Biology subjects

Koerber, N.

Publications and source records attributed to Koerber, N..

4 recordsLinked to original sources

bioRxiv2026

A framework for peptide identification on commercial nanopore sequencing platforms

Direct single-molecule peptide analysis could in principle enable rapid and sensitive identification of pathogen-derived or disease-associated biomarkers without reliance on mass spectrometry. However, existing nanopore peptide sensing methods are typically constrained by limited throughput and lack of accessibility beyond specialized setups. Here, we present an integrated experimental-computational framework for DNA-linked peptide translocation on a commercially available, high-throughput nanopore sequencing platform, the MinION. Synthetic peptides were covalently bound to oligonucleotides at both termini. The resulting peptide-DNA constructs were then translocated through the CsgG-CsgF pores using a DNA motor protein. Current traces were segmented using the known DNA sequences to extract peptide-associated signal regions. From these segments, we extracted signal features and trained feature-based and deep-learning classifiers to distinguish peptides, balancing interpretability and classification performance. We establish a framework for peptide identification using standard nanopore sequencing hardware. Across a diverse panel of synthetic peptides, our approach resolves single-amino-acid substitutions, maintains performance across independent sequencing runs, and correctly identifies peptides in blind mixtures. Interpretable model analyses connect classifier decisions and common errors to specific signal motifs. By combining commercially available instrumentation with a reproducible experimental and computational workflow, this framework lowers the barrier to nanopore-based proteomics and enables broader adoption across laboratories. It provides a foundation for future developments in amino acid modification detection and sequence analysis.

bioinformatics↗

bioRxiv2026

Label-free detection of individual virus-infected cells using deep learning

Numerous applications in research and medicine rely on reliable identification and quantification of virus-infected cells. Current methods either apply reporter viruses, that often differ from clinical isolates (e. g. cell tropism, immune evasion) or staining approaches, that prevent live-cell experiments and may introduce biases through manual counting. We present a deep learning model for the label-free identification of virus-infected cells on light microscopy images (VAIruScope). To overcome limitations, our pipeline enables an automated quantification of virus-infected cells based on the recognition of cytopathic effects. The method was applied to different cell models and four clinically relevant prototype viruses representing RNA- (influenza A virus), DNA- (human cytomegalovirus, herpes simplex virus-1) and retroviruses (human immunodeficiency virus-1). VAIruScope identified infected cells achieving classification accuracies of up to 96 %. As proof-of-concept, the method was validated using electron microscopy for a wild-type HSV-1. VAIruScope may be applicable to live-cell imaging to investigate infection dynamics.

bioinformatics↗

bioRxiv2024

End-to-end simulation of nanopore sequencing signals with feed-forward transformers

MotivationNanopore sequencing represents a significant advancement in genomics, enabling direct long-read DNA sequencing at the single-molecule level. Accurate simulation of nanopore sequencing signals from nucleotide sequences is crucial for method development and for complementing experimental data. Most existing approaches rely on predefined statistical models, which may not adequately capture the properties of experimental signal data. Furthermore, these simulators were developed for earlier versions of nanopore chemistry, which limits their applicability and adaptability to the latest flow cell data. ResultsTo enhance the quality of artificial signals, we introduce seq2squiggle, a novel transformer-based, non-autoregressive model designed to generate nanopore sequencing signals from nucleotide sequences. Unlike existing simulators that rely on static k-mer models, our approach learns sequential contextual information from the signal data. We benchmark seq2squiggle against state-of-the-art simulators on real experimental R10.4.1 data, evaluating signal similarity, basecalling accuracy, and variant detection rates. Seq2squiggle consistently outperforms existing tools across multiple datasets, demonstrating superior similarity to real data and offering a robust solution for simulating nanopore sequencing signals with the latest flow cell generation. Availability and Implementationseq2squiggle is freely available on GitHub at: github.com/ZKI-PH-ImageAnalysis/seq2squiggle

bioinformatics↗

bioRxiv2022

MIA: An Open Source Standalone Deep Learning Application for Microscopic Image Analysis

AO_SCPLOWBSTRACTC_SCPLOWIn recent years the amount of data generated by imaging techniques has grown rapidly along with increasing computational power and the development of deep learning algorithms. To address the need for powerful automated image analysis tools for a broad range of applications in the biomedical sciences, we present the Microscopic Image Analyzer (MIA). MIA combines a graphical user interface that obviates the need for programming skills with state-of-the-art deep learning algorithms for segmentation, object detection, and classification. It runs as a standalone, platform-independent application and is compatible with commonly used open source software packages. The software provides a unified interface for easy image labeling, model training and inference. Furthermore the software was evaluated in a public competition and performed among the top three for all tested data sets. The source code is available on https://github.com/MIAnalyzer/MIA.

bioinformatics↗