Seminário “Four cases of bioinformatics applied to plant sciences” que será proferido pelo Dr. Diego M.R. Pachón, na quinta feira dia 11 de maio, às 16 horas, no Auditório Prof. Dr. Adilson Leite - CBMEG.

Dr. Diego was graduated in Biology from Universidad Nacional de Colombia, Bogotá in 2001, working on molecular epidemiology of Vibrio cholera, followed by a 3 month Internship at the Swiss Institute of Bioinformatics, 2002. Hold a research assistant position in bioinformatics at the Universität Potsdam in Potsdam, Germany between 2002 and 2005. Started doctoral studies in 2005 at the Universität Potsdam, obtaining his PhD in 2008, working on the computational identification of plant transcription factors. Between 2008 and 2010 had a postdoctoral fellowship at the Max Planck Institute of Molecular Plant Physiology working on integrative databases, particularly the Gabi Primary Database. Between 2010 and 2012 held an assistant professor position at the Universidad de Los Andes in Bogotá, Colombia, where among many activities, started the first master program in computational biology in the country and the first Colombian congress in computational biology and bioinformatics. Between 2013 and 2016 held a research scientist position at the Brazilian Bioethanol Science and Technology Laboratory, in Campinas, Brazil. Has advised and/or co-advised more than 10 postgraduate students to date, and published over 30 research articles, in the areas of bioinformatics methods, evolution of gene families, genomics, among others.

In his talk, Dr. Diego will briefly present results from four different projects that he has developed in his career, since 2002 until today, showing how bioinformatics methods can be applied to help answering biological questions, and to better understand biological systems. 1) Identification of orphan transcripts in Arabidopsis thaliana. 2) Development of a computational strategy for the identification of transcription factor genes in plants. 3) Development of integrative databases in biology and 4) Exploring the Sugarcane genome exploiting synthetic long reads.