Because many animal species are undescribed, and because the identification of known species is often difficult, interim taxonomic nomenclature has often been used in biodiversity analysis. By assigning individuals to presumptive species, called operational taxonomic units (OTUs), these systems speed investigations into the patterning of biodiversity and enable studies that would otherwise be impossible. Although OTUs have conventionally been separated through their morphological divergence, DNA-based delineations are not only feasible, but have important advantages. OTU designation can be automated, data can be readily archived, and results can be easily compared among investigations. This study exploits these attributes to develop a persistent, species-level taxonomic registry for the animal kingdom based on the analysis of patterns of nucleotide variation in the barcode region of the cytochrome c oxidase I (COI) gene. It begins by examining the correspondence between groups of specimens identified to a species through prior taxonomic work and those inferred from the analysis of COI sequence variation using one new (RESL) and four established (ABGD, CROP, GMYC, jMOTU) algorithms. It subsequently describes the implementation, and structural attributes of the Barcode Index Number (BIN) system. Aside from a pragmatic role in biodiversity assessments, BINs will aid revisionary taxonomy by flagging possible cases of synonymy, and by collating geographical information, descriptive metadata, and images for specimens that are likely to belong to the same species, even if it is undescribed. More than 274,000 BIN web pages are now available, creating a biodiversity resource that is positioned for rapid growth.

Although high-throughput sequencers (HTS) have largely displaced their Sanger counterparts, the short read lengths and high error rates of most platforms constrain their utility for amplicon sequencing. The present study tests the capacity of single molecule, real-time (SMRT) sequencing implemented on the SEQUEL platform to overcome these limitations, employing 658 bp amplicons of the mitochondrial cytochrome c oxidase I gene as a model system. By examining templates from more than 5000 species and 20,000 specimens, the performance of SMRT sequencing was tested with amplicons showing wide variation in GC composition and varied sequence attributes. SMRT and Sanger sequences were very similar, but SMRT sequencing provided more complete coverage, especially for amplicons with homopolymer tracts. Because it can characterize amplicon pools from 10,000 DNA extracts in a single run, the SEQUEL can reduce greatly reduce sequencing costs in comparison to first (Sanger) and second generation platforms (Illumina, Ion). SMRT analysis generates high-fidelity sequences from amplicons with varying GC content and is resilient to homopolymer tracts. Analytical costs are low, substantially less than those for first or second generation sequencers. When implemented on the SEQUEL platform, SMRT analysis enables massive amplicon characterization because each instrument can recover sequences from more than 5 million DNA extracts a year.

The co-authors of this paper hereby state their intention to work together to launch the Genomic Observatories Network (GOs Network) for which this d

Widespread interest in the study of metabarcoding has resulted in data proliferation and the development of a multitude of powerful computational tools. Yet consistent and reproducible interpretation of the data remains challenging. The integration of different data types, software tools, and analytical parameters pose a barrier to scaling research. Further, though the majority of the necessary tools for performing these analyses are already implemented, there is limited support for high throughput analysis due to the requirement for heavy computational capacity. As a result of these complexities, many researchers lack the time, training, or infrastructure to work with larger datasets. mBRAVE, the Multiplex Barcode Research And Visualization Environment, is a cloud-based data storage and analytics platform with standardized pipelines and a sophisticated web interface for transforming raw high-throughput sequencing (HTS) data into biological insights. mBRAVE integrates common analytical methods and links to the Barcode of Life Data (BOLD) System for reference datasets, presenting users with the ability to analyze large volumes of data, without requiring special technical training. mBRAVE's cloud architecture provides centralized and automated storage and compute capacity, thereby reducing the burden on individual researchers. The mBRAVE platform seeks to alleviate the main informatic challenges faced by the metabarcoding research community: the storage and consistent interpretation of HTS data. It is now available for researcher use at www.mbrave.net.