Biology subjects

Noack, K.

Publications and source records attributed to Noack, K..

3 recordsLinked to original sources

bioRxiv2025

Metalog: curated and harmonised contextual data for global metagenomics samples

Metagenomic sequencing enables the in-depth study of microbes and their functions in humans, animals and the environment. While sequencing data is deposited in public databases, the associated contextual data is often not complete and needs to be retrieved from primary publications. This lack of access to sample-level metadata like clinical data or in situ observations impedes cross-study comparisons and meta-analyses. We therefore created the Metalog database, a repository of manually curated metadata for metagenomics samples across the globe. It contains 73,082 samples from humans (including 58,506 of the gut microbiome), 10,703 animal samples, 5,146 ocean water samples, and 21,802 samples from other environmental habitats such as soil, sediment, or fresh water. Samples have been consistently annotated for a set of habitat-specific core features, such as demographics, disease status and medication for humans, host species and captivity status for animals, and filter sizes and salinity for marine samples. Additionally, all original metadata is provided in tabular form, simplifying focused studies e.g. into nutrient concentrations. Pre-computed taxonomic profiles facilitate rapid data exploration, while links to the SPIRE database enable genome-based analyses. The database is freely available for browsing and download at https://metalog.embl.de/.

microbiology↗

bioRxiv2024

Prediction of context-specific regulatory programs and pathways using interpretable deep learning

Variational autoencoders (VAEs) are being widely adopted for the analysis of single-cell RNA sequencing (scRNA-seq) data. As with any non-linear models, however, they lack interpretability, which is a crucial aspect in the biomedical field where researchers want to be able to trust their model predictions. Our previously developed OntoVAE model addressed this issue by integrating biological ontologies in the decoder, which made the neuronal activations correspond to pathway activities. However, when multiple covariates are present, disentangling their relative contributions is challenging. To address this limitation, we developed COBRA, a VAE tool that combines the interpretable decoder part of OntoVAE with an adversarial approach that separates covariate effects in the latent space. In this work, we demonstrate the use of COBRA on two different scRNA-seq datasets in different contexts. We applied the tool to an interferon stimulated mouse dataset to separate the effects of celltype and treatment on transcription factors and biological pathways. We furthermore showed how COBRA can be used to predict the state of unseen celltypes.

bioinformatics↗

bioRxiv2023

DOPA residues endow collagen with radical scavenging capacity

Here we uncover collagen, the main structural protein of all connective tissues, as a redox-active material. We identify dihydroxyphenylalanine (DOPA) residues, post-translational oxidation products of tyrosine residues, to be common in collagen derived from different connective tissues. We observe that these DOPA residues endow collagen with substantial radical scavenging capacity. When reducing radicals, DOPA residues work as redox relay: they convert to the quinone and generate hydrogen peroxide. In this dual function, DOPA outcompetes its amino acid precursors and ascorbic acid. Our results establish DOPA residues as redox-active side chains of collagens, probably protecting connective tissues against radicals formed under mechanical stress and/or inflammation.

biophysics↗