Composition of Micro-eukaryotes on the Skin of the Cascades Frog ( Rana cascadae) and Patterns of Correlation between Skin Microbes and Batrachochytrium dendrobatidis

Background Massively parallel pyrosequencing of amplicons from the V6 hypervariable regions of small-subunit (SSU) ribosomal RNA (rRNA) genes is commonly used to assess diversity and richness in bacterial and archaeal populations. Recent advances in pyrosequencing technology provide read lengths of up to 240 nucleotides. Amplicon pyrosequencing can now be applied to longer variable regions of the SSU rRNA gene including the V9 region in eukaryotes. Methodology/Principal Findings We present a protocol for the amplicon pyrosequencing of V9 regions for eukaryotic environmental samples for biodiversity inventories and species richness estimation. The International Census of Marine Microbes (ICoMM) and the Microbial Inventory Research Across Diverse Aquatic Long Term Ecological Research Sites (MIRADA-LTERs) projects are already employing this protocol for tag sequencing of eukaryotic samples in a wide diversity of both marine and freshwater environments. Conclusions/Significance Massively parallel pyrosequencing of eukaryotic V9 hypervariable regions of SSU rRNA genes provides a means of estimating species richness from deeply-sampled populations and for discovering novel species from the environment.

We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to “classic” open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, “classic” open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of “classic” open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by “classic” open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME’s uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME’s OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.

Rapid development of high-throughput (HTS) molecular identification methods has revolutionized our knowledge about taxonomic diversity and ecology of fungi. However, PCR-based methods exhibit multiple technical shortcomings that may bias our understanding of the fungal kingdom. This study was initiated to quantify potential biases in fungal community ecology by comparing the relative performance of amplicon-free shotgun metagenomics and amplicons of nine primer pairs over seven nuclear ribosomal DNA (rDNA) regions often used in metabarcoding analyses. The internal transcribed spacer (ITS) barcodes ITS1 and ITS2 provided greater taxonomic and functional resolution and richness of operational taxonomic units (OTUs) at the 97% similarity threshold compared to barcodes located within the ribosomal small subunit (SSU) and large subunit (LSU) genes. All barcode-primer pair combinations provided consistent results in ranking taxonomic richness and recovering the importance of floristic variables in driving fungal community composition in soils of Papua New Guinea. The choice of forward primer explained up to 2.0% of the variation in OTU-level analysis of the ITS1 and ITS2 barcode data sets. Across the whole data set, barcode-primer pair combination explained 37.6–38.1% of the variation, which surpassed any environmental signal. Overall, the metagenomics data set recovered a similar taxonomic overview, but resulted in much lower fungal rDNA sequencing depth, inability to infer OTUs, and high uncertainty in identification. We recommend the use of ITS2 or the whole ITS region for metabarcoding and we advocate careful choice of primer pairs in consideration of the relative proportion of fungal DNA and expected dominant groups.