We belong to several institutions, providing support as a bioinformatics analyses platform and software development. We think that communities are important to create bonds and to make science more accessible to all, as well as to engage in collaborative beliefs of researching.


The Network of Biomedical Research in Rare Diseases consortium (CIBERER Consorcio Centro de Investigación Biomédica en Red en Enfermedades Raras in Spanish) is made up of 62 research groups belonging to institutions of diverse kinds: University Hospitals, Universities, public research organisations. The aim is to improve our knowledge on epidemiology, the causes and mechanisms for production of rare diseases. This research is the basis for providing new tools for diagnosis and therapy of rare diseases, backing translational or transfer research between the scientific medium of the laboratory and the clinical medium of healthcare centres. The collaboration and cooperation between biomedical and clinical research groups are prioritised. Moreover, CIBERER aims to develop high quality cooperative and innovative research into Rare Diseases, fostering the transfer of results to clinical practice. The specific objectives are mainly based on the development of new treatments and improving access to diagnosis of RDs. We contribute to this objective by developing new resources and tools to aid the clinical community.


The Institute of Biomedicine of Seville (Instituto de Biomedicina de Sevilla - IBiS), was created on the 24th March 2006, with the mission to contribute to the strengthening of biomedical research in Spain, with a view to becoming a research centre of excellence in southern Europe.

IBiS was established as a multidisciplinary biomedical research centre within the complex that houses the Virgen del Rocío University Hospital. Its main objective is to undertake competitive research at an international level on the causes of the most prevalent pathologies in the population, and to develop new methods for their diagnosis and treatment. IBiS is based on fundamental research at the molecular and cellular level with a view to promote the rapid transfer of knowledge to the clinical setting, at the same time improving the quality of clinical and epidemiological research. Our team is part of the Oncohematology and Genetics Department, as the Systems Computational Medicine team.


The Spanish National Bioinformatics Institute (‘Instituto Nacional de Bioinformática’ in Spanish, or short INB), founded in 2003, is the bioinformatics technology platform of the Carlos III Health Institute (‘Instituto de Salud Carlos III‘ or ISCIII) since January 2018. Here, we facilitate and provide the tools for the use of patient’s genomic data in the current clinical practice as a central stakeholder in the Personalized Medicine plan of the Andalusian community. The INB serves in the coordination, integration and development of Spanish bioinformatics resources in projects in the areas of genomics, proteomics and translational medicine. It has contributed to the creation of a consistent computational infrastructure in the area of bioinformatics, participated in national and international genome projects, and trained bioinformatics users and developers.


As a part of consortia, we work with others to advance in biological knowledge of diseases.

Disease Maps

Disease Maps Project is an open community effort to comprehensively represent disease mechanisms for various diseases. The consortium comprises computational biology teams working on disease models and clinicians or experimental biologists who would like to contribute as domain experts. The aim is to actively expand the knowledge in molecular mechanisms of diseases.

Machine Learning Frontiers in Precision Medicine

The Marie Curie Innovative Training Network entitled “Machine Learning Frontiers in Precision Medicine” brings together leading European research institutes in machine learning and statistical genetics, both from the private and public sector, to train 14 early stage researchers. These scientists will apply machine learning methods to health data. The goal is to reveal new insights into disease mechanisms and therapy outcomes and to exploit the findings for precision medicine, which hopes to offer personalized preventive care and therapy selection for each patient.