Koala: sistema para integração de métodos de predição e análise de estruturas de proteína

Defelicibus, Alexandre

doi:10.11606/d.82.2016.tde-22062016-102823

Cited by 2 publications

(6 citation statements)

References 35 publications

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“…In this study case, we use the Galaxy Koala, and chose a protein predictor with an NSGA‐II algorithm that calculates multiobjective PSP. The experiments design is described in Table .…”

Section: Methodsmentioning

confidence: 99%

“…In these experiments, there are two or four objectives, in the first case are GBSA Solvation and Van der Waals, and for the second case are added Hydrophobic and Hydrophilic. Those objectives are considered among the most important in PSP experiments . Finally, the minimization process (important when the results are too big) was set as True or False, since that is time‐consuming task .…”

Section: Methodsmentioning

confidence: 99%

“…The workflows used in the tests are three: the first based on the protein 1VII , composed of 36 amino acids and a second for the protein 1AGY composed of 200 amino acids. Both are described in DAX files according to the parameters of the predictors available at Koala . The third one is a synthetic workflow named MONTAGE 25, available in Pegasus portal.…”

Section: Methodsmentioning

confidence: 99%

“…The Koala framework, a personalized Galaxy instance developed at ICMC [Color figure can be viewed at wileyonlinelibrary.com]…”

Section: Introductionmentioning

confidence: 99%

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Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

et al. 2020

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Science Gateways provide portals for experiments execution, regardless of the users' computational background. Nowadays its construction and performance need enhancement in terms of resource provision and task scheduling. We present the Modular Distributed Architecture to support the Protein Structure Prediction (MDAPSP), a Service-Oriented Architecture for management and construction of Science Gateways, with resource provisioning on a heterogeneous environment. The Decision Maker, central module of MDAPSP, defines the best computational environment according to experiment parameters. The proof of concept for MDAPSP is presented in WorkflowSim, with two novel schedulers. Our results demonstrate good Quality of Service (QoS), capable of correctly distributing the workload, fair response times, providing load balance, and overall system improvement. The study case relies on PSP algorithms and the Galaxy framework, with monitoring experiments to show the bottlenecks and critical aspects. K E Y W O R D Soptimization, protein structure prediction, science gateways, scientific workflow, service-oriented architecture, WorkflowSim 1 Softw: Pract Exper. 2020;50:899-924. wileyonlinelibrary.com/journal/spe © 2020 John Wiley & Sons, Ltd. 899 900 MARTINS DE OLIVEIRA et al.Another limitation of scientific gateways is the resource provision for its machines, that affect usability and performance. The problem with resource provisioning is a quest for the best configuration, requiring the smallest investment and minimal overheads. In general, inexperienced users cannot make good decisions on what machines to contract. Limited economic resources complicate the scenario.Cloud computing paradigm is among the principal resources available, as it has many advantages: scalability, pay-as-you-go features, on-demand provisioning, and management by third parties. Multiple providers infrastructure and decentralization of resources are expanding and are expected to create impact on diverse areas. 4 Resource providing and management are one of the challenging aspects of this expansion. 5 Despite its success, cloud is not the only option for provisioning. 6 Computer clusters are reliable infrastructures for conducting experiments, mainly High-Performance Computing (HPC) and concurrent algorithms. The advantages of cluster machines are local management, freedom of configuration, reliable storage, and fast network transfers. 3 For those reasons, we investigate the scientific gateway Galaxy, because it is an adaptable system, capable to operate over different infrastructures, including local workstations.Galaxy is a well-known framework for scientific experiments, with workflow management and reproducibility support. Its tools offer practical solutions and addition of new algorithms according to the evolution of the system. The focus is Biomedical science, protein prediction, gene sequencing, and other bioinformatics-related areas. Its advantages are simple interface, reliability, powerful tools, and an active community. 7 However, as any other...

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“…In this study case, we use the Galaxy Koala, and chose a protein predictor with an NSGA‐II algorithm that calculates multiobjective PSP. The experiments design is described in Table .…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The Koala framework, a personalized Galaxy instance developed at ICMC [Color figure can be viewed at wileyonlinelibrary.com]…”

Section: Introductionmentioning

confidence: 99%

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Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

et al. 2020

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“…This research has been conducted in an instance developed at the Institute of Mathematical Sciences and Computation (ICMC) 2 and named Koala, 3 which provides data management, PSP predictors, analysis tools and workflow assistance. The interface is shown in Figure 2 [41] .

Figure 2 Example of a Galaxy implementation available for use at ICMC servers.…”

Section: Methodsmentioning

confidence: 99%

Selection of computational environments for PSP processing on scientific gateways

Oliveira

Estrella

Delbem

et al. 2018

Heliyon

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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.

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Koala: sistema para integração de métodos de predição e análise de estruturas de proteína

Cited by 2 publications

References 35 publications

Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

Optimizing computational resource management for the scientific gateways ecosystems based on the service‐oriented paradigm

Selection of computational environments for PSP processing on scientific gateways

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