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2,271 results for “Next generation sequencing”
Data from: Utilizing next-generation sequencing to resolve the backbone of the Core Goodeniaceae and inform future taxonomic and floral form studies
Though considerable progress has been made in inferring phylogenetic relationships of many plant lineages, deep unresolved nodes remain a common problem that can impact downstream efforts, including taxonomic decision-making and character reconstruction. The Core Goodeniaceae is a group affected by this issue: data from the plastid regions trnL-trnF and matK have been insufficient to generate adequate support at key nodes along the backbone of the phylogeny. We performed genome skimming for 24 taxa representing major clades within Core Goodeniaceae. The plastome coding regions (CDS) and nuclear ribosomal repeats (NRR) were assembled and complemented with additional accessions sequenced for nuclear G3PDH and plastid trnL-trnF and matk. The CDS, NRR, and G3PDH alignments were analyzed independently and topology tests were used to detect the alignments' ability to reject alternative topologies. The CDS, NRR, and G3PDH alignments independently supported a Brunonia (Scaevola s.l. (Coopernookia (Goodenia s.l.))) backbone topology, but within Goodenia s.l., the strongly-supported plastome topology (Goodenia A (Goodenia B (Velleia + Goodenia C))) contrasts with the poorly supported nuclear topology ((Goodenia A + Goodenia B) (Velleia + Goodenia C)). A fully resolved and maximally supported topology for Core Goodeniaceae was recovered from the plastome CDS, and there is excellent support for most of the major clades and relationships among them in all alignments. The composition of these seven major clades renders many of the current taxonomic divisions non-monophyletic, prompting us to suggest that Goodenia may be split into several segregate genera.
Targeted Next Generation Sequencing data for European Green Crab
<p class="western"><span>In the northeast Pacific Ocean there is high interest in developing eDNA-based survey methods to aid management of invasive populations of European green crab (<i>Carcinus maenas</i>). Expected benefits are improved sensitivity for early detection of secondary spread and to assess the outcome of eradication efforts. A new eDNA-based approach we term 'Targeted Next Generation Sequencing (tNGS)' is introduced here and shown to improve detection relative to qPCR at sites with lower green crab CPUE values measured by trapping. DNA standards (gBlock) with starting molecule copies that were 10- to 100- times lower than the qPCR limit of detection returned significant numbers of sequencing reads, which in our field assessments translated to a 7% - 10% increase in detection probability from tNGS relative to qPCR at sites with lower CPUE. We also found the number of sequencing reads from tNGS was significantly correlated with green crab CPUE whereas Ct values from qPCR were not. When sources of variation were partitioned for each assay, we found the difference between mean within-site and mean between-site variation was much larger and had non-overlapping confidence intervals for tNGS relative to qPCR, suggesting the former may offer more power for detecting spatial variation in eDNA availability. Results presented here indicate this approach is suitable for species of known low abundances where a positive detection has high economic or environmental consequences. Any species with an existing qPCR assay can be easily tested with a tNGS assay. We conclude with a discussion on the fit for purpose applications of tNGS vs. qPCR on how to best apply molecular surveys in management programs.</span></p>
A method for determining the origin of crude drugs derived from animals using MinION, a compact next-generation sequencer
<p><span>We evaluated whether MinION, an inexpensive, portable sequencer, can be applied for identifying the origin of crude drugs</span> <span>derived from animals</span><span>.</span><span> Standard and nonstandard crude drugs with different species of origin were examined. In addition, the standards mixed with nonstandard samples </span><span>were used</span><span>.</span><span> As a target gene, cytochrome c oxidase I was amplified and sequenced. The Fast mode results had a slightly lower match ratio than High-accuracy mode, but the animals of origin were correctly determined by BLAST for all samples. For antler velvet derived from <em>Rangifer tarandus</em>, even the sequences were aligned based on <em>Cervus elaphus</em>, the animal of origin was determined correctly. Minor contents could be detected from mixtures of two animals, if the mixtures contained at least 19:1 mtDNA when the coverage allele-fraction threshold was 0.05. By contrast, in Fast mode, two sequences could not be separated due to the low accuracy of the base-calling in each read. For field work, the species of origin of crude drugs could be identified, by only simple DNA extraction and library preparation. Therefore, MinION appears to be a convenient tool for identifying the origin of crude drugs derived from animals.</span></p>
Data from: Stepwise Threshold Clustering: a new method for genotyping MHC loci using next-generation sequencing technology
Genes of the vertebrate major histocompatibility complex (MHC) are of great interest to biologists because of their important role in immunity and disease, and their extremely high levels of genetic diversity. Next generation sequencing (NGS) technologies are quickly becoming the method of choice for high-throughput genotyping of multi-locus templates like MHC in non-model organisms. Previous approaches to genotyping MHC genes using NGS technologies suffer from two problems: 1) a "gray zone" where low frequency alleles and high frequency artifacts can be difficult to disentangle and 2) a similar sequence problem, where very similar alleles can be difficult to distinguish as two distinct alleles. Here were present a new method for genotyping MHC loci – Stepwise Threshold Clustering (STC) – that addresses these problems by taking full advantage of the increase in sequence data provided by NGS technologies. Unlike previous approaches for genotyping MHC with NGS data that attempt to classify individual sequences as alleles or artifacts, STC uses a quasi-Dirichlet clustering algorithm to cluster similar sequences at increasing levels of sequence similarity. By applying frequency and similarity based criteria to clusters rather than individual sequences, STC is able to successfully identify clusters of sequences that correspond to individual or similar alleles present in the genomes of individual samples. Furthermore, STC does not require duplicate runs of all samples, increasing the number of samples that can be genotyped in a given project. We show how the STC method works using a single sample library. We then apply STC to 295 threespine stickleback (Gasterosteus aculeatus) samples from four populations and show that neighboring populations differ significantly in MHC allele pools. We show that STC is a reliable, accurate, efficient, and flexible method for genotyping MHC that will be of use to biologists interested in a variety of downstream applications.
Supplementary material 1 from: Yuhui X, Lijun Z, Yue H, Xiaoqi W, Chen Z, Huilun Z, Ruoran W, Da P, Hongying S (2017) Complete mitochondrial genomes from two species of Chinese freshwater crabs of the genus Sinopotamon recovered using next-generation sequencing reveal a novel gene order (Brachyura, Potamidae). ZooKeys 705: 41-60. https://doi.org/10.3897/zookeys.705.11852
Figure S1 : Explanation note: Comparisons of the consensus sequence and variable sites in the entire mNCR for Sinopotamon yaanense, S. yangtsekiense and S. xiushuiense. The conserved central domain is grey shaded, and the extended termination associated sequences (ETAS) is underlined.
Tutorial-Next-Generation-Sequence-Analysis
<p>This upload contains .fastq and reference genome files for learning and practicing NGS analysis.</p>
Laboratory validation of a clinical metagenomic next-generation sequencing assay for respiratory virus detection and discovery
<p>This repository contains data and code used to analyze data for this manuscript: </p> <p><em>Laboratory validation of a clinical metagenomic next-generation sequencing assay for respiratory virus detection and discovery</em></p>
Comprehensive survey of common bean viruses in Tanzania using next generation and Sanger sequencing techniques
<p>Common bean (<em>Phaseolus vulgaris</em> L.) is an important legume crop in Tanzania and elsewhere in the tropics and subtropics. We employed a next-generation sequencing technique to detect viruses in common bean plant samples collected from five agricultural research zones in the country. The aim was to target and sequence virus-derived small RNAs. To achieve this, total RNA was isolated from dry leaf samples using the CTAB method. The CTAB buffer contained 2% CTAB, 100 mM Tris–HCl, 20 mM EDTA, 2.5 M NaCl, freshly prepared 1% sodium sulfite, 2% PVP and 2.5% 2-mercaptoethanol in nuclease-free water. Total RNA was sent to Fasteris in Switzerland where the small RNA was purified by electrophoresis in an acrylamide gel. The small RNA library was prepared using the Illumina TrueSeq small RNA sample preparation kit (Illumina Inc., San Diego, CA, USA). Viruses were detected using VirusDetect software (v.1.6 and v.1.7) (available at http://bioinfo.bti.cornell.edu/cgi-bin/virusdetect/index.cgi) and supercomputer at CSC.fi. Viruses detected belonged to at least 11 genera.</p>
Supplementary material 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 S1, S2 : Explanation note: Figure S1. Bayesian phylogeny of 29 samples of Quercus and one Trigonobalanus (outgroup) based on ITS sequences. Branches are labeled with posterior probabilites. Figure S2. Bayesian phylogeny of 29 samples of Quercus and one Trigonobalanus (outgroup) based on concatenated rbcL and matK sequences. Branches are labeled with posterior probabilities.
FIGURE 2 in USING Next-Generation Sequencing (NGS) TO UNCOVER DIVERSITY OF WOOD-DECAYING FUNGI IN NEOTROPICAL ATLANTIC FORESTS
FIGURE 2. Relative abundances of reads of fungal genera per study area and metabarcode using 95% sequence similarity with reference sequences in the UNITE database as surrogate for traditional taxonomic generic concepts.
FIGURE 1 in USING Next-Generation Sequencing (NGS) TO UNCOVER DIVERSITY OF WOOD-DECAYING FUNGI IN NEOTROPICAL ATLANTIC FORESTS
FIGURE 1. Genera richness at the levels of phylum, class and order per study area and metabarcode using 95% sequence similarity with reference sequences in the UNITE database as surrogate for traditional taxonomic generic concepts.
Data from: Spatial dynamics and mixing of bluefin tuna in the Atlantic Ocean and Mediterranean Sea revealed using next generation sequencing
The Atlantic bluefin tuna is a highly migratory species emblematic of the challenges associated with shared fisheries management. In an effort to resolve the species' stock dynamics, a genome-wide search for spatially informative single nucleotide polymorphisms (SNPs) was undertaken, by way of sequencing reduced representation libraries. An allele frequency approach to SNP discovery was used, combining the data of 555 larvae and young-of-the-year (LYOY) into pools representing major geographical areas and mapping against a newly assembled genomic reference. From a set of 184,895 candidate loci, 384 were selected for validation using 167 LYOY. A highly discriminatory genotyping panel of 95 SNPs was ultimately developed by selecting loci with the most pronounced differences between western Atlantic and Mediterranean Sea LYOY. The panel was evaluated by genotyping a different set of LYOY (n= 326) and from these 77.8% and 82.1% were correctly assigned to western Atlantic and Mediterranean Sea origins, respectively. The panel revealed temporally persistent differentiation among LYOY from the western Atlantic and Mediterranean Sea (FST = 0.008, p=0.034). The composition of six mixed feeding aggregations in the Atlantic Ocean and Mediterranean Sea was characterized using genotypes from medium (n = 184) and large (n = 48) adults, applying population assignment and mixture analyses. The results provide evidence of persistent population structuring across broad geographic areas and extensive mixing in the Atlantic Ocean, particularly in the mid-Atlantic Bight and Gulf of St. Lawrence. The genomic reference and genotyping tools presented here constitute novel resources useful for future research and conservation efforts.
FIG. 2 in Diet determined by next generation sequencing reveals pest consumption and opportunistic foraging by bats in macadamia orchards in South Africa
FIG. 2. The proportions of different insect orders in the diet of six species of bats occurring in macadamia orchards based on the frequency of occurrence (number of pellets containing that order divided by the total number of pellets in the species sample) based on NGS results of this study. Sample sizes (n) represent the number of pellets
FIG. 3 in Diet determined by next generation sequencing reveals pest consumption and opportunistic foraging by bats in macadamia orchards in South Africa
FIG. 3. Variation of percentage occurrence of insect families in the diet from faecal pellet samples of M. midas collected on three occasions at two macadamia farms, Welgevonden (A, B) and Vlakfontein (C). Insect orders abbreviated: Blat. — Blattodea, Col. — Coleoptera, Dipt. — Diptera, Hemi. — Hemiptera, Neur. — Neuroptera, Orth. — Orthoptera
FIG. 1 in Diet determined by next generation sequencing reveals pest consumption and opportunistic foraging by bats in macadamia orchards in South Africa
FIG. 1. Map of study area showing location of study area in South Africa and Limpopo Province (A) and detailed location of faecal pellet collection sites within the study area (B). Grey shading indicates the extent of vegetation types associated with the Soutpansberg Mts. Numbers represent localities as follows: 1) Farm Vlakfontein, 2–5) Farm Welgevonden, 6) Farm Laatsgevonden
Phase II Umbrella Study Directed by Next Generation Sequencing
ClinicalTrials.gov study NCT03574402. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Research on the Heterogeneity of Taiwanese Breast Cancer Patients by Next Generation Sequencing (NGS) Tools
ClinicalTrials.gov study NCT04626440. IPD Sharing: NO. Countries: 1. Publications: 1.
Next Generation Sequencing of Tumor Cells in Locally Advanced Pancreatic Adenocarcinoma
ClinicalTrials.gov study NCT03578939. IPD Sharing: NO. Countries: 1. Publications: 1.
Next Generation Sequencing to Detect Acute Rejection in Lung Transplant Patients.
ClinicalTrials.gov study NCT05260372. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
Next Generation Sequencing Detection of Lyme Disease
ClinicalTrials.gov study NCT03505879. IPD Sharing: Not stated. Countries: 1. Publications: 7.
ScienceDex guides
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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.