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Computational Genomics

Mission & Research lines

Evolutionary Genomics

Mission statement

Research in the Evolutionary Genomics group focuses on the use of comparative genomics and large-scale data analysis to gain an understanding on the evolution of genes and genomes and to predict functional elements.

Main Research Lines

  • Comparative analysis of virus genomes and virus-host systems
  • Computational identification of transcription regulatory elements
  • Evolution of low complexity sequences in proteins and DNA
  • Evolutionary rates in mammalian genomes.

Biomedical Genomics

Mission statement

Research in the Biomedical Genomics group is the computational study of human genetic diseases, including hereditary diseases and cancer, at genomic level.

Main Research Lines

  • Development of computational methods for prediction of disease and cancer genes.
  • Functional & expression patterns of genes and diseases.
  • Disease genes and splicing mutations.
  • Human variation and diseases.
  • Study of cancer in the context of gene regulatory networks.

Computational Genomics

Mission statement

Research in the Computational Genomics group within the Research Programme on Biomedical Informatics focuses on the development of computational methods to study the mechanisms of regulation of gene expression.

Main Research Lines

  • Evolution of Alternative Splicing.
  • Computational analysis of Epigenetic mechanisms of gene expression regulation.
  • Computational prediction of Alternative Splicing
  • Graph algorithms applied to the analysis of genomic data

Functional Genomics

Mission statement

Research in the Functional Genomics group is to contribute to narrow the gap between sequence and function by developing computational tools for a better understanding of biological mechanisms in the context of knowledge of whole genome structure.

Main Research Lines

  • Reverse engineering of biomolecular networks
  • Computational identification of functional binding sites
  • Evolution of biomolecular networks
  • Graphical Markov models


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