Even at an early age, I was fascinated by science and, hence, my path led me from the attendance of a high school with special scientific orientation to obtaining a Bachelor’s degree in Biology at the University of Rostock. During my studies, I discovered that I was more interested in data analysis than data generation and after working as a student assistant in the Dept. of Systems Biology and Bioinformatics at the University of Rostock, I decided to study Bioinformatics at the Friedrich-Schiller University Jena.
For my master’s thesis I joined the Max Planck Institute for Chemical Ecology, Jena, Germany, to analyze the transcriptional profiles of diurnally oscillating genes in wild tobacco and to get experience in time series analysis. While my master’s thesis focused on the analysis of transcriptional profiles to answer ecological questions, I switched to the medical field for my PhD by joining the Spanish National Center for Cardiovascular Research, Madrid, Spain, as an early stage researcher, working on the analysis of transcriptome data related to immunology and cardiovascular research in mouse and pig.
The work further manifested my interest in the analysis of transcriptomic data to obtain a deeper understanding of underlying gene regulatory and cell-cell interaction networks. The opportunity to work on large NGS data sets also persuaded me to join the Munich Leukemia Laboratory in 2017, where I’ve been working ever since.
My main research interest is the integration of different data modalities to obtain the most comprehensive picture of the underlying regulatory networks, with a special focus on transcriptome data.
Especially because RNA sequencing (RNA-Seq) has made it possible to broaden the analytical spectrum to study multiple transcriptional events (e.g. chimeric transcripts, isoform switching, expression, etc.) at once and the multifaceted output can greatly benefit clinical diagnostics. I am interested to use partial deconvolution of transcriptional profiles to identify the contribution of different activation programs, to apply network analysis to identify core transcriptional programs and to perform in silico perturbation experiments, and to apply machine learning techniques for the data integration and classification of patients.
Another integral part of the data analysis process I am interested in is the visualization of the obtained results. I like the challenge of finding a way to combine various data types to enhance graphical representation of context-dependent, relevant information. Overall, the main research goal is to get the most out of the available data for the benefit of the patients.
Walter W et al. Next-generation diagnostics for precision oncology: preanalytical considerations, technical challenges, and available technologies. Seminars in Cancer Biology. Academic Press. 2020
Meggendorfer M, Walter W and Haferlach T. WGS and WTS in leukaemia: A tool for diagnostics?. Best Practice & Research Clinical Haematology. 2020
Walter W, Sánchez-Cabo F and Ricote M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics. 2015