Dataset Information

Systematic Gene-to-Phenotype Arrays: A High-Throughput Technique for Molecular Phenotyping.

ABSTRACT: We have developed a highly parallel strategy, systematic gene-to-phenotype arrays (SGPAs), to comprehensively map the genetic landscape driving molecular phenotypes of interest. By this approach, a complete yeast genetic mutant array is crossed with fluorescent reporters and imaged on membranes at high density and contrast. Importantly, SGPA enables quantification of phenotypes that are not readily detectable in ordinary genetic analysis of cell fitness. We benchmark SGPA by examining two fundamental biological phenotypes: first, we explore glucose repression, in which SGPA identifies a requirement for the Mediator complex and a role for the CDK8/kinase module in regulating transcription. Second, we examine selective protein quality control, in which SGPA identifies most known quality control factors along with U34 tRNA modification, which acts independently of proteasomal degradation to limit misfolded protein production. Integration of SGPA with other fluorescent readouts will enable genetic dissection of a wide range of biological pathways and conditions.

SUBMITTER: Jaeger PA

PROVIDER: S-EPMC5777277 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Systematic Gene-to-Phenotype Arrays: A High-Throughput Technique for Molecular Phenotyping.

Jaeger Philipp A PA Ornelas Lilia L McElfresh Cameron C Wong Lily R LR Hampton Randolph Y RY Ideker Trey T

Molecular cell 20180101 2

We have developed a highly parallel strategy, systematic gene-to-phenotype arrays (SGPAs), to comprehensively map the genetic landscape driving molecular phenotypes of interest. By this approach, a complete yeast genetic mutant array is crossed with fluorescent reporters and imaged on membranes at high density and contrast. Importantly, SGPA enables quantification of phenotypes that are not readily detectable in ordinary genetic analysis of cell fitness. We benchmark SGPA by examining two fundam ...[more]

PMID: 29351850

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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

Systematic Gene-to-Phenotype Arrays: A High-Throughput Technique for Molecular Phenotyping.

Publications

Systematic Gene-to-Phenotype Arrays: A High-Throughput Technique for Molecular Phenotyping.

Similar Datasets

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