scholarly journals The contribution of dominance to phenotype prediction in a pine breeding and simulated population

Heredity ◽  
2016 ◽  
Vol 117 (1) ◽  
pp. 33-41 ◽  
Author(s):  
J E de Almeida Filho ◽  
J F R Guimarães ◽  
F F e Silva ◽  
M D V de Resende ◽  
P Muñoz ◽  
...  
Keyword(s):  
Simulated Population ◽  
Phenotype Prediction

scholarly journals Automated Cytogenetic Biodosimetry at Population-Scale

Radiation ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 79-94
Author(s):  
Peter K. Rogan ◽  
Eliseos J. Mucaki ◽  
Ben C. Shirley ◽  
Yanxin Li ◽  
Ruth C. Wilkins ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Dicentric Chromosome ◽  
Automated Assignment ◽  
Simulated Population ◽  
Dose Estimation ◽  
Processing Times ◽  
Multiple Processors ◽  
Exposure Levels ◽  
Population Scale

The dicentric chromosome (DC) assay accurately quantifies exposure to radiation; however, manual and semi-automated assignment of DCs has limited its use for a potential large-scale radiation incident. The Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates unattended DC detection and determines radiation exposures, fulfilling IAEA criteria for triage biodosimetry. This study evaluates the throughput of high-performance ADCI (ADCI-HT) to stratify exposures of populations in 15 simulated population scale radiation exposures. ADCI-HT streamlines dose estimation using a supercomputer by optimal hierarchical scheduling of DC detection for varying numbers of samples and metaphase cell images in parallel on multiple processors. We evaluated processing times and accuracy of estimated exposures across census-defined populations. Image processing of 1744 samples on 16,384 CPUs required 1 h 11 min 23 s and radiation dose estimation based on DC frequencies required 32 sec. Processing of 40,000 samples at 10 exposures from five laboratories required 25 h and met IAEA criteria (dose estimates were within 0.5 Gy; median = 0.07). Geostatistically interpolated radiation exposure contours of simulated nuclear incidents were defined by samples exposed to clinically relevant exposure levels (1 and 2 Gy). Analysis of all exposed individuals with ADCI-HT required 0.6–7.4 days, depending on the population density of the simulation.

scholarly journals Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jingru Zhou ◽  
Yingping Zhuang ◽  
Jianye Xia
Keyword(s):  
Aspergillus Niger ◽  
Large Scale ◽  
Measurement Techniques ◽  
Metabolic Model ◽  
System Level ◽  
Metabolic Phenotype ◽  
Omics Data ◽  
Prediction Ability ◽  
Phenotype Prediction ◽  
Genome Scale

Abstract Background Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. Results Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale $$k_{{cat}}$$ k cat values, predicting the differential expression of enzymes under different growth conditions. Conclusions This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

scholarly journals Omics AnalySIs System for PRecision Oncology (OASISPRO): a web-based omics analysis tool for clinical phenotype prediction

2017 ◽  
Vol 34 (2) ◽  
pp. 319-320 ◽  
Author(s):  
Kun-Hsing Yu ◽  
Michael R Fitzpatrick ◽  
Luke Pappas ◽  
Warren Chan ◽  
Jessica Kung ◽  
...  
Keyword(s):  
Clinical Phenotype ◽  
Analysis Tool ◽  
Precision Oncology ◽  
Phenotype Prediction ◽  
Web Based ◽  
Omics Analysis ◽  
Analysis System

Modeling Habitat Suitability and Management Options for Maintaining Round Whitefish (Prosopium cylindraceum) in Adirondack Ponds

2021 ◽  
Author(s):  
Amy Kathleen Conley ◽  
Matthew D. Schlesinger ◽  
James G. Daley ◽  
Lisa K. Holst ◽  
Timothy G. Howard
Keyword(s):  
Species Distribution Model ◽  
Management Strategies ◽  
Distribution Model ◽  
Nonnative Species ◽  
Simulated Population ◽  
Management Options ◽  
The Past ◽  
Almost All ◽  
Reintroduction Success ◽  
Maximum Air Temperature

Habitat loss, acid precipitation, and nonnative species have drastically reduced the number of Adirondack waterbodies occupied by round whitefish (Prosopium cylindraceum). The goal of this study was to 1) increase the probability of reintroduction success by modeling the suitability of ponds for reintroduction and 2) better understand the effects of different rates of pond reclamation. We created a species distribution model that identified 70 waterbodies that were physically similar to occupied ponds. The most influential variables for describing round whitefish habitat included trophic, temperature, and alkalinity classes; waterbody maximum depth; maximum air temperature; and surrounding soil texture and impervious surface. Next, we simulated population dynamics under a variety of treatment scenarios and compared the probability of complete extirpation using a modified Markov model. Under almost all management strategies, and under pressure from nonnative competitors like that observed in the past 30 years, the number of occupied ponds will decline over the next 100 years. However, restoring one pond every 3 years would result in a 99% chance of round whitefish persistence after 100 years.

scholarly journals Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast

2017 ◽  
Vol 13 (5) ◽  
pp. e1005489 ◽  
Author(s):  
Zhuo Wang ◽  
Samuel A. Danziger ◽  
Benjamin D. Heavner ◽  
Shuyi Ma ◽  
Jennifer J. Smith ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Phenotype Prediction

scholarly journals Ak-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria

2018 ◽  
Author(s):  
Erki Aun ◽  
Age Brauer ◽  
Veljo Kisand ◽  
Tanel Tenson ◽  
Maido Remm
Keyword(s):  
Statistical Model ◽  
Programming Language ◽  
Prediction Models ◽  
Genomic Sequence ◽  
Medical Diagnostics ◽  
Sequencing Data ◽  
Test Set ◽  
Phenotype Prediction ◽  
Python Programming Language ◽  
Python Programming

AbstractWe have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) generates ak-mer-based statistical model for predicting a given phenotype and (b) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167Klebsiella pneumoniaeisolates (virulence), 200Pseudomonas aeruginosaisolates (ciprofloxacin resistance) and 460Clostridium difficileisolates (azithromycin resistance). The phenotype prediction models trained from these datasets performed with 88% accuracy on theK. pneumoniaetest set, 88% on theP. aeruginosatest set and 96.5% on theC. difficiletest set. Prediction accuracy was the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets.PhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).SummaryPredicting phenotypic properties of bacterial isolates from their genomic sequences has numerous potential applications. A good example would be prediction of antimicrobial resistance and virulence phenotypes for use in medical diagnostics. We have developed a method that is able to predict phenotypes of interest from the genomic sequence of the isolate within seconds. The method uses statistical model that can be trained automatically on isolates with known phenotype. The method is implemented in Python programming language and can be run on low-end Linux server and/or on laptop computers.

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