Schedule

For full schedule, please see http://sbbd.org.br/2019/sbbd-program/

Plenary Session 1

07/10 07:30 pm
DANIEL OLIVEIRA
When bioinformatics meets databases: achievements and lessons learned

Plenary Session 2

08/10 04:30 pm
WERNER TREPTOW
Binding of Small Ligands to Two-state Membrane Proteins

For more informations about Plenary Sessions, please see http://bsb.sbc.org.br/2019/speakers/


Minicurso (BSB): Bancos de Dados Biológicos

Tainá Raiol e Waldeyr Mendes Cordeiro da Silva
Data: Segunda-feira, 07/10/2019
Hora: 09:00 às 12:30h
Abstract
The biological data has increased in volume and diversity, becoming known as -omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, and others). Molecular Biology and Databases have played a close and cooperative relationship, which has made it possible to intensify advances in many areas. This course will provide an overview of a range of biological databases for different purposes and how to retrieve data both using the databases interfaces and some API’s. The database range includes biological sequences data, protein structure, and interaction data, and metabolic pathways. Necessary skills: Basic knowledge of Biology and computers. Desirable to maximize the experience: Python basics.

Resumo
O volume e variedade de dados biológicos oriundos de sequenciamento de alto desempenho têm aumentado significativamente. Esses dados são conhecidos como ômicos, palavra que representa o conjunto de dados genômicos, transcritômicos, epigenômicos e outros. A Biologia Molecular e os bancos de dados têm mantido um estreito e cooperativo relacionamento, o qual tem possibilitado diversos avanços em diferentes áreas. Este curso oferece uma visão geral sobre como os bancos de dados biológicos se organizam. Também mostra como buscar dados nesses bancos tanto usando interfaces Web como APIs. Os bancos de dados abordados incluem bancos de sequências biológicas, como genomas, bancos de estruturas de proteínas, de interação entre proteínas e bancos de dados de metabolismo. Pré-requisitos: Conhecimento básico em Biologia e uso de computadores. Desejável para máximo aproveitamento: Python básico.


Oral Presentation:

ST1: Technical Session 1

  • On Clustering Validation in Metagenomics Sequence Binning (Paulo Oliveira, Kleber Padovani and Ronnie Alves)
  • Genome Assembly using Reinforcement Learning (Roberto Xavier, Kleber Souza and Ronnie Alves)
  • GeNWeMME: a network-based computational method for prioritizing groups of significant related genes in cancer (Jorge Francisco Cutigi, Adriane Feijo Evangelista and Adenilso Simao)
  • Searching in silico novel targets for specific coffee rust disease control (Jonathan de Lima, Jennifer Decloquement, Bernard Maigret, Diana Fernandez, Danilo Pinho, Erika Albuquerque, Marcelo Rodrigues and Natalia Martins)

ST2: Technical Session 2

  • Block-Interchange Distance Considering Intergenic Regions (Ulisses Dias, Andre Rodrigues Oliveira, Klairton Lima Brito and Zanoni Dias)
  • K-mer mapping and RDBMS indexes (Sergio Lifschitz, Edward Hermann Haeusler, Maristela Holanda, Paulo Cavalcanti Gomes Ferreira and Elvismary Molina de Armas)
  • A clustering approach to identify candidates to housekeeping genes based on RNA-seq data (Edian Franklin Franco De Los Santos, Dener Maués, Luís Carlos Guimarães, Vasco Azevedo, Artur Luiz Silva, Preetam Ghosh, Jefferson Morais, Rommel T. Ramos and Ronnie Alves)
  • A Framework Approach for Quality Feature Analysis of Genome Assemblies (Guilherme Neumann, Elvismary Molina de Armas,   Fernanda Baião, Ruy Milidiú and Sergio Lifschitz)

ST3: Technical Session 3

  • MDR SurFlexDock: a semi-automatic webserver for discrete receptor-ensemble docking (João Luiz De Almeida Filho and Jorge H. Fernandez)
  • Peptides of Arthropods from the Brazilian Cerrado Biome Unveiled by Transcriptome Analysis (Giovanni M. Guidini, Waldeyr M. C. Da Silva, Thalita S. Camargos, Caroline F. B. Mourão, Priscilla Galante, Tainá Raiol, Marcelo M. Brígido, Maria Emília M. T. Walter and Elisabeth N. F. Schwartz Venom Gland)
  • Predicting Cancer Patients Survival Using Random Forests (Camila Takemoto Bertolini, Saul Leite and Fernanda Almeida)
  • Identifying Schistosoma mansoni essential protein candidates based on machine learning (Francimary Garcia, Gustavo Paiva Guedes and Kele Teixeira Belloze)
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