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253 results for “amplicons”
Data from: Unraveling independent origins of two tetraploid Achillea species by amplicon sequencing
<p>Allopolyploidy is a significant mechanism of plant speciation, and many allopolyploid species have arisen recurrently. However, the probability that allopolyploidization between the same two parental species may lead to the origin of different taxa has received little attention. Here we used a new progenitor-specific amplicon sequencing method to demonstrate the independent origins of two yarrow species, <i>Achillea alpina</i> and <i>A. wilsoniana</i>, via allotetraploidy from the same diploid progenitor species pair, <i>A. acuminata</i> and <i>A. asiatica</i>. Based on the sequences of 17 nuclear genes from 21 wild populations of the four <i>Achillea </i>species investigated, a clear view of genetic structure and demographic history was obtained with each species. Significant genetic differentiation was evident between the two tetraploid species. Two genetically distinguishable groups were detected within one of the progenitor, <i>A. acuminata</i>, and ancestors belonging to those two groups contributed to the two tetraploid species, respectively. Excluding fixed heterozygosity, we detected extremely low genetic diversity in many populations of both tetraploid species. Approximate Bayesian computation indicated that both tetraploid species originated before the Last Glacial Maximum, and nearly all diploid lineages went through population declines after the allopolyploidization events. Our study demonstrates that independent allopolyploidization events between the same <i>Achillea</i> parental species have generated two genetically and ecologically distinct taxa.</p>
STAMINA project 883441 related raw sequencing data of RTPCR positive SARS-CoV-2 amplicons
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MGnify amplicon v5.0 test data for Galaxy iwc
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A georeferenced rRNA amplicon database of aquatic microbiomes from South America
<p>Here we present the samples and data used in Metz & Huber et al. (2022) to construct <strong>µSudAqua[db].</strong></p> <p><strong>µSudAqua[db]</strong> contains over 866 georeferenced samples with contextual environmental information manually revised. For its integration and validation, we constructed a curated database (<strong>µSudAqua[db.sp]</strong>) using the DADA2 pipeline (https://github.com/microsudaqua/usudaquadb). It comprised ~60% of the total georeferenced samples of the <strong>µSudAqua[db]</strong>.</p> <p>Here we provide five files compressed in a zip file .</p> <ul> <li>The <em>microsudaqua_metadata_Vx </em>presents the metadata associated with the samples used to build the <strong>µSudAqua[db] </strong>database. For samples included in the <strong>µSudAqua[db.sp]</strong> database, the number of high-quality reads and Amplicon Sequences Variants (ASVs) defined is also indicated.</li> <li>The archive <em>microsudaqua_rawtable_Vx </em>harvests the<em> </em>number of reads in each sample (<strong>µSudAqua[db.sp]</strong>).</li> <li>The archive <em>microsudaqua_rawseqs_Vx</em> harvests the nucleotide sequences of each ASV (<strong>µSudAqua[db.sp]</strong>).</li> <li>The archive <em>microsudaqua_rawtaxonomy_blast_silva132_nr99_Vx </em>harvests taxonomic classification of each ASV (<strong>µSudAqua[db.sp]</strong>).</li> <li>The archive <em>microsudaqua_bacteria_filtered_50reads_with_taxonomy_Vx</em> harvests the Bacterial filtered ASVs, with more than 50 reads in at least three samples (<strong>µSudAqua[db.sp]</strong>).</li> </ul> <p>Further information regarding data usage and processing is available in the @microsudaqua GitHub (https://github.com/microsudaqua/usudaquadb)</p> <h3><strong>Version history</strong></h3> <ul> <li>February 2025 / version V.1.1: microsudaqua_data_V1.1_Feb2025.zip <ul> <li>The metadata file has been updated: microsudaqua_metadata_V1.1_Feb2025.txt</li> </ul> </li> </ul> <ul> <li>July 2022 / version V.1.0: microsudaqua_data_V1.0_July2022.zip.</li> </ul> <p> </p>
Supplementary material 1 from: Gueidan C, Li L (2022) A long-read amplicon approach to scaling up the metabarcoding of lichen herbarium specimens. MycoKeys 86: 195-212. https://doi.org/10.3897/mycokeys.86.77431
Table S1. List of specimens used for this study, including their voucher information, plate location, indexing, amplicon concentration and sequencing results, both as an output from SMRT tools (CCSs) and as an output from DADA2 (sequence variants). Table S2. List of the 64 barcode sequences used to index the samples. Used barcode pairs are listed in Table S1
Supplementary Information of Amplicon-based nanopore sequencing of patients with COVID-19 omicron (B.1.1.529) variant from India
<p><strong>We report sequencing of omicron variants from SARS-CoV-2 in 75 patients, using Nanopore long-read sequencing chemistry. We highlight the nature of mutations in spike glycoprotein that are unique and common to other populations.</strong></p> <p> </p>
Amplicon_sorter: a tool for reference-free amplicon sorting based on sequence similarity and for building consensus sequences
<p>Oxford Nanopore Technologies (ONT) is a third-generation sequencing technology that is gaining popularity in ecological research for its portable and low-cost sequencing possibilities. Although the technology excels at long-read sequencing, it can also be applied to sequence amplicons. The downside of ONT is the low quality of the raw reads. Hence, generating a high-quality consensus sequence is still a challenge. We present Amplicon_sorter, a tool for reference-free sorting of ONT sequenced amplicons based on their similarity in sequence and length and for building solid consensus sequences.</p>
Supplementary material 7 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S7. Taxonomic classification of the rDNA of fungal.: Explanation note: Taxonomic classification of the rDNA of fungal shotgun metagenome.
Supplementary material 3 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S3. Data set of the SSU V4 and V5 barcodes.: Explanation note: Data set of the SSU V4 and V5 barcodes.
Supplementary material 1 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S1. Characteristics of soil samples.: Explanation note: Characteristics of soil samples used in this study.
Supplementary material 6 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S6. Data set of the LSU D1, D2, and D3 barcodes.: Explanation note: Data set of the LSU D1, D2, and D3 barcodes.
Supplementary material 2 from: Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, Kõljalg U, Kisand V, Nilsson RH, Hildebrand F, Bork P, Abarenkov K (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10: 1-43. https://doi.org/10.3897/mycokeys.10.4852
Table S2. Taxonomic composition and clustering of the mock community sample.: Explanation note: Taxonomic composition and clustering of the mock community sample.
FIGURE 5. The COI PCR amplicons generated via LepF1-LepR1 in First record of the genus Haritalodes Warren, 1890 and H. derogata (Fabricius, 1775) (Lepidoptera: Crambidae: Spilomelinae) from Türkiye and its morphological and molecular identification
FIGURE 5. The COI PCR amplicons generated via LepF1-LepR1 (Lanes 1-2), LCO1490-HCO2198 (Lanes 3-4), and Jerry-Pat (Lanes 5-6) primer pairs using DNA from the larvae (L) or adult (A) at 1% agarose gel. L refers to ladder (100 bp ladder marker, Invitrogen, USA).
18S/16S raw amplicon data for Martínez Martínez et al. : "Coastal bacteria and protists assimilate viral carbon and nitrogen"
<p>Raw amplicon (18S and 16S rRNA) sequencing data (.fastq.gz) for Martínez Martínez <em>et al.</em> : "Coastal bacteria and protists assimilate viral carbon and nitrogen". Each sample has forward (*_1.fastq.gz) and reverse (*_2.fastq.gz) reads as separate files. </p>
16S Amplicon sequence variants (ASVs) data of NEREA Augmented Observatory
<p><strong>Metabarcoding - 16S ASV generation and taxonomic assignment</strong>. Raw 16S paired-end sequences were subjected to a data quality control step and subsequently imported into the QIIME2 pipeline v.2022.2.0. Leftover primers and adapter sequences were removed through cutadapt. The amplicon sequence variants (ASV) table, which represent true biological sequences within each sample, was generated using the denoised-paired method including truncation, denoising, dereplication, merging, and chimera filtering of the DADA2 (Divisive Amplicon Denoising Algorithm 2) plugin inside QIIME2. Default parameters were used with the exception of the forward and reverse sequence length (--p-trunc-len-f and --p-trunc-len-r), that were set to 220 and 180, respectively. Processed reads that passed all these filters were used for taxonomy classification. The V4-V5 regions were extracted from the pre-formatted reference sequences and taxonomy file built on the SILVA 138 99% OTUs database and the vsearch v.2.6.2 global alignment implemented in QIIME2 was used.</p>
Data from: Digital fragment analysis of short tandem repeats by high-throughput amplicon sequencing
High-throughput sequencing has been proposed as a method to genotype microsatellites and overcome the four main technical drawbacks of capillary electrophoresis: amplification artifacts, imprecise sizing, length homoplasy, and limited multiplex capability. The objective of this project was to test a high-throughput amplicon sequencing approach to fragment analysis of short tandem repeats and characterize its advantages and disadvantages against traditional capillary electrophoresis. We amplified and sequenced 12 muskrat microsatellite loci from 180 muskrat specimens and analyzed the sequencing data for precision of allele calling, propensity for amplification or sequencing artifacts, and for evidence of length homoplasy. Of the 294 total alleles, we detected by sequencing, only 164 alleles would have been detected by capillary electrophoresis as the remaining 130 alleles (44%) would have been hidden by length homoplasy. The ability to detect a greater number of unique alleles resulted in the ability to resolve greater population genetic structure. The primary advantages of fragment analysis by sequencing are the ability to precisely size fragments, resolve length homoplasy, multiplex many individuals and many loci into a single high-throughput run, and compare data across projects and across laboratories (present and future) with minimal technical calibration. A significant disadvantage of fragment analysis by sequencing is that the method is only practical and cost-effective when performed on batches of several hundred samples with multiple loci. Future work is needed to optimize throughput while minimizing costs and to update existing microsatellite allele calling and analysis programs to accommodate sequence-aware microsatellite data.
KTU: K-mer Taxonomic Units improve the biological relevance of amplicon sequence variant microbiota data
<p>Testing datasets and files for the KTU algorithm</p>
16s rRNA sequences, R code used for amplicon analysis and example code for NMGS analysis
<p>This submission contains the following data presented in: "Selection processes of Arctic seasonal glacier snowpack bacterial communities" by Keuschnig et al.</p> <p>the R code used to analyze the 16S rRNA amplicon data</p> <p>the script used for NMGS analysis</p> <p>the sequences obtained from snow samples</p>
Data from: Parallel tagged amplicon sequencing reveals major lineages and phylogenetic structure in the North American tiger salamander (Ambystoma tigrinum) species complex
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Data from: Species tree estimation of North American chorus frogs (Hylidae: Pseudacris) with parallel tagged amplicon sequencing
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Allen Brain Atlas
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International Brain Laboratory public data
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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.