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Selection of computational environments for PSP processing on scientific gateways.

ABSTRACT: Science Gateways have been widely accepted as an important tool in academic research, due to their flexibility, simple use and extension. However, such systems may yield performance traps that delay work progress and cause waste of resources or generation of poor scientific results. This paper addresses an investigation on some of the failures in a Galaxy system and analyses of their impacts. The use case is based on protein structure prediction experiments performed. A novel science gateway component is proposed towards the definition of the relation between general parameters and capacity of machines. The machine-learning strategies used appoint the best machine setup in a heterogeneous environment and the results show a complete overview of Galaxy, a diverse platform organization, and the workload behavior. A Support Vector Regression (SVR) model generated and based on a historic data-set provided an excellent learning module and proved a varied platform configuration is valuable as infrastructure in a science gateway. The results revealed the advantages of investing in local cluster infrastructures as a base for scientific experiments.

SUBMITTER: Martins de Oliveira E

PROVIDER: S-EPMC6068289 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Selection of computational environments for PSP processing on scientific gateways.

Martins de Oliveira Edvard E Estrella Júlio Cézar JC Delbem Alexandre Cláudio Botazzo ACB Nunes Luiz Henrique LH Shishido Henrique Yoshikazu HY Reiff-Marganiec Stephan S

Heliyon 20180717 7

Science Gateways have been widely accepted as an important tool in academic research, due to their flexibility, simple use and extension. However, such systems may yield performance traps that delay work progress and cause waste of resources or generation of poor scientific results. This paper addresses an investigation on some of the failures in a Galaxy system and analyses of their impacts. The use case is based on protein structure prediction experiments performed. A novel science gateway com ...[more]

PMID: 30073212

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Project description:BackgroundA thorough verification of the ability of genomic selection (GS) to predict estimated breeding values for pea (Pisum sativum L.) grain yield is pending. Prediction for different environments (inter-environment prediction) has key importance when breeding for target environments featuring high genotype × environment interaction (GEI). The interest of GS would increase if it could display acceptable prediction accuracies in different environments also for germplasm that was not used in model training (inter-population prediction).ResultsSome 306 genotypes belonging to three connected RIL populations derived from paired crosses between elite cultivars were genotyped through genotyping-by-sequencing and phenotyped for grain yield, onset of flowering, lodging susceptibility, seed weight and winter plant survival in three autumn-sown environments of northern or central Italy. The large GEI for grain yield and its pattern (implying larger variation across years than sites mainly due to year-to-year variability for low winter temperatures) encouraged the breeding for wide adaptation. Wider within-population than between-population variation was observed for nearly all traits, supporting GS application to many lines of relatively few elite RIL populations. Bayesian Lasso without structure imputation and 1% maximum genotype missing rate (including 6058 polymorphic SNP markers) was selected for GS modelling after assessing different GS models and data configurations. On average, inter-environment predictive ability using intra-population predictions reached 0.30 for yield, 0.65 for onset of flowering, 0.64 for seed weight, and 0.28 for lodging susceptibility. Using inter-population instead of intra-population predictions reduced the inter-environment predictive ability to 0.19 for grain yield, 0.40 for onset of flowering, 0.28 for seed weight, and 0.22 for lodging susceptibility. A comparison of GS vs phenotypic selection (PS) based on predicted genetic gains per unit time for same selection costs suggested greater efficiency of GS for all traits under various selection scenarios. For yield, the advantage in predicted efficiency of GS over PS was at least 80% using intra-population predictions and 20% using inter-population predictions. A genome-wide association study confirmed the highly polygenic control of most traits.ConclusionsGenome-enabled predictions can increase the efficiency of pea line selection for wide adaptation to Italian environments relative to phenotypic selection.

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Selection of computational environments for PSP processing on scientific gateways.

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Selection of computational environments for PSP processing on scientific gateways.

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