The Computational Biology Group at TECNUN is a multidisciplinary team with an extensive experience in the development of optimization algorithms, statistical analyses, as well as machine learning and deep learning methods, applied to different biological problems. We focus mainly on human health by the integration of high-throughput data (genomics, transcriptomics, proteomics, metabolomics, etc.) and biological databases (genomics, pharmacologics, metabolics, etc.). A snapshot of projects we are currently working on is provided below.
· Metabolic Reprogramming in cancer for the identification of novel therapeutic targets and response biomarkers.
· Precision Oncology. Integration of large-scale gene silencing and pharmacologic experiments.
· Alternative Splicing in cancer. Alterations, causes and effects.
· Predictive Models of drug toxicity based on structural features of the molecules.
· Influence of the gut microbiota in the context of health and nutrition.
· Compression schemes tailored to different omics data. Active members of the development of the MPEG-G standard for genomic information representation.
· DNA sequencing analysis. Methods for improved germline and somatic variant calling.
· Gene Regulatory Network (GRN) inference for bulk and single-cell RNAseq data.
· Characterization of HERV (human endogenous retroviruses) in cancer and brain samples.
We actively collaborate with different research centers and companies. Among others, we list CIMA, Clínica Universidad de Navarra, CIC BioGUNE, Biodonostia, FISABIO, Onkologikoa, Universidad de Granada, Celgene, Biobide, Polytechnic University of Catalonia, University of Illinois at Urbana-Champaign, Stanford University and University of the Republic (Uruguay).