Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4
datasets available to search
ShareScore release 0.9.0
Dataset results
4 results for “molecular operational taxonomic unit”
Optimal sequence similarity thresholds for clustering of molecular operational taxonomic units in DNA metabarcoding studies
<p><span>Clustering approaches are pivotal to handle the many sequence variants obtained in DNA metabarcoding datasets, therefore they have become a key step of metabarcoding analysis pipelines. Clustering often relies on a sequence similarity threshold to gather sequences in Molecular Operational Taxonomic Units (MOTUs), each of which ideally representing a homogeneous taxonomic entity, e.g. a species or a genus. However, the choice of the clustering threshold is rarely justified, and its impact on MOTU over-splitting or over-merging even less tested. Here, we evaluated clustering threshold values for several metabarcoding markers under different criteria: limitation of MOTU over-merging, limitation of MOTU over-splitting, and trade-off between over-merging and over-splitting. We extracted sequences from a public database for nine markers, ranging from generalist markers targeting Bacteria or Eukaryota, to more specific markers targeting a class or a subclass (e.g. Insecta, Oligochaeta). Based on the distributions of pairwise sequence similarities within species and within genera, and on the rates of over-splitting and over-merging across different clustering thresholds, we were able to propose threshold values minimizing the risk of over-splitting, that of over-merging, or offering a trade-off between the two risks. For generalist markers, high similarity thresholds (0.96-0.99) are generally appropriate, while more specific markers require lower values (0.85-0.96). These results do not support the use of a fixed clustering threshold. Instead, we advocate a careful examination of the most appropriate threshold based on the research objectives, the potential costs of over-splitting and over-merging, and the features of the studied markers.</span></p>
Optimal sequence similarity thresholds for clustering of molecular operational taxonomic units in DNA metabarcoding studies
Open the record for dataset details and reuse information.
Data from: Molecular operational taxonomic units reveal restricted geographic ranges and regional endemism in the Indo‐Pacific octocoral family Xeniidae
Aim: To quantify taxon diversity, biogeographic distributions and patterns of community assembly in xeniid octocorals using molecular operational taxonomic units (MOTUs). Location Red Sea, Indian and western Pacific Oceans. Taxon Xeniidae, a family of reef-dwelling octocorals (Anthozoa, Octocorallia). Methods Xeniids collected at 13 locations were sequenced at three barcode loci, and assigned to molecular operational taxonomic units (MOTUs) defined by minimum genetic distance thresholds. Taxon richness (no. of MOTUs) and endemicity (percent of MOTUs found at a single location) were quantified. Patterns of β-diversity (species turnover) and phylogenetic β-diversity (lineage turnover) among geographical regions were visualized using hierarchical clustering, NMDS plots, and distance-decay relationships. Community assembly was investigated by comparing the mean pairwise distance (MPD) and mean nearest taxon distance (MNTD) separating species in each assemblage to values generated for null communities. Results A genetic distance threshold of 0.3% discriminated 67 MOTUs, with taxon richness ranging from 2-18 MOTUs per site. Out of the 67 MOTUs, 48 (72%) were found at only a single location, and only two spanned both the western Indian and Pacific Oceans. Species turnover among sites was high, but phylogenetic β-diversity was lower than β-diversity and differed significantly from null models of community assembly at only two sites. β-diversity and phylogenetic β-diversity both increased significantly with geographic distance, and sites clustered into three distinct biogeographic regions (Red Sea and western Indian Ocean; Western Australia; western Pacific and Great Barrier Reef, Australia). All five major clades of xeniids were represented in each region. Main Conclusions A genetic approach to biodiversity estimation suggests that most xeniid taxa are regional endemics whose geographic distribution is likely governed by dispersal limitation. This conclusion contrasts with published records of certain morphospecies occurrences, which imply that they have broad geographic ranges. So far, the distribution of xeniid biodiversity mirrors that of scleractinian corals, with species richness highest in the Coral Triangle, but endemicity peaking in peripheral areas.
Data from: Molecular operational taxonomic units reveal restricted geographic ranges and regional endemism in the Indo‐Pacific octocoral family Xeniidae
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.