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.
120
datasets available to search
ShareScore release 0.9.0
Dataset results
120 results for “barcode sequence”
Data from: Rapid and accurate taxonomic classification of insect (Class Insecta) cytochrome c oxidase subunit 1 (COI) DNA barcode sequences using a naïve Bayesian classifier
Open the record for dataset details and reuse information.
Data from: Deep sequencing of mixed total DNA without barcodes allows efficient assembly of highly plastic ascidian mitochondrial genomes
Open the record for dataset details and reuse information.
Data for morphometric analysis and DNA barcode sequence for the new fish species Polymixia hollisterae
Open the record for dataset details and reuse information.
Data from: DNA barcoding meets molecular scatology: short mtDNA sequences for standardized species assignment of carnivore noninvasive samples
Although species assignment of scats is important to study carnivoran biology, there is still no standardized assay for the identification of carnivores worldwide, which would allow large-scale routine assessments and reliable cross-comparison of results. Here we evaluate the potential of two short mtDNA fragments (ATP6 [126 bp] and COI [187 bp]) to serve as standard markers for the Carnivora. Samples of 66 species were sequenced for one or both of these segments. Alignments were complemented with archival sequences, and analyzed with three approaches (tree-based, distance-based and character-based). Intraspecific genetic distances were generally lower than between-species distances, resulting in diagnosable clusters for 86% (ATP6) and and 85% (COI) of the species. Notable exceptions were recently diverged species, most of which could still be identified using diagnostic characters, uniqueness of haplotypes, or by reducing the geographic scope of the comparison. In silico comparative analyses were also performed with a 110-bp cytochrome b (cytb) segment, whose identification success was lower (70%), possibly due to the smaller number of informative sites and/or the influence of misidentified sequences obtained from GenBank. Finally, we performed case-studies with faecal samples, which supported the suitability of our two focal markers for poor-quality DNA, and allowed an assessment of prey-DNA co-amplification. No evidence of prey DNA contamination was found for ATP6, while some cases were observed for COI and subsequently eliminated by the design of more specific primers. Overall, our results indicate that these segments hold good potential as standard markers for accurate species-level identification in the Carnivora.
Data from: DNA barcodes from century-old type specimens using next generation sequencing
Type specimens have high scientific importance because they provide the only certain connection between the application of a Linnean name and a physical specimen. Many other individuals may have been identified as a particular species, but their linkage to the taxon concept is inferential. Because type specimens are often more than a century old and have experienced conditions unfavorable for DNA preservation, success in sequence recovery has been uncertain. The present study addresses this challenge by employing next generation sequencing (NGS) to recover sequences for the barcode region of the cytochrome c oxidase 1 gene from small amounts of template DNA. DNA quality was first screened in more than 1800 century-old type specimens of Lepidoptera by attempting to recover 164bp and 94bp reads via Sanger sequencing. This analysis permitted the assignment of each specimen to one of three DNA quality categories – high (164bp sequence), medium (94bp sequence), or low (no sequence). Ten specimens from each category were subsequently analyzed via a PCR-based NGS protocol requiring very little template DNA. It recovered sequence information from all specimens with average read lengths ranging from 458bp to 610bp for the three DNA categories. By sequencing ten specimens in each NGS run, costs were similar to Sanger analysis. Future increases in the number of specimens processed in each run promise substantial reductions in cost, making it possible to anticipate a future where barcode sequences are available from most type specimens.
Fig. 2 in Application Of Dna Barcoding In Taxonomy And Phylogeny: An Individual Case Of Coi Partial Gene Sequencing From Seven Animal Species
Fig. 2. Phylogenetic position of E. roumanicus, Bayesian inference phylogenetic tree. Sequences obtained by us are written in bold.
Fig. 1. MtCOI sequence comparison for Spodoptera litura and S. exigua. A in DNA barcoding and phylogenetic relationships of Spodoptera litura and S. exigua (Lepidoptera: Noctuidae)
Fig. 1. MtCOI sequence comparison for Spodoptera litura and S. exigua. A color version of this graphic can be seen online in supplementary material for this article in Florida Entomologist 98(1) (March 2015) at http://purl.fcla.edu/fcla/entomologist/browse.
Figure 4 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 4 Comparison of Q. langbianensis complex between NJ tree (left, Clade M3 of Fig. 3) and Bayesian tree (right: Clade 2 of Fig. 2).
Figure 1 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 1 Collection sites in Vietnam and Cambodia in this study, including eight national parks, four nature reserves and two conservation areas.
Figure 7 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 7 Quercus bidoupensis Binh & Ngoc. A Leafy twig B Abaxial side of mature leaf C, D Side view and base view of the cupule, respectively E Inside of cupule F Nut. Materials: A–F from Tagane et al. V4328.
Figure 11 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 11 Quercus donnaiensis A.Camus. A Leafy twig B Infructescence, young fruits and abaxial side of mature leaf C Dried specimen. Materials: A, B from Tagane S., Wai J. V4398 C from Ngoc et al. V3208.
Figure 13 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 13 Quercus langbianensis Hickel & A.Camus. A Leafy twig B Abaxial side of mature leaf C Infructescence and mature fruits D Apex of the nut E Basal scar of the nut F Inside of cupule. Materials: A, B from Tagane et al. V 4165 C–F from Tagane et al. V4166.
Figure 10 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 10 Quercus camusiae Trel. ex Hickel & A.Camus. A Branch with young fruit, B. Infructescence and young fruits C, D Abaxial side of young and mature leaf, E. Dried specimen. Materials: A–D from Tagane et al. V342 E from Toyama et al. V2173.
Figure 12 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 12 Quercus honbaensis Binh, Tagane & Yahara. A Leafy twig B Infructescence and mature fruits, C, D Side view and base view of the cupule, respectively, E. Inside of cupule, F. Nut. Materials: A–F from Toyama et al. V1378.
Figure 6 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 6 Quercus baolamensis Binh & Ngoc. A Leafy twig B Abaxial side of mature leaf C Mature fruit D Inside of cupule E Nut. Materials: A–E from Ngoc et al. V3191.
Figure 2 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 2 Bayesian phylogeny of 29 samples of Quercus and one Trigonobalanus (outgroup) based on rbcL, matK and ITS sequences. Braches are labelled with posterior probabilities.
Figure 3 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 3 NJ tree of 31 samples of Quercus and one Trigonobalanus (outgroup) based on presence/absence data of 16,809 MIG-seq loci. Branches are labelled with bootstrap supports (% of 1000 replicates).
Figure 9 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 9 Quercus cambodiensis Hickel & A.Camus. A Leafy twig B Abaxial side of mature leaf C Infructescence and fruits D Nut E Basal scar of the nut. Materials: A–E from Tagane et al. 4302.
Figure 8 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 8 Quercus blaoensis A.Camus A. Branch with fruits B Young fruit C Dried specimen Materials: A–C from Toyama et al. V1366.
Figure 5 from: Binh HT, Ngoc NV, Tagane S, Toyama H, Mase K, Mitsuyuki C, Strijk JS, Suyama Y, Yahara T (2018) A taxonomic study of Quercus langbianensis complex based on morphology, and DNA barcodes of classic and next generation sequences. PhytoKeys 95: 37-70. https://doi.org/10.3897/phytokeys.95.21126
Figure 5 Quercus baniensis A.Camus. A Leafy twig B Abaxial side of mature leaf C Infructescence and young fruits D Dried specimen. Materials: A, B from Hoang T.S. & Tagane S. V6922 C, D from Tagane et al. V3089.
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.