Genomes · Rhodnius prolixus
The first genome sequence of a non-dipteran insect vector of a major human parasitic disease, Chagas disease.
Browse the genomes →
We use computation to read and interpret biological data: from the genomes of insect vectors to gene expression in cancer, from protein and enzyme structure to metabolic models and machine learning.
Seven connected fronts — the same genome and expression data feed protein modelling, metabolic models and machine learning. Click an area for the full description, who works on it and the publications.
Genome and biology of the kissing bug Rhodnius prolixus, vector of Chagas disease, and of other arthropods of medical interest.
Assembly, annotation and comparative analysis of genomes, with an emphasis on disease vector insects.
Transcriptome assembly and differential gene expression analysis across tissues and conditions.
Structural modelling, docking and prospecting of enzymes and proteins of biotechnological and pharmacological interest.
Reconstruction of genome-scale metabolic models to understand and optimise the production of industrially relevant compounds.
Machine learning applied to genomic and transcriptomic data to find patterns, classify samples and prioritise genes.
Applications of bioinformatics to biomedical questions, such as oncology and antibiotic resistance.
The photos show who on the current team works in each area today. The publication count is the laboratory's history in that area — current members are not necessarily authors of those papers.
The first genome sequence of a non-dipteran insect vector of a major human parasitic disease, Chagas disease.
Browse the genomes →
EUGENE is a boy who loves genetics and who learned to find genes with the help of Mendel and his peas — join him and hunt for words hidden in the genome.
Meet Eugene →
A tool that suggests candidate reference and validation genes for your RT-qPCR experiments.
View on GitHub →Usually offered in the first semester.
Usually offered in the second semester.
Rafael Dias Mesquita
Department of Biochemistry, Institute of Chemistry, UFRJ
Av. Athos da Silveira Ramos, 149 — CT building, block A, room 535
Ilha do Fundão, Rio de Janeiro — RJ, 21941-909, Brazil
Phone: +55 21 3938-7356
E-mail: bioinformatica.iq.ufrj@gmail.com