metagenomics strategies to infer fungal species compositions of complex communities

The kingdom of fungi is crucial for life on earth and is highly diverse. Yet fungi can be challenging to characterize. Fungi can be difficult to culture and be morphologically indistinct in culture. They can have complex genomes of over 1 Gb in size and are still underrepresented in whole genome sequencing database. Overall their description and analysis lack well behind other microbes such as bacteria. At the same time classification of species via high throughput sequencing is increasingly becoming the norm including for pathogen detection, microbiome studies, and environmental monitoring. To date, however, it was still not standardized on the procedure to characterize unknown fungi from sequencing data. In this study, we aimed to assess different metagenomics sequencing and analysis strategies for fungal species identification. Using fungal mock communities of 44 phylogenetically diverse species, we compared species classification and community composition analysis pipelines using shotgun metagenomics and amplicon sequencing data generated from both Illumina short and Nanopore long read sequencing technologies. We show that regardless of the sequencing methodology used, the highest accuracy of species identification was achieved by sequence alignment against fungal specific databases. During the assessment of classification algorithms, we found that applying more stringent cut-offs on the query coverage of each read or contig significantly improved the classification accuracy and community composition analysis without significant data loss. Overall, our study expands the toolkit for identifying fungi by improving sequence-based fungal classification, and provides a practical guide for the design of metagenomics analyses.