In the last twenty years, data science has become a crucial tool in biomedical research. At the CNIC Bioinformatics Unit we blend the latest developments in Computational Biology and Statistical modeling with deep molecular phenotyping of large human cohorts and animal models to improve our understanding of cardiovascular diseases and also of aging-related disorders.
To achieve this broad goal we have three main areas of research:
- Omics data analysis: Ad-hoc analysis and integration of omics data (transcriptomics, methylomics, genomics for germline and somatic variants) in bulk samples or at the single-cell level in animal models and in human samples, to understand the underlying molecular mechanisms of health and disease. For that, we prefer probabilistic models inferred using Bayesian statistics to take into account the noisy and heterogeneous nature of various types of omics data.
- Cardiovascular data science: Exploring the use of machine learning methods for outcome prediction through the integration of molecular and deep phenotyping information in large human prospective and retrospective cohorts. In this area we also work on the development of a data-warehousing solutions expanding the already implemented CNICtranSMART.
- In silico prediction of protein structure for drug design and simulation of changes in protein structure produced by DNA variants.
All statistical methods and tools developed by the Bioinformatics Unit can be found at: https://bioinfo.cnic.es/Apps
- 13/12/2019: In collaboration with the Molecular Regulation of Heart Failure group lead by Dr. Lara-Pezzi we have developed dsReg, a Bayesian model to integrate changes in splicing and RNA-binding protein activity. https://doi.org/10.1093/bioinformatics/btz915
- 26/11/2019: SORS and Bionfo4Women seminar by Dra. Sánchez-Cabo at the BSC: https://www.bsc.es/ca/research-and-development/research-seminars/sors-data-driven-approach-cardiovascular-disease-deep-phenotyping-omics-and-machine-learning
- 25/10/2019: Our paper digitalDLSorter is out! Using scRNA-Seq data coupled with Deep Learning we are able to deconvolute bulk RNA-Seq samples to understand the cellular landscape of different diseases
- 07/10/2019: Dra. Sánchez Cabo participates in the symposium “Innovation in Biomedical Engineering” organized by the Spanish Engineering Institute: https://www.iies.es/events/la-innovacion-en-ingenieria-bio-medica
- 17/07/2019: The CNIC Bioinformatics Unit receives funding from the Ministerio de Ciencia, Innovación, y Universidades (MCIU) [grant no. RTI2018-102084-B-I00) to apply digitalDLsorter to improve the understanding of subclinical atherosclerosis
- 15/06/2018: CNIC hosts the third edition of the largest Health Hackathon in Spanish, co-organized by the Bioinformatics Unit: https://www.cnic.es/es/noticias/cnic-acoge-iii-edicion-hackathon-salud-mayor-maraton-programacion-salud-espanol-3
- 12/06/2018: “Big data” para las personas (in SPANISH) https://www.lavanguardia.com/vida/20180612/4559577153/big-data-para-las-personas.html
- 17/07/2017: APERIM H2020 Project coordinated by Prof. Trajanoski from MedUni Innsbruck awarded with € 2 999 287.50 € to develop new bioinformatics tools for tumor immunology. CNIC Bioinformatics Unit will develop deconvolution methods for bulk RNA-Seq.