done using intermittent imagery fed into complex image analysis software, which is not especially accurate and is challenging to use effectively. We have developed a simple and fast alternative technique that uses a pinning robot and a commonplace microplate reader to continuously measure the thickness of colonies growing on solid agar, complemented by a technique for normalizing the amount of cells initially printed to each spot of the array in the first place. We have developed software to automate the process of combining multiple sets of readings, subtracting agar absorbance, and visualizing colony thickness changes in a number of informative ways.The "PHENOS" pipeline (PHENotyping On Solid media), optimized for Saccharomyces yeasts, produces highly reproducible growth curves and is particularly sensitive to low-level growth. We have empirically determined a formula to estimate colony cell count from an absorbance measurement, and shown this to be comparable with estimates from measurements in liquid. We have also validated the technique by reproducing the results of an earlier QTL study done with conventional liquid phenotyping, and found PHENOS to be considerably more sensitive."PHENOS" is a cost effective and reliable high-throughput technique for quantifying growth of yeast arrays, and is likely to be equally very useful for a range of other types of microbial arrays. A detailed guide to the pipeline and software is provided with the installation files at https://github.com/gact/phenos .

Project description:ObjectiveHigh-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs.Materials and methodsWe develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms.ResultsPhenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm's classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations.DiscussionThe proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease.ConclusionSequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology.

Dataset Information

A tool named Iris for versatile high-throughput phenotyping in microorganisms.

Publications

A tool named Iris for versatile high-throughput phenotyping in microorganisms.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets