Molecular Data Science

While the lab develops tools for various analysis tasks, we are interested in interpreting computational results and to gain biological insights. In collaboration with various experimental groups we work towards a deeper, mechanistic understanding of molecular phenotypes (such as transcriptome changes).

Nonsense Mediated mRNA-Decay as a Transcriptome Regulator

The nonsense-mediated decay (NMD) surveillance pathway can recognize erroneous transcripts and physiological mRNAs, such as precursor mRNA alternative splicing variants. We conducted transcriptome-wide splicing studies using Arabidopsis thaliana mutants in the NMD factor homologs UPF1 and UPF3 as well as wild-type samples treated with the translation inhibitor cycloheximide. Our analyses revealed that at least 17.4% of all multiple-exon, protein-coding genes produce splicing variants that are targeted by NMD. We provide evidence for a major function of alternative splicing-coupled NMD in shaping the Arabidopsis transcriptome, having fundamental implications in gene regulation and quality control of transcript processing.

Mapping between genotype and transcriptome aberrations in cancer

In order to bring data analysis methods towards biological data interpretation, we are currently working on the analysis of data produced by “The Cancer Genome Atlas” (TCGA). Advanced RNA-seq processing tools are being used to quantify transcriptome changes which in conjunction with statistical genetics approaches are examined for their correlation with genetic variations. We are particularly interested in gaining biological insights about splicing mechanisms in cancer as well as technical insights into understanding the effect of heterogeneity and clonal evolution onto association analysis in cancer.

Experimental data analysis

A wide range of experimental data produced by many research groups are produced from complex tools and in large scale, requiring sophisticated tools and models to gain useful results from such analysis. However, interpretation is often not trivial and requires knowledge about assumptions made and limitations of the models used. Within this scope we are applying sophisticated tools and methods towards exciting experimental data and we work in close collaboration with experimental scientists in order to produce new insights about mechanisms involving translation and transcriptome patterns.