Traglia, Germán Matías

Research interests

Genomic evolution and comparative genomics in bacteria relevant to human, animal, and agricultural health
Epidemiology and genomics of pathogens with multi-antimicrobial resistance
Genetic evolution associated with horizontal gene transfer
Prediction of mobile genetic elements using machine learning algorithms

Working area

>>> Genomic evolution of antibiotic resistance in various clinically important bacterial pathogens:

Infectious diseases increase in prevalence year after year, threatening not only human but also animal health. In addition to the global causes of death from viruses, bacteria, and parasites, which represent enormous burdens on regional and global public health systems, the progressive increase in multidrug resistance in all geographic regions is considered one of the most relevant emerging problems worldwide, according to data from the World Health Organization (2015). Among the main clinically important multidrug-resistant pathogens are: Acinetobacter baumannii, Klebsiella pneumoniae, Achromobacter sp., Pseudomonas sp., Burkholderia cepacia complex, Salmonella sp., Staphylococcus, among others. This line of research is comprised of several sublines developed in collaboration with several research groups, including Dr. Andrés Iriarte and Magister Leticia Diana (UdeLaR), Dr. María Soledad Ramirez and Dr. Marcelo Tolmasky (University of California), and Dr Marisa Almuzara, Dr Claudia Barberis and Dr Carlos Vay (University of Buenos aires). The main objective of these lines of research is to identify how antibiotic resistance evolves in the hospital environment and during infection. In parallel, we seek to determine the different mechanisms by which antibiotic resistance is acquired in the main pathogens of clinical importance.

>>> Prediction and classification of genetic elements in bacterial genomes using machine learning algorithms:

The massive sequencing of bacterial genomes has led to an exponential growth in information. As a result, there are large databases ranging from millions to billions of data, and it is becoming increasingly difficult to work with such large volumes. Machine learning strategies have opened up the possibility of providing feasible solutions for data analysis. One of the areas where this is gaining significant relevance is Genomics, allowing us to work with millions of data sets and predict gene functions, classify genes or genetic elements, among many other applications. A line of research associated with the prediction of mobile genetic elements, such as insertion sequences and bacterial transposons, in bacterial genomes using machine learning algorithms was established. Consequently, based on the implementation of machine learning algorithms, bioinformatics tools (programs) will be developed for easy implementation of the algorithms. This line of research is led by a master's student, who is currently completing the thesis associated with this line.