Biology subjects

Hauptfeld, E.

Publications and source records attributed to Hauptfeld, E..

4 recordsLinked to original sources

bioRxiv2026

First community challenge for automated virus taxonomy

The rapid rate of virus discovery renders manual curation by taxonomy experts increasingly impractical, creating a need for reliable software that can reproducibly assign viral contigs to taxa at all fifteen ranks of the virus taxonomy. We led an open community challenge for the computational taxonomic classification of viruses and assembled a dataset of virus sequences combining expert-curated and metagenomic sequences. Seventeen teams contributed a total of thirty-four automated, fully reproducible classification pipelines. Most tools correctly assigned viruses belonging to established species, genera, or families, but viruses that are unclassified at those lower ranks remain challenging. This study provides datasets, open-source software, novel approaches, and recommendations to benchmark computational taxonomic classification of viruses, and support organizing the many viruses discovered in big omics data.

microbiology↗

bioRxiv2024

Jaeger: an accurate and fast deep-learning tool to detect bacteriophage sequences

Viruses are integral to every biome on Earth, yet we still need a more comprehensive picture of their identity and global distribution. Global metagenomics sequencing efforts revealed the genomic content of tens of thousands of environmental samples, however identifying the viral sequences in these datasets remains challenging due to their vast genomic diversity. Here, we address identifying bacteriophage sequences in unlabeled sequencing data. In a recent benchmarking paper, we observed that existing deep-learning tools show a high true positive rate, but may also produce many false positives when confronted with divergent sequences. To tackle this challenge, we introduce Jaeger, a novel deep-learning method designed specifically for identifying bacteriophage genome fragments. Extensive benchmarking on the IMG/VR database and real-world metagenomes reveals Jaegers consistent high sensitivity (0.87) and precision (0.92). Applying Jaeger to over 16,000 metagenomic assemblies from the MGnify database yielded over five million putative phage contigs. On average, Jaeger is around 20 times faster than the other state-of-the-art methods. Jaeger is available at https://github.com/MGXlab/Jaeger.

bioinformatics↗

bioRxiv2023

Integration of taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes

AO_SCPLOWBSTRACTC_SCPLOWMetagenomic analysis typically includes read-based taxonomic profiling, assembly, and binning of metagenome-assembled genomes (MAGs). Here we integrate these steps in Read Annotation Tool (RAT), which uses robust taxonomic signals from MAGs and contigs to enhance read annotation. RAT reconstructs taxonomic profiles with high precision and sensitivity, outperforming other state-of-the-art tools. In high-diversity groundwater samples, RAT annotates a large fraction of the metagenomic reads, calling novel taxa at the appropriate, sometimes high taxonomic ranks. Thus, RAT integrative profiling provides an accurate and comprehensive view of the microbiome. CAT/BAT/RAT is available at https://github.com/MGXlab/CAT. The CAT pack now also supports GTDB annotations.

bioinformatics↗

bioRxiv2019

Exploring groundwater microbial communities for natural attenuation potential of micropollutants

Groundwater is a key water resource, with 45.7% of all drinking water globally being extracted from groundwater. Maintaining good groundwater quality is thus crucial to secure drinking water. Micropollutants, such as pesticides, threaten groundwater quality which can be mitigated by biodegradation. Hence, exploring microbial communities in aquifers used for drinking water production is essential for understanding micropollutants biodegradation capacity. This study aimed at understanding the interaction between groundwater geochemistry, pesticide presence, and microbial communities in aquifers used for drinking water production. Two groundwater monitoring wells located in the northeast of The Netherlands and at 500 m distance from each other were sampled in 2014, 2015, 2016 and 2018. In both wells, water was extracted from five discrete depths ranging from 13 to 54 m and used to analyze geochemical parameters, pesticide concentrations and microbial community dynamics using 16S rRNA gene sequencing and qPCR. Groundwater geochemistry was stable throughout the study period and pesticides were heterogeneously distributed at low concentrations (g/L range). Integration of the groundwater chemical and microbial data showed that geochemical parameters and pesticides exerted selective pressure on microbial communities. Furthermore, microbial communities in both wells showed a more similar composition in the deeper part of the aquifer as compared to shallow sections, suggesting vertical differences in hydrological connection. This study provides initial insights into microbial community composition and distribution in groundwater systems in relation to geochemical parameters. This information can contribute for the implementation of bioremediation technologies that guarantee safe drinking water production from clean aquifers. Importance sectionGroundwater is an essential source of drinking water. However, its quality is threathened by the presence of micropollutants. Certain microorganisms are capable of degrading micropollutants. However, groundwater is an unexplored environment, where the biodegradation potential of naturally-present microorganisms is unknown. We thus explore how groundwater microbial ecology in shaped by groundwater composition, namely geochemical parameters and micropollutants. This is a first step towards understanding which microbial communities and environmental conditions support natural attenuation of micropollutants. This study thus provides a first step towards developing in situ bioremediation strategies to remove micropollutants from groundwater used for drinking water production.

microbiology↗