Bioinformatics for Biomonitoring: Species Detection and Diversity Estimates Across Next-Generation Sequencing Platforms

Abstract

Abstract As a fast-growing area of technology, sequencing platforms are updated frequently and this rapid technical revolution poses not only great advances but also challenges. To be effective, biomonitoring programmes need to deliver comparable results across research groups and time. Understanding the sources of bias in bioinformatics promotes reliable results that accurately reflect biodiversity. We assembled two mock communities of planktonic organisms to assess the accuracy of species recovery based on sequencing the 18S rRNA V4 region using two NGS platforms, Roche 454 (the platform of choice for early metabarcoding studies), and Illumina MiSeq (employed frequently in recent metabarcoding studies). Our findings suggest that the two platforms have comparable performance on metabarcoding datasets. When singletons (sequences represented by a single read) were excluded from analyses, Illumina MiSeq had a slightly better operational taxonomic unit (OTU) precision score than Roche 454 (calculated as the number of species detected divided by the number of OTUs generated) but only in one bioinformatics workflow (when paired reads were appended, not merged). Roche 454 performed slightly better than Illumina MiSeq in terms of species detection but only when simple mock communities with a single individual per species were analysed. When singleton sequences were included, both platforms detected more than 75% of species with a slightly higher detection achieved by Illumina MiSeq. The OTU clustering of both datasets resulted in a gross overestimation of species richness. This finding suggests that studies employing OTU clustering as a proxy for genetic diversity must carefully perform read processing, such as singleton exclusion, to avoid overestimates. Finally, this study provides insight into technical bioinformatic strategies that should accompany such transitions. In a field such as metabarcoding, where advances in sequencing technology constantly drive the discipline, ensuring the comparability of past and future technologies, and the derived ecological conclusions is important.