Scientific gateways bring enormous benefits to end users by simplifying access and hiding the complexity of the underlying distributed computing infrastructure. Gateways require significant development and maintenance efforts. BioinfoPortal^{[1]<\/sup><\/a>, through its CSGrid[2]<\/sup><\/a>\u00a0 middleware, takes advantage of Santos Dumont [3]<\/sup><\/a> heterogeneous resources. However, task submission still requires a substantial step regarding deciding the best configuration that leads to efficient execution. This project aims to develop green and intelligent scientific gateways for BioinfoPortal supported by high-performance computing environments (HPC) and specialised technologies such as scientific workflows, data mining, machine learning, and deep learning. The efficient analysis and interpretation of Big Data opens new challenges to explore molecular biology, genetics, biomedical, and healthcare to improve personalised diagnostics and therapeutics; finding new avenues to deal with this massive amount of information becomes necessary. New Bioinformatics and Computational Biology paradigms drive storage, management, and data access. HPC and Big Data advanced in this domain represent a vast new field of opportunities for bioinformatics researchers and a significant challenge. the BioinfoPortal science gateway is a multiuser Brazilian infrastructure. We present several challenges for efficiently executing applications and discuss the findings on improving the use of computational resources. We performed several large-scale bioinformatics experiments that are considered computationally intensive and time-consuming. We are currently coupling artificial intelligence to generate models to analyze computational and bioinformatics metadata to understand how automatic learning can predict computational resources’ efficient use. The computational executions are conducted at Santos Dumont, the largest supercomputer in Latin America, dedicated to the research community with 5.1 Petaflops and 36,472 computational cores distributed in 1,134 computational nodes.}<\/a><\/p>\n

By:<\/strong><\/p>\n

Carneiro, B. Fagundes, C. Osthoff, G. Freire, K. Oca\u00f1a, L. Cruz, L. Gadelha, M. Coelho, M. Galheigo, and R. Terra are with the National Laboratory of Scientific Computing, Rio de Janeiro, Brazil.<\/p>\n

Carvalho is with the Federal Center for Technological Education Celso Suckow da Fonseca, Rio de Janeiro, Brazil.<\/p>\n

Douglas Cardoso is with the Polytechnic Institute of Tomar, Portugal.<\/p>\n

Boito and L, Teylo is with the University of Bordeaux, CNRS, Bordeaux INP, INRIA, LaBRI, Talence, France.<\/p>\n

Navaux is with the Informatics Institute, the Federal University of Rio Grande do Sul, and Rio Grande do Sul, Brazil.<\/p>\n

References:<\/strong><\/p>\n

Oca\u00f1a, K. A. C. S.; Galheigo, M.; Osthoff, C.; Gadelha, L. M. R.; Porto, F.; Gomes, A. T. A.; Oliveira, D.; Vasconcelos, A. T. BioinfoPortal: A scientific gateway for integrating bioinformatics applications on the Brazilian national high-performance computing network. Future Generation Computer Systems, v. 107, p. 192-214, 2020.<\/p>\n

Mondelli, M. L.; Magalh\u00e3es, T.; Loss, G.; Wilde, M.; Foster, I.; Mattoso, M. L. Q.; Katz, D. S.; Barbosa, H. J. C.; Vasconcelos, A. T. R.; Oca\u00f1a, K. A. C. S; Gadelha, L. BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments. PeerJ, v. 1, p. 1, 2018.<\/p>\n

Coelho, M.; Freire, G.; Oca\u00f1a, K.; Osthoff, C.; Galheigo, M.; Carneiro, A. R.; Boito, F.; Navaux, P.; Cardoso, D. O. Desenvolvimento de um Framework de Aprendizado de M\u00e1quina no Apoio a Gateways Cient\u00edficos Verdes, Inteligentes e Eficientes: BioinfoPortal como Caso de Estudo Brasileiro In: XXIII Simp\u00f3sio em Sistemas Computacionais de Alto Desempenho – WSCAD 2022 (https:\/\/wscad.ufsc.br\/), 2022.<\/p>\n

Terra, R.; Oca\u00f1a, K.; Osthoff, C.; Cruz, L.; Boito, F.; Navaux, P.; Carvalho, D. Framework para a Constru\u00e7\u00e3o de Redes Filogen\u00e9ticas em Ambiente de Computa\u00e7\u00e3o de Alto Desempenho. In: XXIII Simp\u00f3sio em Sistemas Computacionais de Alto Desempenho – WSCAD 2022 (https:\/\/wscad.ufsc.br\/), 2022.<\/p>\n

Oca\u00f1a, K.; Cruz, L.; Coelho, M.; Terra, R.; Galheigo, M.; Carneiro, A.; Carvalho, D.; Gadelha, L.; Boito, F.; Navaux, P.; Osthoff, C. ParslRNA-Seq: an efficient and scalable RNAseq analysis workflow for studies of differentiated gene expression. In: Latin America High-Performance Computing Conference (CARLA), 2022, Rio Grande do Sul, Brazil. Proceedings of the Latin American High-Performance Computing Conference – CARLA 2022 (http:\/\/www.carla22.org\/), 2022.<\/p>\n

^{[1]<\/sup><\/a> https:\/\/bioinfo.lncc.br\/<\/p>\n}

^{[2]<\/sup><\/a>} https:\/\/git.tecgraf.puc-rio.br\/csbase-dev\/csgrid\/-\/tree\/CSGRID-2.3-LNCC<\/a><\/p>\n

^{[3]<\/sup><\/a> https:\/\/https:\/\/sdumont.lncc.br<\/p>\n","protected":false},"excerpt":{"rendered":"}

Scientific gateways bring enormous benefits to end users by simplifying access and hiding the complexity of the underlying distributed computing infrastructure. Gateways require significant development and maintenance efforts. BioinfoPortal[1], through its CSGrid[2]\u00a0 middleware, takes advantage of Santos Dumont [3] heterogeneous resources. However, task submission still requires a substantial step regarding deciding the best configuration that […]<\/p>\n","protected":false},"author":2,"featured_media":2782,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[2],"tags":[261,180,238,101,209],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/risc2-project.eu\/index.php?rest_route=\/wp\/v2\/posts\/2781"}],"collection":[{"href":"https:\/\/risc2-project.eu\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/risc2-project.eu\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/risc2-project.eu\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/risc2-project.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2781"}],"version-history":[{"count":2,"href":"https:\/\/risc2-project.eu\/index.php?rest_route=\/wp\/v2\/posts\/2781\/revisions"}],"predecessor-version":[{"id":2784,"href":"https:\/\/risc2-project.eu\/index.php?rest_route=\/wp\/v2\/posts\/2781\/revisions\/2784"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/risc2-project.eu\/index.php?rest_route=\/wp\/v2\/media\/2782"}],"wp:attachment":[{"href":"https:\/\/risc2-project.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2781"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/risc2-project.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2781"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/risc2-project.eu\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2781"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}