Biology subjects

Neuhaus, F.

Publications and source records attributed to Neuhaus, F..

5 recordsLinked to original sources

bioRxiv2026

MetaGEAR Explorer: Rapid interactive searches and cross-cohort analyses of microbiome gene associations in disease

Background: Investigating functional gut microbiome signals remains challenging due to the large scale and sparsity of gene-level metagenomic profiles and reproducibility of microbial gene associations across microbiome studies is limited. Further, interactive tools for phenotype-aware cross-study meta-analysis are currently lacking. Results: We developed MetaGEAR Explorer, a web platform for interactive and programmatic gene-centric analyses that enables users to rapidly search genes of interest across 33 million gene families from 24 metagenomic cohorts spanning inflammatory bowel disease (IBD), colorectal cancer (CRC), and healthy individuals. To demonstrate our platform's capabilities, we first used narG, a well-characterized nitrate reductase gene in Enterobacteriaceae (including Escherichia coli and Klebsiella species), to evaluate the detection of remotely related, disease-relevant homologs. While sequence-based searches using the E. coli narG gene as a query failed to capture the known diversity of narG, MetaGEAR Explorer's domain-based search expansion identified 160 NarG-like gene families with consistent IBD enrichment across cohorts. This included narG homologs from Veillonella parvula and Veillonella atypica that have been recently implicated in intestinal inflammation. We further applied MetaGEAR Explorer to investigate the underexplored diversity of clbS, the self-protection gene against the bacterial genotoxin colibactin, which is implicated in CRC tumorigenesis. The canonical clbS is part of the E. coli clb operon, which produces colibactin. Our analysis showed that E. coli-associated clbS was rare in healthy individuals but increased in disease (1.85% in Healthy, 6.21% in IBD, and 7.62% in CRC). In contrast, domain-based expansion revealed widespread clbS-domain (DUF1706) homologs, present in 95.15% of healthy individuals, while disease-associated contributors shift from commensal Clostridia to Gammaproteobacteria. Notably, Proteus mirabilis was identified as a novel potential ClbS-like carrier enriched in IBD. Furthermore, much of this prevalence was driven by a single unclassified gene family, present in 63.40% of healthy individuals. Conclusions: MetaGEAR Explorer facilitates rapid cross-cohort functional meta-analyses and can identify reproducible, biologically interpretable microbial gene signatures in IBD and CRC, such as newly identified sequence-domain dynamics in nitrate respiration and colibactin self-protection.

bioinformatics↗

bioRxiv2025

Ontology pre-training improves machine learning-based predictions for metabolites

Recent advances in the field of machine learning have shown that integration of expert knowledge improves performances, in particular for complex domains such as biology. Bio-ontologies offer a rich source of curated biological knowledge that can be harnessed to this end. Here, we describe an intuitive and generalisable approach to embed the knowledge contained in a classification hierarchy derived from a bio-ontology into a machine learning model as an intermediate training step between general-purpose pre-training and task-specific fine-tuning in a process that we call ontology pre-training. We show that this approach leads to an improvement in predictive performance and a reduction in training time for a broad range of prediction tasks relevant to understanding metabolite functions in living systems, using a range of datasets derived from MoleculeNet. We see the biggest improvement for regression tasks, e.g. prediction of lipophilicity and aqueous solubility of molecules, and a robust improvement for most classification tasks. Our approach can be adapted for a wide range of knowledge sources, models and prediction tasks.

bioinformatics↗

bioRxiv2025

Detection of alternative splicing: deep sequencing or deep learning?

Alternative splicing (AS) is a central mechanism of gene regulation that enables condition- and tissue-specific expression of gene isoforms. Its dysregulation plays a role in diseases such as cancer, neurological disorders, and metabolic conditions. Despite its importance, accurately identifying AS events remains challenging, especially in large-scale studies relying on publicly available RNA sequencing (RNA-seq) data. State-of-the-art AS event detection typically requires deep sequencing with over 100 million reads; however, much of the publicly accessible data is of lower sequencing depth. Recent advances, particularly deep learning models working with genomic sequences, offer new avenues for predicting AS without reliance on high sequencing depth data. Our study addresses the question: Can we utilize the vast repository of publicly available RNA-seq data for AS detection, despite often lacking the sequencing depth typically required? We show that sequence-based tools such as DeepSplice and SpliceAI show promising performance in retrieving novel and unannotated splice junctions, even when RNA-seq data are limited, but are not suitable for de novo splice junction detection. Our results demonstrate the potential of sequence-based tools for initial hypothesis development and as additional filters in standard RNA-seq pipelines, especially when sequencing depth is limited. Nonetheless, validation with higher sequencing depths remains essential for confirmation of splice events. Overall, our findings underscore the need for integrative methods combining genomic and RNA-seq data for prediction of tissue-/condition-specific AS in resource-limited settings.

bioinformatics↗

bioRxiv2024

Nfib regulates progenitor competence in maturation of GABAergic neurons

Diverse types of GABAergic projection neurons and interneurons of the telencephalon derive from progenitors in a ventral germinal zone, called the ganglionic eminence. Using single-cell transcriptomics, chromatin accessibility profiling, lineage tracing, birthdating, heterochronic transplantation, and perturbation sequencing in mouse embryos, we investigated how progenitor competence influences the maturation and differentiation of these neurons. We found that the progression of neurogenesis over developmental time shapes maturation competence in ganglionic eminence progenitors, influencing how they progress into mature states. In contrast, differentiation competence, which defines the ability to produce diverse transcriptomic identities, remains largely unaffected by the stages of neurogenesis. Chromatin remodeling alongside a NFIB-driven regulatory gene module influences maturation competence in late-born neurons. These findings provide key insights into how transcriptional programs and chromatin accessibility govern neuronal maturation and the diversification of GABAergic neuron subtypes during neurodevelopment.

neuroscience↗

bioRxiv2024

Transcription factor prediction using protein 3D structures

MotivationTranscription factors (TFs) are DNA-binding proteins that regulate gene expression. Traditional methods predict a protein as a TF if the protein contains any DNA-binding domains (DBDs) of known TFs. However, this approach fails to identify a novel TF that does not contain any known DBDs. Recently proposed TF prediction methods do not rely on DBDs. Such methods use features of protein sequences to train a machine learning model, and then use the trained model to predict whether a protein is a TF or not. Because the 3-dimensional (3D) structure of a protein captures more information than its sequence, using 3D protein structures will likely allow for more accurate prediction of novel TFs. ResultsWe propose a deep learning-based TF prediction method (StrucTFactor), which is the first method to utilize 3D secondary structural information of proteins. We compare StrucTFactor with recent state-of-the-art TF prediction methods based on[~] 525 000 proteins across 12 datasets, capturing different aspects of data bias (including sequence redundancy) possibly influencing a methods performance. We find that StrucTFactor significantly (p-value < 0.001) outperforms the existing TF prediction methods, improving the performance over its closest competitor by up to 17% based on Matthews correlation coefficient. AvailabilityData and source code are available at https://github.com/lieboldj/StrucTFactor and on our website at https://apps.cosy.bio/StrucTFactor/ Contactkhalique.newaz@uni-hamburg.de Supplementary informationIncluded

bioinformatics↗