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2,271 results for “Next generation sequencing”
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.
Figure 3 from: Shaoli M, Hao Y, Chao L, Yafu Z, Fuming S, Yuchao W (2018) The complete mitochondrial genome of Xizicus (Haploxizicus) maculatus revealed by next-generation sequencing and phylogenetic implication (Orthoptera, Meconematinae). ZooKeys 773: 57-67. https://doi.org/10.3897/zookeys.773.24156
Figure 3 Phylogenetic reconstruction of Tettigoniidea using mitochondrial PCGs and rRNA concatenated dataset. A Bayesian result, applicable posterior probability values are shown B Maximum likelihood result with applicable bootstrap values shown.
Figure 2 from: Shaoli M, Hao Y, Chao L, Yafu Z, Fuming S, Yuchao W (2018) The complete mitochondrial genome of Xizicus (Haploxizicus) maculatus revealed by next-generation sequencing and phylogenetic implication (Orthoptera, Meconematinae). ZooKeys 773: 57-67. https://doi.org/10.3897/zookeys.773.24156
Figure 2 Relative synonymous codon usage of X. (X.) fascipes, X. (E.) howardi, X. (H.) maculatus mitochondrial protein-coding genes. Condon families are provided on the x-axis.
Data from: Next-generation polyploid phylogenetics: rapid resolution of hybrid polyploid complexes using PacBio single-molecule sequencing
Difficulties in generating nuclear data for polyploids have impeded phylogenetic study of these groups. We describe a high-throughput protocol and an associated bioinformatics pipeline (PURC: "Pipeline for Untangling Reticulate Complexes") that is able to generate these data quickly and conveniently, and demonstrate its efficacy on accessions from the fern family Cystopteridaceae. We conclude with a demonstration of the downstream utility of these data by inferring a multilabeled species tree for a subset of our accessions. We amplified four ~1kb-long nuclear loci and sequenced them in a parallel-tagged amplicon sequencing approach using the PacBio platform. PURC infers the final sequences from the raw reads via an iterative approach that corrects PCR and sequencing errors and removes PCR-mediated recombinant sequences (chimeras). We generated data for all gene copies (homeologs, paralogs, and segregating alleles) present in each of three sets of 50 mostly-polyploid accessions, for four loci, in three PacBio runs (one run per set). From the raw sequencing reads PURC was able to accurately infer the underlying sequences. This approach makes it easy and economical to study the phylogenetics of polyploids, and in conjunction with recent analytical advances, facilitates investigation of broad patterns of polyploid evolution.
Data from: Fast and cost-effective genetic mapping in apple using next-generation sequencing
Next-generation DNA sequencing (NGS) produces vast amounts of DNA sequence data, but it is not specifically designed to generate data suitable for genetic mapping. Recently developed DNA library preparation methods for NGS have helped solve this problem, however, by combining the use of reduced representation libraries with DNA sample barcoding to generate genome-wide genotype data from a common set of genetic markers across a large number of samples. Here we use such a method, called genotyping-by-sequencing (GBS), to produce a data set for genetic mapping in an F1 population of apples (Malus x domestica) segregating for skin color. We show that GBS produces a relatively large, but extremely sparse, genotype matrix: over 270,000 SNPs were discovered, but most SNPs have too much missing data across samples to be useful for genetic mapping. After filtering for genotype quality and missing data, only 6% of the 85 million DNA sequence reads contributed to useful genotype calls. Despite this limitation, using existing software and a set of simple heuristics, we generated a final genotype matrix containing 3967 SNPs from 89 DNA samples from a single lane of Illumina HiSeq and used it to create a saturated genetic linkage map and to identify a known QTL underlying apple skin color. We therefore demonstrate that GBS is a cost effective method for generating genome-wide SNP data suitable for genetic mapping in a highly diverse and heterozygous agricultural species. We anticipate future improvements to the GBS analysis pipeline presented here that will enhance the utility of next-generation DNA sequence data for the purposes of genetic mapping across diverse species.
A Streamlined and High-Throughput Error-Corrected Next-Generation Sequencing Method for Low Variant Allele Frequency Quantitation
<p></p><p>Quantifying mutant or variable allele frequencies (VAFs) of ≤10−3 using next-generation sequencing (NGS) has utility in both clinical and nonclinical settings. Two common approaches for quantifying VAFs using NGS are tagged single-strand sequencing and duplex sequencing. While duplex sequencing is reported to have sensitivity up to 10−8 VAF, it is not a quick, easy, or inexpensive method. We report a method for quantifying VAFs that are ≥10−4 that is as easy and quick for processing samples as standard sequencing kits, yet less expensive than the kits. The method was developed using PCR fragment-based VAFs of Kras codon 12 in log10 increments from 10−5 to 10−1, then applied and tested on native genomic DNA. For both sources of DNA, there is a proportional increase in the observed VAF to input VAF from 10−4 to 100% mutant samples. Variability of quantitation was evaluated within experimental replicates and shown to be consistent across sample preparations. The error at each successive base read was evaluated to determine if there is a limit of read length for quantitation of ≥10−4, and it was determined that read lengths up to 70 bases are reliable for quantitation. The method described here is adaptable to various oncogene or tumor suppressor gene targets, with the potential to implement multiplexing at the initial tagging step. While easy to perform manually, it is also suited for robotic handling and batch processing of samples, facilitating detection and quantitation of genetic carcinogenic biomarkers before tumor formation or in normal-appearing tissue.</p><p></p>
Data from: Advanced characterization of DNA molecules in rAAV vector preparations by single-stranded virus next-generation sequencing
Recent successful clinical trials with recombinant adeno-associated viral vectors (rAAVs) have led to a renewed interest in gene therapy. However, despite extensive developments to improve vector-manufacturing processes, undesirable DNA contaminants in rAAV preparations remain a major safety concern. Indeed, the presence of DNA fragments containing antibiotic resistance genes, wild-type AAV, and packaging cell genomes has been found in previous studies using quantitative polymerase chain reaction (qPCR) analyses. However, because qPCR only provides a partial view of the DNA molecules in rAAV preparations, we developed a method based on next-generation sequencing (NGS) to extensively characterize single-stranded DNA virus preparations (SSV-Seq). In order to validate SSV-Seq, we analyzed three rAAV vector preparations produced by transient transfection of mammalian cells. Our data were consistent with qPCR results and showed a quasi-random distribution of contaminants originating from the packaging cells genome. Finally, we found single-nucleotide variants (SNVs) along the vector genome but no evidence of large deletions. Altogether, SSV-Seq could provide a characterization of DNA contaminants and a map of the rAAV genome with unprecedented resolution and exhaustiveness. We expect SSV-Seq to pave the way for a new generation of quality controls, guiding process development toward rAAV preparations of higher potency and with improved safety profiles.
Figure 7 from: Fischer G, Azorsa F, Hita Garcia F, Mikheyev AS, Economo EP (2015) Two new phragmotic ant species from Africa: morphology and next-generation sequencing solve a caste association problem in the genus Carebara Westwood. ZooKeys 525: 77-105. https://doi.org/10.3897/zookeys.525.6057
Figure 7 - Carebara phragmotica sp. n. Minor worker (paratype: CASENT0709554). A head in full-face view B body in profile view C body in dorsal view.
Figure 4 from: Fischer G, Azorsa F, Hita Garcia F, Mikheyev AS, Economo EP (2015) Two new phragmotic ant species from Africa: morphology and next-generation sequencing solve a caste association problem in the genus Carebara Westwood. ZooKeys 525: 77-105. https://doi.org/10.3897/zookeys.525.6057
Figure 4 - Carebara thoracica (Weber, 1950) – original drawings. A minor worker full-face view B minor worker profile of body C major worker full-face view D major worker profile.
Figure 3 from: Fischer G, Azorsa F, Hita Garcia F, Mikheyev AS, Economo EP (2015) Two new phragmotic ant species from Africa: morphology and next-generation sequencing solve a caste association problem in the genus Carebara Westwood. ZooKeys 525: 77-105. https://doi.org/10.3897/zookeys.525.6057
Figure 3 - Carebara elmenteitae (Patrizi, 1948) – original drawings. Holotype phragmotic worker (I), dorsal view (II): A head in ventral view B head in oblique dorsolateral view C mesosoma and waist in profile.
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.