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We are interested in developing new approaches for the integration of omics data in order to discover new biomarkers and classification schemas of complex diseases. Some of these methods are available in public bioinformatics tools that can be used by researchers to study and interpret their data.

Omics Data Integration and Meta-Analysis

One of our aims is to develop new methods for the integration and analysis of heterogeneous omics data in order to decipher the molecular mechanisms behind complex diseases. In this context, we have implemented new approaches that allow us to combine and compare gene expression datasets from different origins (Carmona-Saez et al., 2017, Toro-Domínguez et al. 2014) or perform an integrative analysis of methyolome and transcriptome (Martorell et al., mCSEA. 2019). We have also developed user-friendly applications for meta-analysis of gene expression and genetic association studies (MetaGENyO and ImaGEO) that allows users to combine and jointly analyze data from different sources.

Drug Repurposing analysis

The availability of large panels of drug-induced molecular signatures through consortiums such as Connectivity Map or public gene expression repositories such as Gene Expression Omnibus database is allowing us to develop bioinformatics tools to explore these databases in order to discover new uses for existing drgus. We initiated this line of research few years ago with the development of a tool that was able to compare phenotypes based on their similarities using all information available in NCBI-GEO database (Vazquez et al., 2010). In the last years, we are using these approaches for exploring potential new treatments in the field of autoimmune diseases (Toro-Domínguez et al., 2017).

New biomarkers in Complex Diseases from multi-omics data analysis

The integration of different layers of -omics data can provide more reliable biomarkers for complex diseases. In this context, we are promoting new approaches that integrate omics data for discovering conserved and coherent patterns of genes and proteins across heterogeneous datasets, thus allowing us to define novel or more robust biomarkers of complex diseases.

Projects and Networks

We are part of different projects and networks, including:

– The PRECISESADS project that aims to use multi-omics data to establish a classification of systemic autoimmune diseases based on molecular patterns rather than clinical manifestations. Our efforts here are focused on the implementation of state-of-the-art clustering and machine learning methods that help us to integrate and establish new classification schemas for these diseases (Toro-Domínguez et al. 2018).

– The ONCONET-SUDOE, a cooperation network in oncology, co-financed by the Interreg Sudoe Programme through the European Regional Development Fund.  In collaboration with Fundación Parque Tecnológico de Ciencias de la Salud de Granada – PTS, we have promoted initiatives such as the Oncothon, a datathon-oriented event that brought the opportunity to bioinformaticans and biomedical/clinical researchers to work together and explore the potential of cancer genomics data to open new pathways for cancer diagnosis and treatment.

TransBioNet, the Translational Bioinformatics Network coordinated by the Spanish National Bioinformatics Institute (INB) has been created as the reference network for Translational Bioinformatics that brings together most of the bioinformatics units and groups working at