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133 results for “amplicon sequencing”
(Fastq Files) Amplicon sequencing of ama1 and mdr1 to track within-host P. falciparum diversity in Kilifi, KENYA
<p>These data were generated from amplicon sequencing of <em>Plasmodium falciparum</em> <em>ama1 </em>and<em> </em><em>mdr1</em> genes in samples collected from Kilifi, at the coast of Kenya.</p> <p>The two papers that reference these data will soon be included here:</p> <ol> <li> The Journal of Infectious Diseases - https://doi.org/10.1093/infdis/jiac144</li> <li>Wellcome Open Research - https://wellcomeopenresearch.org/articles/7-95</li> </ol> <p>Two objectives were explored:</p> <ol> <li>To determine temporal changes in the genetic diversity of malaria parasites in asymptomatic and febrile infections.</li> <li>To track within-host parasite diversity, throughout treatment in a clinical drug trial.</li> </ol>
Benchmarking bioinformatic tools for amplicon-based sequencing of norovirus
<p>This repository contains associated datasets and accession numbers for a study entitled '<strong>Benchmarking bioinformatic tools for amplicon-based sequencing of norovirus'</strong>. The scripts for this project can be found on the GitHub project<a href="https://github.com/ahfitzpa/Benchmarking-bioinformatics-norovirus-amplicons"> page</a>. </p> <p>Expected composition tsv files are the OTU tables for each simulation performed (001-010). OTU IDs in this case are the expected taxonomy with the associated accession numbers. Samples are numbered 1-40, including the simulation number. Expected sequences fasta files contain the sequences used as input for each simulation, without primers or Illumina adapter sequences.</p> <p>Amplicons were generated using the following primers:</p> <p><strong>GI Primers </strong><br> GISKF: CTG CCC GAA TTY GTA AAT GA 4<br> GISKR: CCA ACC CAR CCA TTR TAC A 5<br> <br> <strong>GII Primers </strong><br> G2SKF: CNT GGG AGG GCG ATC GCAA 8<br> G2SKR: CCR CCN GCA TRH CCR TTR TAC AT</p> <p>In this study, three databases and multiple classifiers were compared. Here we include the taxonomy and fasta files for each database; noronet =NoroNet RIVM, calicinet= HuCat CDC and custom, randomly generated database. Fasta files for the classifiers include the GI/GII primers listed above in a 5-3 orientation. </p> <p>The tags.txt file contains the Illumina adapters used for the simulation component of the study.</p>
BAMBI ITS - Analysis of the fungal component (via ITS amplicon sequencing) of stool samples from preterm babies
<p>Amplicon analysis of ITS amplicons from preterm babies.</p> <p>Associated GitHub repository: <a href="https://github.com/quadram-institute-bioscience/bambi-its">https://github.com/quadram-institute-bioscience/bambi-its</a></p>
Identification of grapevine clones via high-throughput amplicon sequencing: a proof-of-concept study VCF files
<p>VCF files used and cited in the article: Identification of grapevine clones via high-throughput amplicon sequencing: a proof-of-concept study</p>
Inventory of soil prokaryotic and fungal microbiome (via 16S rRNA gene amplicons and ITS sequencing) from Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, February 2019 - October 2020
Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased saltwater exposure, leading to change in soil microbial communities and many important biogeochemical processes. Given the high spatial and temporal heterogeneity in coastal wetlands, especially in tropical or subtropical climates characterized by seasonal temperature, precipitation, and tidal fluctuations, it remains unclear which environmental factors influence the compositions of soil microbial communities in wetlands affected by varying degrees of sea-water intrusion. To address this, a two-year survey was conducted on microbial community structure in submerged surface soils from 14 wetland sites across the Florida Everglades, representing three major ecosystem types, i.e. freshwater marshes, mangrove forests, and seagrass meadows. Bulk surface soil samples of each site were collected from February 2019 to October 2020 to cover dry and wet seasons. In addition to bulk soil samples, soil cores were collected from each site in August 2020 to assess vertical gradients of microbial communities. The dataset contains amplicon sequencing data of 16S rRNA gene (both bulk soil and soil cores) and ITS gene (only the bulk soil). The 2019 to 2020 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804243 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804243), PRJNA804246 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804246), and PRJNA804228 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804228). This data package is associated with the following publication: Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-H
Code for generating figures and analyzing amplicon sequencing of human mRNA and reporter mRNA targeted with type III-A CRISPR complex from Streptococcus thermophiles
<p>This dataset contains code for analyzing amplicon sequencing data and generating figures in the manuscript by Anna Nemudraia, Artem Nemudryi, and Blake Wiedenheft (2024), "Repair of CRISPR-guided RNA breaks enables site-specific RNA excision in human cells." </p> <p>Amplicon sequencing data has been deposited to NCBI Sequence Read Archive (SRA) under BioProject PRJNA1099688. The description of read files deposited to SRA can be found in the spreadsheet ./code_for_sequencing_data_analysis/SRA_read_files_description.xlsx</p> <p>The code for analyzing amplicon sequencing data can be found in the archive "code_for_sequencing_data_analysis.tar.gz." Output files from this analysis were used to generate figures. Figures were generated using the ggplot2 package in R and finalized in CorelDRAW.</p> <p>Code for generating figures can be found in the archive "code_for_generating_figures.tar.gz". </p> <p>Any questions or requests regarding the data or the code should be addressed to Dr. Artem Nemudryi at artem.nemudryi@gmail.com.</p>
Data from: Pitfalls and pointers: an accessible guide to marker gene amplicon sequencing in ecological applications
<p>Next Generation Sequencing (NGS) is a powerful tool that has been rapidly adopted by many ecologists studying microbial communities. Despite the exciting demonstration of NGS technology as a tool for ecological research, cryptic pitfalls inherent to its use can obscure correct interpretation of NGS data. Here, we provide an accessible overview of a NGS process that uses marker gene amplicon sequences (MGAS) that will allow scientists, particularly community ecologists, to make appropriate methodological choices and understand limits on inference about community composition and diversity that can be drawn from MGAS data.</p> <p>We describe the MGAS pipeline, focusing specifically on cryptic sources of variation that have received less emphasis in the ecological literature, but which may substantially impact inference about microbial community diversity and composition. By simulating communities from published microbiome data, we demonstrate how these sources of variation can generate inaccurate or misleading patterns.</p> <p>We specifically highlight sample dilution without researcher awareness and lane-to-lane variability, two cryptic sources of variation arising during the MGAS pipeline. These sources of variation affect estimates of species presence and relative abundance, particularly for species with moderate to low abundances. Each of these sources of bias can lead to errors in the estimation of both absolute and relative abundance within, and turnover among, microbial communities.</p> <p>Awareness and understanding of what happens and, specifically, why it happens during MGAS generation is key to generating a strong data set and building a robust community matrix. Requesting sample dilution information from the sequencing center, including technical replicates across sequencing lanes, and understanding how sampling intensity and community taxa distribution patterns shape the measurement of community richness, evenness, and diversity are critical for drawing correct ecological inferences using MGAS data.</p>
Assessment of microphytobenthos communities in the Kinzig catchment using photosynthesis-related traits, digital light microscopy and 18S-V9 amplicon sequencing
<p>This folder contains the datasets used in the article submitted to Frontiers in Ecology and Evolution in which we investigated the functional and compositional responses of microphytobenthos communities to surrounding land uses in the Kinzig River catchment, central Germany. We measured photosynthetic biomass using a Benthotorch, and analysed the diatom community using a newly developed digital light microscopy approach and 18S-V9 amplicon sequencing to characterise the whole protistan assemblages at sampling sites located in rural vs. urban areas.</p> <p>The folder contain the following datasets:</p> <p>kinzig2021_18SV9_filtered.csv # microphytobenthos 18SV9 amplicon sequencing data</p> <p>kinzig_paper.csv # OMNIDIA output of diatom data from microscopy and diatom subset from 18SV9 amplicon sequencing (including 4 letters OMINIDIA taxa codes for diatoms)</p> <p>kinzig2021_benthotorch.csv # Photosynthetic biomass (BenthoTorch data)</p> <p>Kinzig2021_fieldData.csv # environmental data</p> <p>env_dataKinz2021.csv # environment dataset</p> <p>Kinzig2021_diatDM&18S.R # Rscript used for analysis</p>
MinION sequence data: MinION sequencing of colorectal cancer tumor microbiomes – a comparison with amplicon-based and RNA-Sequencing
<p>MinION sequencing data that was unmapped by minimap2 for the 11 samples using in the "MinION sequencing of colorectal cancer tumor microbiomes – a comparison with amplicon-based and RNA-Sequencing" paper.</p>
Simultaneous genotyping of snails and infecting trematode parasites using high-throughput amplicon sequencing.
<p>Several methodological issues currently hamper the study of entire trematode communities within populations of their intermediate snail hosts. Here we develop a new workflow using high-throughput amplicon sequencing to simultaneously genotype snail hosts and their infecting trematode parasites. We designed primers to amplify 4 snail and 5 trematode markers in a single multiplex PCR. While also applicable to other genera, we focused on medically and economically important snail genera within the Superorder Hygrophila and targeted a broad taxonomic range of parasites within the Class Trematoda. We tested the workflow using 417 <i>Biomphalaria glabrata </i>specimens experimentally infected with <i>Schistosoma rodhaini</i>, two strains of<i> Schistosoma mansoni</i>,<i> </i>and combinations thereof. We evaluated the reliability of infection diagnostics, the robustness of the workflow, its specificity related to host and parasite identification, and the sensitivity to detect co-infections, immature infections, and changes of parasite biomass during the infection process. Finally, we investigated its applicability in wild-caught snails of other genera naturally infected with diverse trematode assemblages. After stringent quality control the workflow allows the identification of snails to species level, and of trematodes to taxonomic levels ranging from family to strain. It is sensitive to detect immature infections and changes in parasite biomass described in previous experimental studies. Co-infections were successfully identified, opening the possibility to examine parasite-parasite interactions such as interspecific competition. Altogether, these results demonstrate that our workflow provides a powerful tool to analyze the processes shaping trematode communities within natural snail populations.</p>
FASTA consensus sequences obtained using amplicon-based genome sequencing of SARS-CoV-2
<p>Set of 22 FASTA consensus sequences that were produced during routine SARS-CoV-2 sequencing obtained using amplicon-based sequencing (ARTIC protocol). Those sequences were compared to those generated in NASCarD applications.</p>
Data from: Pitfalls and pointers: an accessible guide to marker gene amplicon sequencing in ecological applications
Open the record for dataset details and reuse information.
Simultaneous genotyping of snails and infecting trematode parasites using high-throughput amplicon sequencing.
Open the record for dataset details and reuse information.
Raw Fast5 data for "Microbiota profiling with long amplicons using Nanopore sequencing: full-length 16S rRNA gene and the 16S-ITS-23S of the rrn operon" - PART I
<p>Raw Fast5 data for "Microbiota profiling with long amplicons using Nanopore sequencing: full-length 16S rRNA gene and the 16S-ITS-23S of the rrn operon". See Supplementary Table 2 for associating each sample to its barcode.</p> <p>- FC1_1 includes data for the HM mock community from BEI resources and skin microbiome of the chin in dogs.</p> <p>- FC1_2 includes data for the dorsal skin samples</p> <p>- FC2 includes data for the Zymobiomics mock community and Staphylococcus pseudintermedius isolate</p> <p> </p> <p> </p>
Rapid and real-time identification of fungi up to the species level with long amplicon Nanopore sequencing from clinical samples
<p>Samples collected from fungal cultures, skin of dogs and ZymoBIOMICS<sup>TM </sup>mock community (which includes <em>Saccharomyces cerevisiae</em> and <em>Cryptococcus neoformans</em>). The amplicons length of the fungal cultures and ZymoBIOMICS<sup>TM </sup>mock community is 3,5 Kb and 6 Kb, while the <em>Malassezia spp</em> samples used as control is 3,5 Kb. The amplicons length of the four samples from the skin is 3,5 Kb.</p>
Amplicon sequence variants by sample table from Antarctic methane seeps
<p>Antarctica is estimated to contain as much as a quarter of earth's marine methane, however we have not discovered an active Antarctic methane seep limiting our understanding of the methane cycle. In 2011, an expansive (70m x 1m) microbial mat formed at 10m water depth in the Ross Sea, Antarctica and we carried out 16S rRNA gene analysis on samples collected one year and five years after the methane seep formed. The data set attached is the resulting Amplicon Sequence Variant table by sample that we used to track the community composition change during this time and in comparison to other sampling in the McMurdo sound region. </p>
Non-Perennial rivers and streams under hydrological stress – comparing the effectiveness of amplicon sequencing and digital microscopy for diatom biodiversity appraisal
<p>ABC is project leader, data collector, contact person, etc.</p>
A pan-cetacean MHC amplicon sequencing panel developed and evaluated in combination with genome assemblies
<p>The major histocompatibility complex (MHC) is a highly polymorphic gene family that is crucial in immunity, and its diversity can be effectively used as a fitness marker for populations. Despite this, MHC remains poorly characterised in non-model species (e.g., cetaceans: whales, dolphins and porpoises) as high gene copy number variation, especially in the fast-evolving class I region, makes analyses of genomic sequences difficult. To date, only small sections of class I and IIa genes have been used to assess functional diversity in cetacean populations. Here, we undertook a systematic characterisation of the MHC class I and IIa regions in available cetacean genomes. We extracted full-length gene sequences to design pan-cetacean primers that amplified the complete exon2 from MHC class I and IIa genes in one combined sequencing panel. We validated this panel in 19 cetacean species and described 354 alleles for both classes. Furthermore, we identified likely assembly artefacts for many MHC class I assemblies based on the presence of class I genes in the amplicon data compared to missing genes from genomes. Finally, we investigated MHC diversity using the panel in 25 humpback and 30 southern right whales, including four paternity trios for humpback whales. This revealed copy-number variable class I haplotypes in humpback whales, which is likely a common phenomenon across cetaceans. These MHC alleles will form the basis for a cetacean branch of the Immuno-Polymorphism Database (IPD-MHC), a curated resource intended to aid in the systematic compilation of MHC alleles across several species, to support conservation initiatives.</p>
(Extended Data) Amplicon deep sequencing of ama1 and mdr1 to track within-host P. falciparum diversity throughout treatment in a clinical drug trial
<p>These extended data accompany the manuscript: Targeted Amplicon deep sequencing of ama1 and mdr1 to track within-host <em>P. falciparum</em> diversity throughout treatment in a clinical drug trial</p> <p><strong>Table S1: Concentration ratios and resulting parasitemia in artificial dna mixtures of P. falciparum Lab Isolates 3D7 and Dd2.</strong> This table presents the parasitemia for the artificial mixtures of P. falciparum lab isolates 3D7 and Dd2. Each mixture was prepared at varying ratios of 3D7 to Dd2, starting from equal proportions to a complete presence of only 3D7. The original concentration of each isolate was approximately 50,000 parasites per microliter (pf/μl), and the table displays the proportion of each strain in the mixture and the resulting total parasitemia concentration.</p> <p><strong>Table S2. List of PCR and deep sequencing primers.</strong> This table shows the list of forward and reverse primers used for deep sequencing. In boldface are the MID tags, while in the regular face are the forward primers</p> <p><strong>Table S3. The relative frequencies of each ama1 variant and the number of samples with each variant.</strong> The relative frequencies (%) of the 33 AMA1 variants in pre-and post-treatment samples (n = 330) are shown as a 33 amino acid sequence. The frequencies were calculated by dividing the number of reads of each microhaplotype by the total number of reads obtained per sample (116,187,131).</p> <p><strong>Table S4. Distribution of microhaplotypes among samples.</strong> This table shows the occurrence of microhaplotypes across all participants, both with monoclonal and multiclonal ama1 infections. It presents the ama1 clonality – monoclonal or multiclonal (column 1) - participant IDs (column 2), microhaplotype IDs (column 3), and the relative frequencies of these microhaplotypes across timepoints from 0 to 1008 hours (day 42) (column 3). Dashes represent time points where microhaplotypes were missing or were not detected.</p> <p><strong>Table S5. Distribution of rare microhaplotypes among samples.</strong> This table shows the occurrence of rare microhaplotypes in various samples. It presents participant IDs (column 1), microhaplotype IDs (column 2), and the relative frequencies of these microhaplotypes across time points from 0 to 1008 hours (day 42) (column 3). Samples containing rare microhaplotypes - specifically from PID10, PID32, PID38, PID40, PID49, PID60, PID63, and PID65 - are shown in orange, along with the corresponding rare microhaplotypes and their time points of occurrence. Furthermore, participants are categorised by shared microhaplotypes to indicate instances of rarity and commonality. Except for one microhaplotype unique to PID30, rare microhaplotypes were detected in several samples, frequently exceeding a 5% relative frequency. Dashes represent time points where microhaplotypes were missing or were not detected.</p> <p><strong>Table S6. The parasitemia levels associated with each ama1 microhaplotype per timepoint.</strong> This table shows the parasitemia for each ama1 microhaplotype per timepoint and each participant. “Patient ID” represents the patient ID, “AMA1 COI at 0h” represents the complexity of infection (COI) for each participant at baseline, based on ama1 while subsequent columns represent the parasitemia for each ama1 microhaplotype from timepoint 0h to 1008h. Parasitemia was back-calculated using the COI and total parasitemia for each time point. For time points with a COI > 1, parasitemia for the respective ama1 microhaplotypes are separated by commas, cells in red indicate timepoints without sequencing data (ND = not determined). In contrast, cells in grey indicate time points where microhaplotypes were detected below 10 parasites/μl, hence at risk of falling below the sampling limit.</p> <p><strong>Figure S1. Performance of AmpSeq in the sequencing controls.</strong> Six aliquots were prepared for each control set to ensure sufficient control data in case of PCR or sequencing failure. The median read depth in the lab controls was 5,658 (range 4,310 – 12,603) and 704 (291 – 1,676). The x-axis represents the aliquot identifier across the five mixtures, starting from 1 to 6, while the y-axis represents the proportions of each variant across all aliquots. For ama1 (A), two variants (3D7 and Dd2) were detected, whereas in mdr1 (B), two variants were detected YY, FY and NY following amplification of Dd2 Copy I, Dd2 Copy II and 3D7, respectively. For ama1, sequencing failed for aliquot 6 of control set 1, while for mdr1, sequencing failed for aliquot 2 and 6 of control set 3, aliquots 1 and 6 of control set 4 and aliquots 1 and 5 of control set 5. Under the mdr1 control set 4, the Dd2 copy II (86F, 184Y) was not identified, possibly due to having very low concentrations that were not picked up in this aliquot. Based on our control mixtures, the minimum variant frequency we could detect was 5%.</p> <p><strong>Figure S2. Heatmaps of the successfully PCR amplified and sequenced samples for ama1 (A) and mdr1 (B).</strong> The rows represent the study participants, while the columns represent time in hours. Successfully sequenced samples are shown in blue, those that failed PCR are shown in red and those that failed sequencing are in black. The timepoint “ Rec” represents unscheduled visits where a recurrent sample was collected. The unshaded areas with "-" are time points where samples were not collected. For each time point, the number of samples successfully sequenced (n Successful) is indicated in the last row of each panel. The table in panel C shows the groupings of samples based on parasitemia, high (> 5,000), moderate (100-5,000) and low (< 100 parasites per microlitre). Many samples collected between 0h-12h had high parasitemia, samples collected between 18h–30h had moderate parasitemia, while samples collected after 30h were primarily of low parasitemia.</p> <p><strong>Figure S3. The mean complexity of infection (COI) by AMA1 throughout treatment.</strong> The mean COI (red diamonds) appeared to be stable (between 1.5 - 2) from baseline (0h) up to 72h and thereafter fluctuated due to the small sample sizes (<5) in the post-treatment samples. The black dots represent the COI per sample.</p> <p> </p>
Raw data: multispecies amplicon sequencing (Loera, Studer, and Kölliker, 2021, Molecular Ecology Resources)
<p>Grasslands cover close to two fifths of Earth's land. They provide many ecosystem services related to the maintenance of soil integrity, and the regulation of water, carbon and nitrogen flows. Grasslands constitute the basis for sustainable roughage production for ruminant feeding. In Switzerland, grasslands cover more than 70% of the total agricultural land, which highlights their importance in the domestic food production chains.</p> <p>Plant genetic diversity (PGD), a component of biodiversity, influences ecosystem functioning in grasslands. High levels of grassland PGD are related to resistance against invasive plants and yield stabilization during environmental stress (e.g., drought or frost). The PGD of grasses and legumes —the two most economically relevant plant families found in grasslands, which naturally grow in a wide climate spectrum— harbors valuable genetic resources for forage breeding. Nevertheless, most PGD studies of natural or semi-natural grasslands (i.e., grasslands that are not sown) focus on a single or a few related species. Traditional PGD monitoring methods (e.g., simple sequence repeats, or SSRs) are ill-suited for large-scale, multispecies assessments. This limits our ability to study the ecological effects of grassland PGD, its spatiotemporal patterns, and its significance for grassland management.</p> <p>Looking to provide cost-effective tools for multispecies PGD monitoring in grasslands, we performed a sequence capture assay targeting 611 single-copy nuclear loci, followed by multispecies amplicon sequencing (i.e., amplicon sequencing using primer pairs that can be used in multiple species) on eleven selected loci.</p> <p>Our results indicate that multispecies amplicon sequencing is a cost-effective tool for genetic diversity assessment in grassland plant species. Furthermore, the sequence capture data provides the means to extend the number of multispecies amplicons for further research.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.