Bioinformatic investigation and prediction of gene regulatory elements in pathogenic fungi
Understanding and modeling of gene regulation in fungi requires knowledge about transcription factors (TFs) and transcription factor binding sites (TFBSs). The increasing number of sequenced genomes and the huge amount of gene expression data together with the availability of computational methods allow to predict TFs and TFBSs.
Novel methods for TFBS prediction will be applied and improved by integration the experimental findings stored in databases. A novel tool integrating RNA-Seq data with prior knowledge to reverse engineer regulatory networks will be created. RNA-Seq from fungal data will be exploited to refine the understanding of the organization of regulatory elements. These elements will be studied and modeled in pathogenic fungi. A special emphasis will be made on the regulation of secondary metabolite gene clusters, which are in the research focus of the HKI.
(2016) Draft Genome Sequence of Shewanella sp. Strain P1-14-1, a Bacterial Inducer of Settlement and Morphogenesis in Larvae of the Marine Hydroid Hydractinia echinata. Genome Announc 4(1),
(2015) Genome Sequences of Three Pseudoalteromonas Strains (P1-8, P1-11, and P1-30), Isolated from the Marine Hydroid Hydractinia echinata. Genome Announc 3(6),
(2015) Draft Genome Sequences of Six Pseudoalteromonas Strains, P1-7a, P1-9, P1-13-1a, P1-16-1b, P1-25, and P1-26, Which Induce Larval Settlement and Metamorphosis in Hydractinia echinata. Genome Announc 3(6),
(2015) CASSIS and SMIPS: promoter-based prediction of secondary metabolite gene clusters in eukaryotic genomes. Bioinformatics 32(8), 1138.
(2014) Microevolution of Candida albicans in macrophages restores filamentation in a nonfilamentous mutant. PLoS Genet 10(12), e1004824.
Start of PhD
May 15, 2012
May 12, 2017