Wieland Meyer

The StrainDiscoveryDatabase: an open framework for standardized microbial strain data

The paper introduces the StrainDiscoveryDatabase (SDD), a unified microbial strain dataset that resolves data fragmentation and synonymy to support AI-driven bioindustrial applications.

Inferring species compositions of complex fungal communities from long- and short-read sequence data

The paper compares methods for identifying fungal species from sequencing data and finds that aligning sequences to a fungi-specific database with coverage cutoffs improves accuracy.

Linked machine learning classifiers improve species classification of fungi when using error-prone long-reads on extended metabarcodes

This paper demonstrates that linked machine learning classifiers improve species classification of fungi using error-prone long-reads on extended metabarcodes, achieving high recall and low false positives.

CCMetagen: comprehensive and accurate identification of eukaryotes and prokaryotes in metagenomic data

CCMetagen improves metagenomic classification by using weighted read mappings, outperforming existing tools in precision and F1 scores for species-level identification.

Metatranscriptomics as a tool to identify fungal species and subspecies in mixed communities

This study evaluates metatranscriptomics for identifying fungal species and subspecies in mixed communities using a mock community of 17 fungal isolates, showing high accuracy at species and strain levels when low-abundance taxa are excluded.

Population Genomics Of Cryptococcus neoformans var. grubii Reveals New Biogeographic Relationships And Finely Maps Hybridization

This study analyzes the population genomics of Cryptococcus neoformans var. grubii, revealing hybridization, biogeographic relationships, and genetic structure across global isolates.