Biology subjects

Ulrich, J.-U.

Publications and source records attributed to Ulrich, J.-U..

5 recordsLinked to original sources

bioRxiv2026

Fast and alignment-free flavivirus classification from low-coverage genomes

High genomic variability among viral species makes sequence classification highly dependent on multiple sequence alignment (MSA) methods, which are both computationally intensive and sensitive to data quality issues. To provide a more efficient and robust alternative, we developed DiCNN-UniK, a Dual-Input Convolutional Neural Network (DiCNN) utilizing unique k-mer signatures and universal k-mer libraries to generate novel and direct embeddings. Instead of relying on k-mer frequency patterns, DiCNN-UniK directly leverages k-mer embedding information, which provides a clear picture of local genomic context. This architecture is designed to handle full-length genomic sequences, overcoming the restrictive 512-token limit common in many genomic foundation models. Trained on Flaviviruses, our model shows high sensitivity, robustness, and reliability, achieving an accuracy of 99% on an independent test set. DiCNN-UniK is trained on full-genome data and is able to handle partial genomic sequences without preprocessing, maintaining high accuracy and precision with genomic coverage as low as 20%. DiCNN-UniK currently stands as the best available model for the classification of flaviviruses, offering a sensitive, robust, and reliable solution for sequence analysis under real-world genomic coverage and data quality scenarios.

bioinformatics↗

bioRxiv2024

Machine Learning Reveals Key Glycoprotein Mutations and Rapidly Assigns Lassa Virus Lineages

Lassa fever, caused by the Lassa virus (LASV), remains a major public health concern in West Africa, causing numerous fatalities annually and several intercontinental cases since its discovery in 1969. Despite ongoing research, no approved vaccines are available, with current efforts focusing on immunotherapy. LASV is divided into distinct lineages that circulate in specific geographic regions, elicit varying immune responses, and exhibit different pathophysiological effects. Understanding the genetic differences between these lineages is crucial for developing, improving, and distributing diagnostics, treatments, and vaccines. In this study, we analyzed the LASV glycoprotein complex (GPC), the only surface protein, using statistics, machine learning, and phylogenetics. At a population scale, we identified key amino acid differences between Nigerian lineages and those in other West African regions, particularly near the stable signal peptide cleavage site and other immune-related regions (e.g., AA positions 59-76). Additionally, we found that GPC sequences from Lineages II and III are shorter than those from Lineage IV, due to the high prevalence of a codon insertion at positions 178-180 (amino acid position 60). This insertion may contribute to inaccuracies observed in molecular diagnostics for LASV and may also play a role in the increased fatality associated with Lineage IV. The insertion has reemerged and persisted in Lineage II which may indicate a fitness advantage. Furthermore, we developed a fast and highly accurate lineage classification tool called CLASV that allows rapid identification of LASV lineages, improving the ability to monitor emerging outbreaks and exported cases.

bioinformatics↗

bioRxiv2023

Taxor: Fast and space-efficient taxonomic classification of long reads with hierarchicalinterleaved XOR filters

Metagenomic long-read sequencing is gaining popularity for various applications, including pathogen detection and microbiome studies. To analyze the large data created in those studies, software tools need to taxonomically classify the sequenced molecules and estimate the relative abundances of organisms in the sequenced sample. Due to the exponential growth of reference genome databases, the current taxonomic classification methods have large computational requirements. This issue motivated us to develop a new data structure for fast and memoryefficient querying of long reads. Here we present Taxor as a new tool for long-read metagenomic classification using a hierarchical interleaved XOR filter data structure for indexing and querying large reference genome sets. Taxor implements several k-mer-based approaches such as syncmers for pseudoalignment to classify reads and an Expectation-Maximization algorithm for metagenomic profiling. Our results show that Taxor outperforms competing shortand long-read tools regarding precision, while having a similar recall. Most notably, Taxor reduces the memory requirements and index size by more than 50% and is among the fastest tools regarding query times. This enables real-time metagenomics analysis with large reference databases on a small laptop in the field. Taxor is available at https://gitlab.com/dacs-hpi/taxor.

bioinformatics↗

bioRxiv2022

In-silico enrichment of bacterial plasmids by chromosomal depletion using nanopore adaptive sampling

Bacterial plasmids play a major role in the spread of antibiotic resistance genes. However, their characterization via DNA sequencing suffers from the low abundance of plasmid DNA in those samples. Although sample preparation methods can enrich the proportion of plasmid DNA before sequencing, these methods are expensive and laborious, and they might introduce a bias by enriching only for specific plasmid DNA sequences. Nanopore adaptive sampling could overcome these issues by rejecting uninteresting DNA molecules during the sequencing process. In this study, we assess the application of adaptive sampling for the enrichment of low-abundant plasmids in known bacterial isolates using two different adaptive sampling tools. We show that a significant enrichment can be achieved even on expired flow cells. By applying adaptive sampling, we also improve the quality of de novo plasmid assemblies and reduce the sequencing time. However, our experiments also highlight issues with adaptive sampling if target and non-target sequences span similar regions.

genomics↗

bioRxiv2022

ReadBouncer: Precise and Scalable Adaptive Sampling for Nanopore Sequencing

Nanopore sequencers allow targeted sequencing of interesting nucleotide sequences by rejecting other sequences from individual pores. This feature facilitates the enrichment of low-abundant sequences by depleting overrepresented ones in-silico. Existing tools for adaptive sampling either apply signal alignment, which cannot handle human-sized reference sequences, or apply read mapping in sequence space relying on fast GPU base callers for real-time read rejection. Using nanopore long-read mapping tools is also not optimal when mapping shorter reads as usually analyzed in adaptive sampling applications. Here we present a new approach for nanopore adaptive sampling that combines fast CPU and GPU base calling with read classification based on Interleaved Bloom Filters (IBF). Read-Bouncer improves the potential enrichment of low abundance sequences by its high read classification sensitivity and specificity, outperforming existing tools in the field. It robustly removes even reads belonging to large reference sequences while running on commodity hardware without graphical processing units (GPUs), making adaptive sampling accessible for in-field researchers. Readbouncer also provides a user-friendly interface and installer files for end-users without a bioinformatics background. AvailabilityThe C++ source code of ReadBouncer is available at (https://gitlab.com/dacs-hpi/readbouncer).

bioinformatics↗