Tecnun's Computational Biology group is a team multidisciplinary, with extensive experience in the development of optimization algorithms, statistical analysis, as well as methods based on machine learning and deep learning, mainly focused on issues in the field of human health through high molecular resolution data (genomics, transcriptomics, proteomics, metabolomics, etc) and instructions of biological data (genomic, pharmacological, metabolic, etc). Currently, we are working on the following topics:

• Metabolic reprogramming in cancer for the identification of new therapeutic targets and biomarkers of response.

• Precision oncology. Integration of large-scale gene silencing and pharmacological experiments for the finding of new biomarkers of treatment effectiveness.

• Study of alternative splicing in different types of cancer: its alterations, causes and effects.

• Influence of the gut microbiota in the context of health and nutrition.

• development of enzyme promiscuity techniques to better understand the interaction between per diem expenses and gut microbiota

• Compression schemes adapted to different data omics. Active members of development of the MPEG-G standard for the representation of genomic information.

• DNA sequencing analysis. Methods for germline enhancement and somatic variant calling.

• Inference of gene regulatory networks for RNA data from bulk and single-cell sequencing.

• Application of Deep Learning techniques to discover relationships between mutations and drugs.

• Application of Deep Learning techniques to better describe the effect of splicing factors between the different events produced during transcription.