Summary

Many diseases can be prevented or managed if they are detected at an early stage. Thus knowledge of the genetic risk factors of an individual could have enormous value for health care, disease prevention as well as healthy aging. Next Generation Sequencing (NGS) has been established as a key method in disease research and allowed to identify hundreds of new candidate genes for diseases and cancer. NGS techniques enable analyses of the genome, transcriptome, epigenome and microbiome of an individual in a tissue or cell type specific manner at single nucleotide resolution. This allows for identification of disease specific alterations at the molecular level and will likely result in optimized individual treatment of patients.  Our group utilizes various NGS methods (Exome-seq, RNA-seq, BS-seq, ChIP-seq, ATAC-seq etc.) and develops algorithms for integrative analysis of the resulting data, in order to detect genomic and epigenomic variation related to disease, cancer or response to treatment. We envision studying multiple snapshots of the genomic and epigenomic landscape of tissues during the development of a disease, i.e. the personal OMICs profile of a patient, to better understand the impact of genetic predisposition, epigenomic and regulatory variability, viral and bacterial infections and environmental effects on the development of Mendelian and complex diseases.

Research projects

• We are studying signatures of tumour clonal evolution (positive and negative selection) to reveal cancer driver genes and to understand the rapid formation of treatment resistant tumour cells. We developed a novel Bayesian model for identification of recurrently mutated genes taking into account measures of positive selection and clonal fitness. Applying our model to several thousand cases of 22 cancers studied as part of ICGC and TCGA we demonstrated the high accuracy of our approach and identified most known as well as several novel candidate driver genes.
• Many cancers can be managed or cured if they are diagnosed at an early stage, making knowledge about potential genetic risk factors a valuable resource for cancer prevention. The Pan-Cancer Analysis of Whole Genomes (PCAWG) project has as main goal the complete characterization of 2,833 whole cancer genomes. We perform a detailed characterization of the germline cancer genome, including: a) the evaluation of the burden of genomic changes that are involved in cancer susceptibility, b) the definition of the interplay between cancer somatic mutations and germline changes in driving cancer evolution and c) regulatory variation in enhancers and promoters.
• Novel or inherited genetic variations can lead to rare Mendelian diseases. Whole exome sequencing (WES) has recently been established as a key approach for identification of disease related genetic variations and clinical diagnostics. We develop computational methods for the identification, functional analysis and prioritization of disease-associated mutations in WES studies of families and parent-child trios. To facilitate the application in clinical diagnostics, we have integrated these methods into a single platform called eDiVA (Exome-seq Disease Variant Analysis), which has already identified causal mutations in several disease studies including hyperkalaemia, ataxia, myasthenia and immunodeficiency in collaboration with clinicians at Hospital Vall d’Hebron.
• We have established a genome wide map of epigenetic markers in mouse cortex samples from 16 individuals subjected to different treatments. We are analysing their methylome (BS-seq), transcriptome (RNA-seq), key histone modifications (ChIP-seq) and chromatin accessibility (ATAC-seq) in order to determine epigenomic patterns related to cognitive function. Resulting maps of cortex specific chromatin states are used to identify regulatory variability leading to changes in gene expression. We further develop experimental and statistical methods that utilize chromatin signatures to identify enhancer regions and their interplay with promoters.
• Staphylococcus aureus, Pseudomonas aeruginosa and Citrobacter freundii are pathogenic bacteria responsible for significant morbidity and mortality in community and health care settings. Using a combination of NGS and Nanopore sequencing based de novo assembly we study the genetic factors influencing pathogenicity of these bacteria, the distribution and evolution of plasmids carrying resistance genes and the flow of outbreaks of antibiotic resistant strains in hospitals.