López-Bigas, Núria
ICREA Research Professor at Institut de Recerca Biomèdica (IRB Barcelona).
Life & Medical Sciences
Short biography
Núria López-Bigas has a PhD in Biology from the University of Barcelona and has expertise in Medical Genetics and in Computational Biology and Bioinformatics. During her PhD work, she studied the molecular causes of hereditary deafness at the group of Xavier Estivill. Next she moved to the European Bioinformatics Institute in Hinxton (Cambridge, UK) to work on Computational Genomics at the group of Christos A. Ouzounis and then at the Center for Regulatory Genomics (Barcelona) at the group of Roderic Guigó. Núria joined the Pompeu Fabra University in April 2006 with a Ramón y Cajal Position, was appointed ICREA Research Professor in October 2011 and her lab moved to Institute for Research in Biomedicine in November 2016. She leads the Biomedical Genomics Research Group (http://bbglab.irbbarcelona.org).
Research interests
Núria López-Bigas research is focused on the study of cancer from a genomics perspective. She is particularly interested in the identification of cancer driver mutations, genes and pathways across tumor types and in understanding the mutational processes leading to accumulation of mutations in tumors. Among the most important achievements obtained by Lopez-Bigas' lab are the development of pioneer methods to identify driver genes (Oncodrive methods), the creation a compendium of cancer genes across cancer types: IntOGen (http://www.intogen.org), the discovery that protein-bound DNA impairs nucleotide excision repair (Radhakrishnan et al., 2016), the finding that exons have reduced mutation rate due to differential mismatch repair (Frigola et al., 2017), the discovery that nucleosome covered DNA shows a 10 bp periodicity on the rate of somatic and germline mutations (Pich et al., 2018), the identification of the mutational footprints of cancer therapies (Pich et al., 2019), the study of the evolution of relapse of adult T cell acute lymphoblastic leukemia (Sentis et al., 2020), and the development of machine learning models to identify driver mutations in cancer genes (Muiños, Martinez-Jimenez et al 2021).