Cristian Enrique Cadena Caballero
Cristian Enrique Cadena Caballero finished his undergraduate studies in Biology at the Industrial Santander University. Since his university training he has participated in research projects in evolution of vertebrate and invertebrate neuropeptide genes, especially with the evolution of Gonadotropin Releasing Hormone and its possible origin with lower organisms. Other of his discoveries have been the validation and nature of genes that were determined by computer models and found in living beings. In addition, he has participated in genomic projects in collaboration with the Autonomous University of Sinaloa to establish the importance of Vitelogenin in the reproduction and growth of Pacific white shrimp for fisheries and aquaculture. his bioinformatics and biological knowledge of the use in data mining, through High Performance Computing, allowed him to participate in a project to improve the one-step real-time RT-PCR diagnosis of the SARS-CoV-2 virus and Influenza A H1N1 and its genomic characterization through New Generation Sequencing (NGS), since the beginning of the COVID-19 pandemic. Currently, it collaborates with the company Technologies in Organic Nutrition (NORGTECH SA) of Bucaramanga, Colombia, which is a leader in innovation and scientific developments and applied to the organoleptic improvement and properties of the egg, as well as the nutritional metagenomics of chicken whose data are treated by HPC. Among his main achievements has been the characterization of 10 SARS-CoV-2 genomes and other variants that show a possible viral regulation that is under study.
Title:
High Performance Computing applied to viral genomic diagnosis and sequencing for epidemiological surveillance
Abstract:
Viral diseases have generated epidemics and pandemics in human society, as well as animals and plants used for food. However, thanks to technological and scientific developments, especially Next Generation Sequencing, it is currently possible to know viral genetic information and its expression in a short time. Therefore, the thousands or millions of nucleotides that make up a viral genome can be obtained in days and this information stored in international public databases to be applied in basic sciences, health and epidemiological surveillance. However, the process of downloading and analyzing nucleotide information can take months or years with conventional computational technologies. Given the health emergency generated in the COVID-19 pandemic, a series of tools were developed by High Performance Computing (HPC) in the GUANE-1 supercomputer of the Center for Supercomputing and Scientific Calculation of the Industrial Santander University, a set of tools that allow the download and purge genes and genomes in seconds or minutes depending on the quantity. The result obtained can be analyzed by evolutionary software adaptations whose consensus allows establishing the constant and variable regions of a gene or genome to design diagnostic systems for the Polymerization Chain Reaction (PCR) and its variants; in addition, of nucleotide elements of regulation of genomes. In this sense, other tools were developed that establish the in silico characteristics of the diagnosis with the selected region and regions for genomic sequencing. The results of these applications in the SARS-CoV-2 and Influenza A H1N1 viruses are analyzed and discussed.