Biomedical Informatics

Der Fokus unserer Arbeit liegt auf der Auswertung von Next-Generation Sequenzierungsdaten zur Detektion und weiterführenden Analyse von krankheitsrelevanten DNA-Mutationen und epigenetischen Veränderungen. Unser Ziel ist es, das Verständnis, die Diagnostik und Behandlung von Krebserkrankungen, insbesondere von Leukämien, zu fördern.

Mit Methoden der Informatik und Statistik werten wir dazu Whole-Genome, Whole-Exome und Targeted Sequencing Datensätze, sowie ChIP-, RNA-, 4C- und STARR-Seq-Daten aus.

Publikationen

Evaluating Variant Calling Tools for Non-Matched Next-Generation Sequencing Data
Nature. 2017; doi:10.1038/srep43169

Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models
Bioinformatics. 2014 (ePub ahead of print)
Detection of Significantly Differentially Methylated Regions in Targeted Bisulfite Sequencing Data
Bioinformatics. 2013;29(13):1647-53
RSVSim: an R/Bioconductor package for the simulation of structural variations
Bioinformatics. 2013;29(13):1679-81
DNA methylation changes are a late event in acute promyelocytic leukemia and coincide with loss of transcription factor binding
Blood. 2013;121(1):178-87
Leukemia gene atlas--a public platform for integrative exploration of genome-wide molecular data
PLoS One. 2012;7(6):e39148
Integrative analyses for omics data: a Bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements
Toxicol Environ Health A. 2012;75(8-10):461-70
Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review
Brief Bioinform. 2013;14(1):27-35
Inhibition of the LSD1 (KDM1A) demethylase reactivates the all-trans-retinoic acid differentiation pathway in acute myeloid leukemia
Nat Med. 2012;18(4):605-11
The Interlaboratory RObustness of Next-generation sequencing (IRON) study: a deep sequencing investigation of TET2, CBL and KRAS mutations by an international consortium involving 10 laboratories
Leukemia. 2011;25(12):1840-8
R453Plus1Toolbox: an R/Bioconductor package for analyzing Roche 454 Sequencing data
Bioinformatics. 2011;27(8):1162-3
Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data
BMC Bioinformatics. 2010;11:567
Quantitative comparison of microarray experiments with published leukemia related gene expression signatures
BMC Bioinformatics. 2009;10:422