GENOMICS OF GENE EXPRESSION
We develop statistical methods and software tools that analyze the dynamic aspects of transcriptomes
We are interested in understanding functional aspects of gene expression at the genome-wide level and across different organisms and its relationship with diseases and traits. For that, we develop statistical methods and software tools that analyze the dynamic aspects of transcriptomes, integrate these with other types of molecular data and annotate them functionally, with a special focus on Next Generation Sequencing (NGS) data. Functional genomics research is complemented with the development of bioinformatics software for the analysis of genomics data.
Our current research lines are:.
- Integration of multi-omic data: Implementation of statistical methods for experimental planning, and integrative analysis of different omics such as Next Generation Sequencing data, proteomics, metabolomics, clinical data, etc. We apply these strategies in data modeling for systems medicine in pathologies such as diabetes, cancer and neurologial disorders.
- Methods for the analysis of transcriptome complexity through single-molecule third generation sequencing approaches and single-cell genomics. We are interested in how alternative splicing creates functional diversity that shapes the complexity of higher eukaryotes and is a key player in development and onset of high incidence diseases such as diabetes and Alzheimer.
The group has published over 100 papers that have received more than 15,400 citations. We are creators of 12 bioinformatics tools, including Blast2GO, Paintomics, Qualimap, NOISeq, maSigPro and SQANTI, with tens of thousands of users world-wide. We have lead 26 projects including two EU projects, STATegra and DEANN, as coordinator, the ITN ChroMe as WP4 leader, and a Prometeo project from the Generalitat Valenciana.