“Science is not only a disciple of reason but also one of romance and passion” – Stephen Hawking

Our mission is to understand the (epi)genome biology and its impact on cancer and other diseases using computational multi-omics approaches. Such methods rely on the statistical analysis and integration of large scale data (high-throughput sequencing, microarrays, proteomics, high-throughput screening) and clinical/phenotypic data.


Generate scientific Knowledge by Mining Biological Data and extracting Information

The wealth of genomic data generated by the International Cancer Genome Consortium (ICGC), Roadmap Epigenomics, ENCODE and GTEX has allowed the genome, epigenome and transcriptome profiling of a wide range of normal and pathological tissues. Given the accumulation of this huge amount of biological information, handling and mining big data is becoming increasingly mandatory for major research. However, more than data processing and integration, the key challenge relies on extracting information and generating scientific knowledge. Therefore, we aim to achieve biomedical advances through novel ways of combining large multi-omics and phenotypic data. Hence, we integrate large-scale profiles at different levels: genome (information within the DNA sequence and mutations), epigenome (DNA methylation, histone modifications, nucleosome positioning and chromosome conformation), transcriptome (RNA expression and isoforms). Integration and visualization of such complex data sets is crucial for interpretation and decoding of the underlying biology associated pathological conditions.

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Unveiling the complexity of cell biology and underlying disregulation of diseases by deciphering multi-omics data

A wide range of diseases, from cancer to age-related disorders, are associated with an increase in transcriptional noise and expression of many aberrant mRNAs. While it is clear that the regulation of epigenome and transcriptome networks is a crucial component of a healthy cell, it is relatively unknown the molecular mechanisms underlying its misregulation for many pahthological conditions. Therefore, we aim to decipher pathological conditions using multi-omics approaches, identifying molecular events to be further used as biomarkers and therapeutic targets.

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