Biology subjects

Steinkamp, R.

Publications and source records attributed to Steinkamp, R..

3 recordsLinked to original sources

bioRxiv2026

FERDINAND: An Open Framework for Download and AI-ready Preprocessing of IMPC 2D Radiographs

The International Mouse Phenotyping Consortium (IMPC) maintains a large archive of whole-body dorsoventral radiographs. However, heterogeneity in file formats, metadata completeness, orientation, and image quality limits its reuse for large-scale quantitative analysis. We present FERDINAND (Framework for Extracting and Restructuring Distributed IMPC radiographs into a Normalised, AI-ready Navigable Dataset), an open framework for programmatic access, harmonisation, quality control, and curation of IMPC radiographs. FERDINAND combines metadata retrieval and local radiograph archiving with centre-aware preprocessing, automated exposure screening, orientation standardisation, and clustering-assisted visual curation. Applied to IMPC Data Release 24, FERDINAND organises 104,819 radiographs acquired between February 2012 and June 2025 across ten phenotyping centres and 6,306 single-gene knockout mouse lines, with semi-automated curation recorded for every image. As an independent validation of the harmonised output, a DeepLabCut-based bone-morphometry pipeline showed that FERDINAND-processed radiographs improve data efficiency for landmark detection: at reduced training-set sizes, they achieve better performance than original radiographs and require fewer manual annotations to reach a given level of quality. FERDINAND provides a reproducible foundation for artificial intelligence (AI)-ready analysis and scalable quantitative phenotyping of heterogeneous IMPC and comparable radiograph resources. The complete FERDINAND source code is openly available on GitHub.

bioinformatics↗

bioRxiv2024

CoCoPyE: feature engineering for learning and prediction of genome quality indices

The exploration of the microbial world has been greatly advanced by the reconstruction of genomes from metagenomic sequence data. However, the rapidly increasing number of metagenome-assembled genomes has also resulted in a wide variation in data quality. It is therefore essential to quantify the achieved completeness and possible contamination of a reconstructed genome before it is used in subsequent analyses. The classical approach for the estimation of quality indices solely relies on a relatively small number of universal single copy genes. Recent tools try to extend the genomic coverage of estimates for an increased accuracy. CoCoPyE is a fast tool based on a novel two-stage feature extraction and transformation scheme. First it identifies genomic markers and then refines the marker-based estimates with a machine learning approach. In our simulation studies, CoCoPyE showed a more accurate prediction of quality indices than the existing tools.

bioinformatics↗

bioRxiv2022

Environmental gradients reveal stress hubs predating plant terrestrialization

Plant terrestrialization brought forth the land plants (embryophytes). Embryophytes account for most of the biomass on land and evolved from streptophyte algae in a singular event. Recent advances have unraveled the first full genomes of the closest algal relatives of land plants; among the first such species was Mesotaenium endlicherianum. Here, we used fine-combed RNAseq in tandem with photophysiological assessment on Mesotaenium exposed to a continuous range of temperature and light cues. Our data establish a grid of 42 different conditions, resulting in 128 transcriptomes and ~1.5 Tbp (~9.9 billion reads) of data to study combinatory effects of stress response using clustering along gradients. We describe major hubs in genetic networks underpinning stress response and acclimation in the molecular physiology of Mesotaenium. Our data suggest that lipid droplet formation, plastid and cell wall-derived signals denominate molecular programs since more than 600 million years of streptophyte evolution--before plants made their first steps on land.

evolutionary biology↗