Biology subjects

Fritscher, J.

Publications and source records attributed to Fritscher, J..

4 recordsLinked to original sources

bioRxiv2026

Protal: Ultra-fast metagenomic profiling and strain-resolved analysis

Large-scale metagenomic studies increasingly require taxonomic profiles that are sensitive, precise, strain-resolved and computationally tractable. Existing profilers typically trade taxonomic breadth, sensitivity, precision and speed against one another, limiting their utility for high-resolution microbiome analyses. Here we present protal -- profiling through alignment -- an ultra-fast alignment-based method for species-and strain-resolved profiling of metagenomes. Protal combines a newly developed alignment algorithm, machine-learning-based classification and conserved bacterial marker genes to profile species represented in the standardized and regularly updated GTDB taxonomy. Protal reliably profiles all 143,614 bacterial and archaeal species in GTDB r226 and achieved higher precision on CAMI2 benchmarks than all tested contemporary profilers, including MetaPhlAn 4, mOTUs4, sylph and Kraken2+Bracken (mean species-level precision 98.3% versus 97.5% for the next-best profiler, sylph). In custom benchmarks, protal showed particularly strong gains for rare species represented by a single reference genome and for highly complex communities containing 10,000 species (F1-score 9% and 14% higher than the second best profiler, respectively). At the strain level, protal reconstructs intraspecific phylogenetic relationships among detected bacteria with similar accuracy as StrainPhlAn 4; because protal produces precise alignments, the phylogenies can be de novo produced without reliance on reference strain collections. Unlike dedicated strain-profiling workflows, however, protal performs strain analysis concurrently with species-level profiling, making it up to 40-fold faster without requiring additional steps. Together, these features make strain-resolved profiling of thousands of metagenomes feasible on commodity hardware. The software, databases and tutorials are available at https://github.com/4less/protal and http://protal.earlham.ac.uk.

bioinformatics↗

bioRxiv2025

Functional diversity of soil microbial communities increases with ecosystem development

Land abandonment is the single largest process of land-use change in the Global North driving succession and afforestation at continental scales, but assessing its impacts on soil microbial communities remains a challenge. Here, we established a nationwide successional gradient of paired grassland and forest sites to track developments in microbial structure and functioning following land abandonment and gradually changing plant communities. We show that microbes generally respond through threshold dynamics, leading to increasing functional but decreasing taxonomic diversity. Succession also increased the specialization of microbial nutrient (C-N-P) cycling genes while decreasing genetic redundancy, highlighting a putative trade-off between two desirable ecosystem properties: functional diversity and functional redundancy. Increasing fungal functional diversity underpinned higher microbial C-cycling capacity, underscoring the causal link between functional traits and ecosystem processes. Changing litter quality similarly provided a mechanistic link between plant and microbial communities despite otherwise largely decoupled successional developments. Land abandonment is frequently touted as an opportunity to increase biodiversity and carbon storage. Our results show that deeper knowledge about the multifaceted development of soil microbial communities and its links to plant communities during succession may be needed to fully grasp the impacts of global land abandonment processes.

ecology↗

bioRxiv2023

Improved taxonomic annotation of Archaea communities using LotuS2, the Genome Taxonomy Database and RNAseq data

Metabarcoding is increasingly used to uncover diversity and characterise communities of Archaea In various habitats, but taxonomic annotation of their sequences remains more challenging than for bacteria. Fewer reference sequences are available; widely used databases do not reflect recent revisions of higher level archaeal taxonomy and a substantial fraction of their phylogenetic diversity remains to be fully characterised. We address these gaps with a systematic and tractable approach based around the Genome Taxonomy Database (GTDB). GTDB provides a standardized taxonomy with normalized ranks based on protein coding genes, allowing us to identify and remove incongruent SSU sequences. We then use this in combination with the eukaryote PR2 database to annotate a collection of near full length rRNA sequences and the Archaea SSU sequences in SILVA, creating a new reference database, KSGP (Karst, Silva, GTDB and PR2). GTDB SSUs alone provides a small improvement in annotation of an example marine Archaea OTU data set over standardized SSU databases such as SILVA and Greengenes2, while KSGP increases Class and Order assignments by 145% and 280% respectively and is likely to provide some improvement in annotation of bacterial sequences too. We make the KSGP database and a cleaned and deduplicated subset of GTDB SSU sequences available at ksgp.earlham.ac.uk; integrate them into a metabarcoding pipeline, LotuS2 and outline rapid and robust strategies to generate a set of annotated Archaea OTUs and to determine the proportion of Archaea sequences in metatranscriptomic data. We also demonstrate simple tools to visualise the completeness of database coverage and outline strategies to further understand poorly characterised components of the archaeal community which will be equally applicable to bacteria.

microbiology↗

bioRxiv2021

LotuS2: An ultrafast and highly accurate tool for amplicon sequencing analysis

BackgroundAmplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (Less OTU Scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences. ResultsIn LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios. We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines - yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxa composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified genera and species (98% and 57%, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reconstructed 16S sequences. ConclusionLotuS2 is a lightweight and user-friendly pipeline that is fast, precise and streamlined. High data usage rates and reliability enable high-throughput microbiome analysis in minutes. AvailabilityLotuS2 is available from GitHub, conda or via a Galaxy web interface, documented at http://lotus2.earlham.ac.uk/.

bioinformatics↗