Biology subjects

Branders, S.

Publications and source records attributed to Branders, S..

4 recordsLinked to original sources

bioRxiv2025

K-MARVEL: K-Mer based Antimicrobial Resistance Virtual Exploration Lab

The rapid global spread of antimicrobial resistance (AMR) necessitates a new generation of computational tools for its surveillance. While next-generation sequencing offers unprecedented insight into the resistome, current methods face a trade-off: assembly-based approaches are computationally expensive and struggle with complex metagenomes, whereas direct-mapping of long reads is hampered by high error rates that obscure critical resistance-conferring mutations. Here, we present K-MARVEL (K-Mer based Antimicrobial Resistance Virtual Exploration Lab), a novel, open-source method to capture ARGs and resistance-conferring mutations from short and long-read sequencing datasets. It operates in protein k-mer space, providing inherent tolerance to nucleotide-level sequencing errors. On a comprehensive benchmark of 61 long and 49 short-read diverse datasets, K-MARVEL demonstrated superior accuracy, achieving F1-scores of 0.9783 and 0.9754 for short and long-read datasets, respectively. Its implementation in Rust enables high speed through parallelization while guaranteeing memory safety. Computationally, it demonstrated superior performance to conventional assembly-based methods, achieving an average speed up of 7x on short-read datasets and 5x on long-read datasets. In terms of memory footprint, it outperformed the assembly-based approaches for short-read datasets, but its memory footprint was comparable for long-read datasets. Notably, K-MARVEL accurately reconstructs functional genes from genomically fragmented evidence, providing a more comprehensive resistome assessment. In conclusion, K-MARVEL provides a scalable, flexible and memory-efficient solution for AMR surveillance. Its unique capabilities for handling noisy long-read data and complex genomic scenarios make it a powerful tool for researchers and public health scientists. K-MARVEL is open-source and freely available at https://bitbucket.org/amr-avenger/k-marvel under the GPL version 3 license.

bioinformatics↗

bioRxiv2025

Mango: Unearthing Patterns in Large-Scale Biological Data Through Interactive Correlation Analysis

Integrating different types of biological data is often challenging due to the presence of both numerical and categorical data. This complexity makes it harder to evaluate causal biological effects, especially when confounders like population structure, sampling methods, or multi-omics integration can lead to incorrect conclusions. We introduce Mango, an interactive correlation browser designed for visually exploring any tabular data type, using a novel algorithm to correlate numerical and categorical data, regardless of their distribution, called Median-Ranked Label Encoding. Our results on genomic and transcriptomic datasets demonstrate that these correlations can effectively distinguish between biases and causal relationships in large-scale data.

bioinformatics↗

bioRxiv2024

Real-time Taxonomic Characterization of Long-read Mixed-species Sequencing Samples in Sorted Motif Distance Space: Voyager

Recent advances in long-read sequencing technology enable its use in potentially life-saving applications for rapid clinical diagnostics and epidemiological monitoring. To take advantage of these enabling characteristics, we present Voyager, a novel algorithm that complements real-time sequencing by rapidly and efficiently mapping long sequencing reads with insertion- and deletion errors to a large set of reference genomes. The concept of Sorted Motif Distance Space (SMDS), i.e., distances between exact matches of short motifs sorted by rank, represents sequences and sequence complementarity in a highly compressed form and is thus computationally efficient while enabling strain-level discrimination. In addition, Voyager applies a deconvolution algorithm rather than reducing taxonomic resolution if sequences of closely related organisms cannot be discerned by SMDS alone. Using relevant real-world data, we evaluated Voyager against the current best taxonomic classification methods (Kraken 2 and Centrifuge). Voyager was on average more than twice as fast as the current fastest method and obtained on average over 40% higher species level accuracy while maintaining lower memory usage than both other methods.

bioinformatics↗

bioRxiv2024

Detection of pathogens and antimicrobial resistant genes from urine within 5 hours using Nanopore sequencing

PurposeUrinary Tract Infection (UTI) is a prevalent global health concern accounting for 1-3% of primary healthcare visits. The current methods for UTI diagnosis have a high turnaround time of 3-5 days for pathogen identification and susceptibility testing. This work is a proof-of-concept study aimed at determining the detection limit by establishing a culture and amplification-free DNA extraction methodology from spiked urine samples followed by real-time Nanopore sequencing and data analysis. MethodsThis study first establishes an optical density culture-based method for spiking healthy urine samples with the six most prevalent uropathogens. Pathogens were spiked at two clinically significant concentrations of 103 and 105 CFU/ml. Three commercial DNA extraction kits were investigated based on the quantity of isolated DNA, average processing time, elution volume and the average cost incurred per extraction. The outperforming kit was used for direct DNA extraction and subsequent sequencing on MinION and Flongle flowcells. ResultsThe Blood and Tissue kit outperformed the other kits. All pathogens were identified at a concentration of 105 CFU/ml within ten minutes, and the corresponding AMR genes were detected within three hours of the sequencing start. The overall turnaround time including the DNA extraction and sequencing steps was five hours. Moreover, we also demonstrate that the identification of some pathogens and antibiotic-resistance genes was possible at a spike concentration of 103 CFU/mL. ConclusionThis study shows great promise toward reducing the time required for making an informed antibiotic administration from approximately 48 hours to five hours thereby reducing the number of empirical doses and saving lives.

microbiology↗