Biology subjects

Eenjes, T.

Publications and source records attributed to Eenjes, T..

2 recordsLinked to original sources

bioRxiv2021

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

BackgroundThe kingdom fungi is crucial for life on earth and is highly diverse. Yet fungi are challenging to characterize. They can be difficult to culture and may be morphologically indistinct in culture. They can have complex genomes of over 1 Gb in size and are still underrepresented in whole genome sequence databases. Overall their description and analysis lags far behind other microbes such as bacteria. At the same time, classification of species via high throughput sequencing without prior purification is increasingly becoming the norm for pathogen detection, microbiome studies, and environmental monitoring. However, standardized procedures for characterizing unknown fungi from complex sequencing data have not yet been established. ResultsWe compared different metagenomics sequencing and analysis strategies for the identification of fungal species. Using two 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 short and 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 a fungi-specific database. During the assessment of classification algorithms, we found that applying cut-offs to the query coverage of each read or contig significantly improved the classification accuracy and community composition analysis without significant data loss. ConclusionOverall, 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.

bioinformatics↗

bioRxiv2021

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

BackgroundThe increased usage of error-prone long-read sequencing for metabarcoding of fungi has not been matched with adequate public databases and concomitant analysis approaches. We address this gap and present a proof-of-concept study for classifying fungal taxa using linked machine learning classifiers. We demonstrate the capability of linked machine learning classifiers to accurately classify species and strains using real-world and simulated fungal ribosomal DNA datasets, including plant and human pathogens. We benchmark our new approach in comparison to current alignment and k-mer based methods based on synthetic mock communities. We also assess real world applications of species identification in complex unlabelled datasets. ResultsOur machine learning approach assigned individual nanopore long-read amplicon sequences to fungal species with high recall rates and low false positive rates. Importantly, our approach successfully distinguished between closely-related species and strains when individual read errors were higher than the genetic distance between individual taxa, which the alignment and k-mer methods could not do. The machine learning approach showed an ability to identify key species with high recall rates, even in complex samples of unknown species composition. ConclusionsA proof of concept machine learning approach using a tree-descent approach on a decision tree of classifiers can identify known taxa with high accuracy, and precisely detect known target species from complex samples with high recall rates. We propose this approach is suitable for detecting the known knowns of pathogens or invasive species in any environment of mostly unknown composition, including agriculture and wild ecosystems.

bioinformatics↗