Vivian Brandenburg

I’m a bioinformatician passionate about uncovering hidden patterns in complex biological data and translating them into actionable insights for experimental research. I am currently working at Resolve Biosciences, contributing to the development of spatial transcriptomics technologies. I program prototype instruments and apply computational pipelines to improve workflows and ensure reproducibility.

In my previous work at the Bioinformatics Group of the Ruhr University Bochum, I designed and implemented deep learning models to extract phylogenetic insights. There, I also developed methods integrating thermodynamics models with deep learning to predict RNA secondary structures. Before that, I was part of the Microbial Biology Group in Bochum, where I developed the computational pipeline for Lead-Seq, combining high-throughput sequencing data analysis with bioinformatic workflows to probe RNA structures at cellular scales. Leveraging my expertise in RNA sequencing, I contributed to interdisciplinary projects investigating regulatory networks controlled by small RNAs, combining computational analysis with biological interpretation.

selected publications

A quartet-based approach for inferring phylogenetically informative features from genomic and phenomic data

Brandenburg, Hack, Mosig

Computational and Structural Biotechnology Journal Aug 27 2025

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Neural networks are widely used in bioinformatics to extract features from morphological, structural, and sequence data of different taxa. A key question is whether such features are compatible with a known phylogenetic tree describing the evolutionary relationships among the taxa. We address this question with a machine learning approach that takes taxon-specific data and a reference tree as input, and trains a neural network to produce a latent feature space whose pairwise distances are consistent with the tree topology. Our approach builds on the established role of quartets in distance-based phylogeny, leading to a quartet-based loss function for neural network training. In a proof-of-concept study using bacterial ribosomal RNA sequences, we show that the learned feature distances closely match the reference phylogeny. This framework can be applied to diverse biological data types, providing a principled way to incorporate phylogenetic constraints into neural network-based feature extraction.
Inverse folding based pre-training for the reliable identification of intrinsic transcription terminators

Brandenburg, Narberhaus, Mosig

PLOS Computational Biology 18 (7) 2022

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It is well-established that neural networks can predict or identify structural motifs of non-coding RNAs (ncRNAs). Yet, the neural network based identification of RNA structural motifs is limited by the availability of training data that are often insufficient for learning features of specific ncRNA families or structural motifs. Aiming to reliably identify intrinsic transcription terminators in bacteria, we introduce a novel pre-training approach that uses inverse folding to generate training data for predicting or identifying a specific family or structural motif of ncRNA. We assess the ability of neural networks to identify secondary structure by systematic in silico mutagenesis experiments. In a study to identify intrinsic transcription terminators as functionally well-understood RNA structural motifs, our inverse folding based pre-training approach significantly boosts the performance of neural network topologies, which outperform previous approaches to identify intrinsic transcription terminators. Inverse-folding based pre-training provides a simple, yet highly effective way to integrate the well-established thermodynamic energy model into deep neural networks for identifying ncRNA families or motifs. The pre-training technique is broadly applicable to a range of network topologies as well as different types of ncRNA families and motifs.
Lead-seq: transcriptome-wide structure probing in vivo using lead(II) ions

Twittenhoff*, Brandenburg*, Righetti*, Nuss, Mosig, Dersch, Narberhaus

Nucleic Acids Research 48 (12) 2020

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The dynamic conformation of RNA molecules within living cells is key to their function. Recent advances in probing the RNA structurome in vivo, including the use of SHAPE (Selective 2′-Hydroxyl Acylation analyzed by Primer Extension) or kethoxal reagents or DMS (dimethyl sulfate), provided unprecedented insights into the architecture of RNA molecules in the living cell. Here, we report the establishment of lead probing in a global RNA structuromics approach. In order to elucidate the transcriptome-wide RNA landscape in the enteric pathogen Yersinia pseudotuberculosis, we combined lead(II) acetate-mediated cleavage of single-stranded RNA regions with high-throughput sequencing. This new approach, termed ‘Lead-seq’, provides structural information independent of base identity. We show that the method recapitulates secondary structures of tRNAs, RNase P RNA, tmRNA, 16S rRNA and the rpsT 5′-untranslated region, and that it reveals global structural features of mRNAs. The application of Lead-seq to Y. pseudotuberculosis cells grown at two different temperatures unveiled the first temperature-responsive in vivo RNA structurome of a bacterial pathogen. The translation of candidate genes derived from this approach was confirmed to be temperature regulated. Overall, this study establishes Lead-seq as complementary approach to interrogate intracellular RNA structures on a global scale.

*shared first authorship