me-seq studies of rare and common, mendelian and complex diseases. Our studies cover various types of diseases including cardio-vascular (e.g. familial hyperkalemic hypertension, varicose veins), neurological (e.g. Parkinson’s disease and congenital myopathies) and rheumatologic diseases (e.g. fibromyalgia) as well as cancer (chronic lymphocytic leukemia). To facilitate the routine application of Exome-seq for diagnosis and improved personalized treatment in hospital environments we have implemented ExomeCRG. This pipeline performs read alignment, SNP and indel prediction, CNV identification, functional annotation of coding variants and adds OMICs information from e.g. dbSNP, 1000genomes and OMIM. To improve data accessibility and to facilitate comparison between studies we develop BioMart and browser based retrieval tools. These incorporate predicted variants together with relevant OMICs data on diseases as well as common and rare variants in the population, focusing on samples from the Iberian Peninsula. Applying our approach we have recently identified KLHL3 as a gene responsible for familial hyperkalemic hypertension (FHHt). A novel damaging missense mutation in a family with three affected members was identified by the analysis of Exome-seq data.
3. Understanding cancer genomes: computational analysis of structural variants and correlated transcriptional and epigenomic variation in leukemia
In this project we will develop novel methods for detection of structural variants (SV), copy number variants (CNV) and movement of transposable elements using Next Generation Sequencing (NGS) data from chronic lymphocytic leukemia tumors (CLL) sequenced as part of the International Cancer Genome Consortium. Further we will combine information across multiple tumor and normal samples from hundreds of CLL patients sequenced using multiple strategies (genome or exome sequencing, RNA-seq) to study driver and passenger mutations of the tumor, mutation heterogeneity as well as mutations influencing metastasis. The group is participating in the International Cancer Genomics Consortium (ICGC) with the aim to fully characterize the genome of chronic lymphocytic leukemia (CLL). The role of our group is to optimize the computational analysis of NGS data in order to obtain high quality micro-indel and structural variant predictions in normal and leukemic cells from patients with CLL. The Spanish contribution to the ICGC is a collaborative effort between several centres in Spain (Clinic Hospital, University of Oviedo and CNIO, among others). We have made substantial progress in implementing a new algorithm to detect small to medium sized indels (1 to 200bp), which are hard to detect using paired-end mapping (PEM) based strategies. We have further set up a standardized analysis pipeline including SNP, microindel, SV and CNV prediction, which is currently applied to whole genome data of 50 patients and Exome data of 120 patients.
4. Determining genetic factors and metagenomic alterations related to increased virulence and multi-antibiotic resistances in S. aureus and P. aeruginosa
In this project we will study genetic factors influencing Staphylococcus aureus and Pseudomonas aeruginosa infections responsible for significant morbidity and mortality in community and health care settings due to increasing frequency of antibiotic resistance. Next generation sequencing technology allows for a detailed analysis of the genes and variants correlated to e.g. pathogenicity and antibiotic resistance. New algorithms are developed for comparison of hundreds of sequenced strains. Further we develop approaches for candidate gene screening using T-DNA directed insertion-site sequencing of S. aureus mutants and metagenomic sequencing of the nares. We have already sequenced 12 strains of S. aureus including pathogenic and non-pathogenic strains as well as 23 strains of P. aeruginosa, which are currently being assembled and compared in order to detect causal variants.
PUBLICATIONS Articles Paschold A, Jia Y, Marcon C, Lund S, Larson NB, Yeh CT, Ossowski S, Lanz C, Nettleton D, Schnable PS, Hochholdinger F. “Complementation contributes to transcriptome complexity in maize (Zea mays L.) hybrids relative to their inbred parents.� Genome Res, doi: 10.1101/gr.138461.112. Epub 2012 Oct 19. (*)
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