Research projects

Rare diseases (RDs) can be caused by a wide variety of mutations and tend to present complex symptomatic profiles that make it difficult for the non-specialist physician to identify them. The low frequency in the population that characterizes these diseases makes comparative and statistical studies between patients extremely difficult, complicating and delaying their correct diagnosis and impeding treatment discovery through conventional clinical trials.
This project aims to improve our understanding of multiple rare diseases through a multidisciplinary collaborative approach combining groups expertise in different areas of bioinformatics and clinical research. As we begin to better understand these pathologies at the genetic and molecular level, light is being shed on novel ways to intervene therapeutically in these diseases. To achieve these goals, this project develops various computational approaches, including modeling and the use of biomedical networks to identify genes related to disease and its symptoms, affected underlying genetic systems and pathways, and to find new therapeutic targets and pathways. We have developed a bioinformatics workflow to analyze transcriptomic data, focusing on gene expression changes related to functional systems underlying the rare diseases Lafora and PMM2-CDG (Jabato et al., 2021).

We have also made important advances in the development of a comprehensive workflow for miRNA transcriptomic analysis, which is capable of predicting, based on a correlation analysis, target genes whose expression is regulated by miRNAs. We have also implemented a workflow based on tripartite networks of mutations, phenotypes and patients that has allowed us to link phenotypes with genes and functional systems in patients and to predict comorbid groups of pathological phenotypes, as well as to identify and validate patient cohorts (Díaz-Santiago et al., 2020; Jabato et al., 2020; Díaz-Santiago et al., 2021; Rojano et al., 2021; Ranea et. Al., 2022; Pazos et al., 2022). Many of the results obtained from the bioinformatics analyzes are being contrasted with pre-existing clinical data and are starting to be validated experimentally, with promising preliminary results being obtained.