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79 results for “genetic barcoding”
Datasets for phylogenetic analyses and phylogenetic trees for: Genetic barcodes for species identification and phylogenetic estimation in ghost spiders (Araneae: Anyphaenidae: Amaurobioidinae). Invertebrate Systematics, 2024
<p>We combined the COI sequence data with legacy multigene sequence data to create a new, taxon-rich phylogeny for the Amaurobioidinae. We used sequences for four loci that have been used in previous studies on the subfamily: two mitochondrial loci, COI (658bp) and ribosomal subunit 16S (16S, 410bp); and two nuclear loci, Histone H3 (H3, 327bp) and ribosomal subunit 28S (28S, 839bp). We complemented the Amaurobioidinae data with sequences from several non-amaurobioidine anyphaenids and two clubionids as outgroups. Sequence alignment was performed using the MAFFT (ver. 7.308) plugin in Geneious, allowing MAFFT to automatically select an appropriate alignment strategy based on the properties of each locus, or with the online MAFFT server (https://mafft.cbrc.jp), which consistently selected the L-INS-i algorithm. Finally, alignments of the four loci were concatenated to construct a 2234 bp multigene sequence matrix containing 692 taxa, with about 55% missing/gap data (“full” matrix henceforth). To ensure that excessive missing data did not affect the resulting topology, we also constructed a reduced matrix by removing additional COI-only specimens so that each species and morphotype was represented by just one or two specimens for which all loci were available (where possible). After realignment, this reduced matrix was 2235 bp long, included 167 taxa, and had about 22% missing/gap data (“reduced” matrix henceforth). Phylogenetic analyses under maximum likelihood, including model selection, were then conducted with IQ-TREE 2. We performed phylogenetic analyses on both concatenated matrices (the full matrix and the reduced matrix) and on each individual locus. For model selection, we provided an initial scheme that partitioned the matrix by locus, and further partitioned the protein-coding loci (COI and H3) by codon position. We used ModelFinder and searched for the best partition scheme, all in IQ-TREE. The best models (partitions) for the full dataset were: GTR+F+I+G4 (16S), GTR+F+I+I+R4 (28S), TVM+F+I+I+R2 (COI-1), TIM2+F+R4 (COI-2), GTR+F+R5 (COI-3), TVMe+G4 (H3-1-H3-2), SYM+G4 (H3-3); and for the reduced dataset: GTR+F+I+G4 (16S), GTR+F+I+G4: (28S), GTR+F+I+G4: (COI-2), GTR+F+I+G4: (COI-3), TVM+F+I+G4: (COI-1, H3-2), GTR+F+I+G4: (H3-1), GTR+F+I+G4: (H3-3). For each dataset, once the best models and partitions were defined, we executed 10 independent replicates of tree calculations followed by 1000 ultrafast bootstrap replicates, and the replicate reaching the maximum likelihood was chosen. Phylogenetic analyses under parsimony were made with TNT, under equal weights, using the “new technology” search with default values, asking for 10 independent hits to the minimal length, and submitting the resulting trees to a round of TBR branch swapping. </p>
Raw data used for COI delineation of the Eupolybothrus species: Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar from: Eupolybothrus cavernicolus Komerički & Stoev sp. n. (Chilopoda: Lithobiomorpha: Lithobiidae): the first eukaryotic species description combining transcriptomic, DNA barcoding and micro-CT imaging data - Biodiversity Data Journal 1: e1013 (28 October 2013) https://doi.org/10.3897/BDJ.1.e1013
<p>Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar</p>
Fig. 3 in Chloroplast genome of the conserved Aster altaicus var. uchiyamae B2015-0044 as genetic barcode
Fig. 3. The variable sites in the chloroplast genomes of Aster altaicus var. uchiyamae. Variable sequences are marked in red. GG: Yeoju, Gyeonggi Province, CB: Cheongju, Chungcheongbuk Province.
Fig. 2 in Chloroplast genome of the conserved Aster altaicus var. uchiyamae B2015-0044 as genetic barcode
Fig. 2. The sequence alignment of variable sites in the chloroplast genomes of Aster altaicus var. uchiyamae. Variable sequences are marked in red. GG: Yeoju, Gyeonggi Province, CB: Cheongju, Chungcheongbuk Province.
Fig. 2 in Chloroplast genome of white wild chrysanthemum, Dendranthema sp. K247003, as genetic barcode
Fig. 2. Comparison of chloroplast genomes of Dendranthema sp. K247003 and D. boreale IT121002 using mVISTA program. Grey arrows and thick black lines above the alignment indicate genes with their orientation and the position of the IRs, respectively. The Yscale represents the percent identity between 50-100%. Genome regions are color-coded: Coding regions in blue; noncoding sequences (CNS) in red.
Fig. 1 in Chloroplast genome of the conserved Aster altaicus var. uchiyamae B2015-0044 as genetic barcode
Fig. 1. Plastid genomic map of Aster altaicus var. uchiyamae.
Fig. 1 in Chloroplast genome of white wild chrysanthemum, Dendranthema sp. K247003, as genetic barcode
Fig. 1. Plastid genomic map of Dendranthema sp. K247003.
Obuasi case study data: Performance of neutral SNP barcodes to determine genetic diversity and structure of Plasmodium falciparum in Africa
<p>A small number of informative biallelic single nucleotide polymorphisms (SNPs) have been proposed to be an economical method to fast-track the genotyping and relatedness analysis of <em>Plasmodium</em> <em>falciparum</em> in malaria-endemic areas. Whilst used successfully in low-transmission areas where infections are monoclonal and highly related, we present the first study to evaluate the performance of these 24- and 96-SNP molecular barcodes in African countries characterised by moderate-to-high transmission. Using haplotypes generated from the MalariaGEN <em>P. falciparum</em> Community Project version 6 database, 52.3% of infections were multiclonal, generating high frequencies of mixed-allele calls (MACs) per isolate. Both multiclonality and low heterozygosity of SNPs impeded haplotype construction for analyses of relatedness. Although fewer SNPs provided usable data, these SNP barcodes weakly identified genetic differentiation across large geographic distances. However, both minor and major alleles' frequencies were temporally unstable. We conclude that these standardised SNP barcodes are vulnerable to ascertainment bias. While large numbers of SNPs acquired by whole-genome sequencing and computational methods to construct haplotypes present a way forward, these approaches may not be practical or cost-effective for surveillance on large scales in malaria-endemic areas. </p>
Reintroducing Akanthomyces ampullifer: providing genetic barcodes, culture, and updated description for the dipteran pathogen rediscovered in Germany
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Leaf beetle community data for 20 Iberian localities and associated genetic barcodes (cox1)
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Obuasi case study data: Performance of neutral SNP barcodes to determine genetic diversity and structure of Plasmodium falciparum in Africa
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Figure 3 in DNA Barcodes Reveal High Genetic Diversity in Philippine Fruit Bats
Figure 3. COI ML tree for Pteropodidae dataset using GTR+G+I model of substitution. Bootstrap supports 50% or greater are shown in the nodes. Clades with more than one sequence have been compressed. Labels indicate scientific name and number of sequences for that taxon in the clade. Scientific names in bold text indicate known Philippine endemic species. Colored shapes indicate the geographic origin of the sequences from Genbank and BOLD: purple – Southeast Asia, red – East Asia, blue – South Asia, white – Middle East, yellow – Africa, and black – Oceania. Red lines indicate sequences generated by this study. Scale indicates five nucleotide substitutions per 100 nucleotides.
Figure 2 in DNA Barcodes Reveal High Genetic Diversity in Philippine Fruit Bats
Figure 2. COI NJ tree for Pteropodidae dataset using K2P model of substitution. Bootstrap supports 50% or greater are shown in the nodes. Clades with more than one sequence have been compressed. Labels indicate scientific name and number of sequences for that taxon in the clade. Red lines indicate Philippine pteropodid sequences generated in this study. Scientific names in bold text indicate known Philippine endemic species. Colored shapes indicate the geographic origin of the sequences from Genbank and BOLD: purple – Southeast Asia, red – East Asia, blue – South Asia, white – Middle East, yellow – Africa, and black – Oceania. Scale indicates two nucleotide substitutions per 100 nucleotides.
Evaluating the genetic variation of the COI gene of Insecta: Implications for DNA barcoding, metabarcoding and species delimitation studies
<p>The genetic variation of the COI gene has a great effect on the final results of the species delimitation studies. However, little research has comprehensively investigated the genetic divergence in COI among Insecta. The fast-growing COI data in BOLD provide an opportunity for comprehensively appraising the genetic variation in COI among Insecta. We calculated the K2P distance of 64,414 insect species downloaded from BOLD. The match ratios of the clustering analysis based on different thresholds were compared among 4,288 genera (35,068 species). Besides, we also compared the match ratios obtained from two species delimitation methods: the clustering analysis (distance-based method) and the bPTP analysis (tree-based method). Furthermore, the effectiveness of two different results of the bPTP analysis: bPTP_h and bPTP_ml was also tested. Approximately one-quarter of the species of Insecta showed high intraspecific genetic variation (> 3%), and a conservative estimate of this value is 12.05-22.58%. The application of empirical thresholds (e.g., 2% and 3%) in the clustering analysis may result in the overestimation of species diversity. In metabarcoding studies, a threshold of 3% can only be used to estimate the insect diversity roughly. As for the clustering analysis, the "threshOpt" or "localMinima" algorithms can provide a priori value for the researcher. Nevertheless, if the minimum interspecific genetic distance of congeneric species was greater than or equal to 2%, it is possible to avoid overestimating the species diversity based on the empirical thresholds. Besides, the match ratios of the bPTP_ml results were higher than those of the bPTP_h results. As for the bPTP analysis, the bPTP_ml results were recommended. If a proper threshold was selected, the clustering analysis may outperform the bPTP analysis.</p>
FIGURE S1. Automatic Barcode Gap Discovery genetic distances calculated for 18 Asteronotus Ehrenberg, 1831 in A tale of two genera: the revival of Hoplodoris (Nudibranchia: Discodorididae) with the description of new species of Hoplodoris and Asteronotus
FIGURE S1. Automatic Barcode Gap Discovery genetic distances calculated for 18 Asteronotus Ehrenberg, 1831 and Hoplodoris Bergh, 1880 COI sequences using three genetic distance calculations: (A) Jukes-Cantor (JC69); (B) Kimura (K80); (C) Simple Distance. Presumed intraspecific variation is shown in white, interspecific variation in grey.
Genetic barcoding of museum eggshell improves data integrity of avian biological collections
<p>Natural history collections are often plagued by missing or inaccurate metadata for collection items, particularly for specimens that are difficult to verify or rare. Avian eggshell in particular can be challenging to identify due to extensive morphological ambiguity among taxa. Species identifications can be improved using DNA extracted from museum eggshell; however, the suitability of current methods for use on small museum eggshell specimens has not been rigorously tested, hindering uptake. In this study, we compare three sampling methodologies to genetically identify 45 data-poor eggshell specimens, including a putatively extinct bird's egg. Using an optimised drilling technique to retrieve eggshell powder, we demonstrate that sufficient DNA for molecular identification can be obtained from even the tiniest eggshells without significant alteration to the specimen's appearance or integrity. This method proved superior to swabbing the external surface or sampling the interior; however, we also show that these methods can be viable alternatives. We then applied our drilling method to confirm that a purported clutch of Paradise Parrot eggs collected 40 years after the species' accepted extinction date were falsely identified, laying to rest a 53-year-old ornithological controversy. Thus, even the smallest museum eggshells can offer new insights into old questions.</p>
Genetic barcoding systematically comparing haploinsufficiency of key genes in del(5q) MDS reveals a central role for CSNK1A1 in clonal expansion
<p>R object with pre-processed single cell data of the manuscript "<strong>Genetic barcoding systematically comparing haploinsufficiency of key genes in del(5q) MDS reveals a central role for </strong><em>CSNK1A1</em><strong> in clonal expansion"</strong></p>
FIGURE 11 in New Thai giant pill-millipede species, with new genetic barcoding data (Diplopoda Sphaerotheriida, Zephroniidae)
FIGURE 11. Zephronia chrysomallos Bhansali & Wesener sp. nov., holotype ♂ (NHMD MYR8826), drawings, A. Left posterior telopod, anterior view. B. Left posterior telopod, posterior view. Abbreviations: ct = crenulated teeth; imf = immovable finger; ss = sclerotized spot. Scale bars = 1 mm.
FIGURE 12 in New Thai giant pill-millipede species, with new genetic barcoding data (Diplopoda Sphaerotheriida, Zephroniidae)
FIGURE 12. Zephronia erawani Bhansali & Wesener sp. nov., holotype ♂ (NHMD K56-9), volume rendering based on microcomputed tomography. A. Habitus, lateral view. B. Habitus, ventro-lateral view. C. Habitus, ventral view. D. Habitus, anterior view. E. Body-ring architecture, cross section trough midbody-ring. Abbreviations: Ant = Antennae; As = anal shield; Cl = clypeus; Col = collum; Cp = central pad; Cx = coxa; Fe = femur; Gc = gnathochilarium; Go = gonopore; Ip = inner palpi; Lc = locking carina; o = ommatidia; Pl = pleurite; Pl1= pleurite 1; Pl2= pleurite 2; Po = postfemur; Pre = prefemur; Pt = paratergite; Stp = stigmatic plate; St = stipites; Ta = tarsus; Te = tergite; Tg = thoracic shield groove; Ti = tibia; Ts = thoracic shield. Not to scale.
FIGURE 8 in New Thai giant pill-millipede species, with new genetic barcoding data (Diplopoda Sphaerotheriida, Zephroniidae)
FIGURE 8. Zephronia chrysomallos Bhansali & Wesener sp. nov., holotype ♂ (ZFMK MYR8826), scanning electron micrographs. A. Left antennae, lateral view. B. Left sixth antennomere with antennal disc. C. Left antenna, disc. D. Gnathochilarium, apical view. E. Left lateral palpi. F. Left central pad, two types of sensory cones on central pad, detail. Abbreviations: ac = apical cone; ad = antennal disc; Cp = central pad; Hyp = hypopharynx; Ip = inner palpi; LL = lamellae lingulales; Sc = sensory cone; St = stipites.
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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)
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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.