Generation of a global freshwater algal taxonomic database by application of PCR-free rbcL gene detection and machine-learning-based taxonomic classification to public metagenome datasets

Abstract

While it has become increasingly evident that microalgae are critical components of aquatic ecosystems, comprehensive taxonomic analysis of the microalgal component of microbial communities has lagged behind recent innovations in prokaryotic sequencing and bioinformatics. Microalgae are sequenced alongside prokaryotes in metagenomic whole genome shotgun (WGS) sequencing efforts but remain overlooked. In this study, an analytic pipeline for detecting a key algal taxonomic barcode, rbcL, from WGS datasets. This approach allowed for near-perfect detection of rbcL sequences from eight major algal phyla, whereas in silico PCR utilizing popular phyla-specific rbcL primer sets for four algal phyla suggests that such approaches may miss much algal diversity. Further, a machine learning classification model was built and validated to allow for taxonomic classification of rbcL sequences of eight algal phyla to genus or species level with high accuracy and recall values (>90%). Benchmarking of this approach against microscopic algal identifications showed accurate identifications for algae with higher relative abundance values and may offer additional detections of algae not readily identified by microscopy. This pipeline was applied against over 4,000 freshwater WGS metagenomes to generate a global taxonomic occurrence and relative abundance database of freshwater algae composed of over 14,000 accessions. WGS-based algal detection offers broad, unbiased taxon-agnostic algal detection in a single sample analysis. Furthermore, it allows for simultaneous identification of the prokaryotic community, offering new opportunities for studying inter-domain relationships as evidenced by a case-study presented herein. The rbcL taxonomic classifier and metagenome-derived algal detection database are available for download and use at https://doi.org/10.6084/m9.figshare.29996962.