Bioinformatics and Genomics

Genomic and Epigenomic Variation in Disease

Group Structure

Stephan Ossowski
Oliver Drechsel, Charlotte Hor
Luis Zapata, Shalu Jhanwar, Rubayte Rahman, Sergio Espeso-Gil, Hana Susak (since October 2013)
Daniela Bezdan


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 disease 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 to reveal 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 hundred cases of chronic lymphocytic leukaemia we identified novel candidate cancer drivers.
  • Novel or inherited genetic variations can lead to rare and common diseases. Exome-sequencing 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 Exome-seq studies of rare and common diseases. To facilitate their 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.
  • 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 variants leading to changes in gene expression. Our work pilots studies in human neurodegenerative disorders.
  • Staphylococcus aureus and Pseudomonas aeruginosa are pathogenic bacteria responsible for significant morbidity and mortality in community and health care settings. Using NGS based de novo assembly we study the genetic factors influencing S. aureus and P. aeruginosa pathogenicity as well as the distribution and evolution of plasmids carrying resistance genes.

Selected Publications

  1. Bassaganyas L, Beà S, Escaramís G, Tornador C, Salaverria I, Zapata L, et al.
    “Sporadic and reversible chromothripsis in chronic lymphocytic leukemia revealed by longitudinal genomic analysis.”
    Leukemia, 27(12):2376-2379 (2013).
  2. Trujillano D, Ramos MD, González J, Tornador C, Sotillo F, Escaramis G, et al.
    “Next generation diagnostics of cystic fibrosis and CFTR-related disorders by targeted multiplex high-coverage resequencing of CFTR.”
    J Med Genet, 50(7):455-62 (2013).
  3. Trujillano D, Perez B, González J, Tornador C, Navarrete R, Escaramis G, et al.
    “Accurate molecular diagnosis of phenylketonuria and tetrahydrobiopterin-deficient hyperphenylalaninemias using high-throughput targeted sequencing.”
    Eur J Hum Genet 2013, Aug 14 2013, doi: 10.1038/ejhg.2013.175
  4. Koenig D, Jiménez-Gómez JM, Kimura S, Fulop D, Chitwood DH, Headland LR, et al.
    “Comparative transcriptomics reveals patterns of selection in domesticated and wild tomato.”
    Proc Natl Acad Sci U S A, 110(28):E2655-62 (2013).
  5. Wijnker E, Velikkakam James G, Ding J, Becker F, Klasen JR, Rawat V, et al.
    “The genomic landscape of meiotic crossovers and gene conversions in Arabidopsis thaliana.”
    Elife, 2(0):e01426 (2013).