Information technology applied to bioenergy genomics: probabilistic annotation using artificial intelligence

• Grant number: 09/53161-6
• Support Opportunities: Program for Research on Bioenergy (BIOEN) - Regular Program Grants
• Field of knowledge: Interdisciplinary Subjects
• Agreement: Microsoft Research
• Principal Investigator: Ricardo Zorzetto Nicoliello Vêncio
• Grantee: Ricardo Zorzetto Nicoliello Vêncio
• Host Institution: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil

Abstract

An alternative to the problem of fossil fuels depletion is the use of renewable energy. In Brazil, sugarcane (Saccharum officinarum) is used for years as alternative energy source and, therefore, Brazil has become a key player in alternative energy development. Our main aim is to develop methods and tools to attack some of the bioinformatics issues raised in sugarcane genomics research. In order to achieve this, we opt for Sifter (Engelhardt et al., 2006), a powerful method based on Bayesian Networks. Our major aim was to establish a local implementation of the Sifter methodology for application in bioenergy related problems and following is to improve the original source code performance, potentially allowing it to be used in a genome-wide scale. (AU)

Scientific publications

(5)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

ALMEIDA-E-SILVA, DANILLO C.; VENCIO, RICARDO Z. N.. SIFTER-T: A scalable and optimized framework for the SIFTER phylogenomic method of probabilistic protein domain annotation. BIOTECHNIQUES, v. 58, n. 3, p. 140-142, MAR 2015. (09/53161-6)

SILVA, RICARDO R.; JOURDAN, FABIEN; SALVANHA, DIEGO M.; LETISSE, FABIEN; JAMIN, EMILIEN L.; GUIDETTI-GONZALEZ, SIMONE; LABATE, CARLOS A.; VENCIO, RICARDO Z. N.. ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS-based metabolomics. Bioinformatics, v. 30, n. 9, p. 1336-1337, MAY 1 2014. (09/53161-6)

ABRAHAM, KURUVILLA JOSEPH; DIAZ, CLARA. Identifying large sets of unrelated individuals and unrelated markers. SOURCE CODE FOR BIOLOGY AND MEDICINE, v. 9, n. 1, p. 8-pg., 2014-12-01. (09/53161-6)

JIANG, YUXIANG; ORON, TAL RONNEN; CLARK, WYATT T.; BANKAPUR, ASMA R.; D'ANDREA, DANIEL; LEPORE, ROSALBA; FUNK, CHRISTOPHER S.; KAHANDA, INDIKA; VERSPOOR, KARIN M.; BEN-HUR, ASA; et al. An expanded evaluation of protein function prediction methods shows an improvement in accuracy. GENOME BIOLOGY, v. 17, p. 19-pg., 2016-09-07. (09/53161-6)

JIANG, YUXIANG; ORON, TAL RONNEN; CLARK, WYATT T.; BANKAPUR, ASMA R.; D'ANDREA, DANIEL; LEPORE, ROSALBA; FUNK, CHRISTOPHER S.; KAHANDA, INDIKA; VERSPOOR, KARIN M.; BEN-HUR, ASA; et al. An expanded evaluation of protein function prediction methods shows an improvement in accuracy. Genome Biology, v. 17, SEP 7 2016. (09/53161-6)