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13 results for “16S metabarcoding”
16S V4-V5 metabarcoding reference databases and weighted naive-bayes classifiers, dereplicated
<p>16S metabarcoding databases and naive-bayes classifiers specific to the V4-V5 region. Built from the <a href="https://www.arb-silva.de/documentation/release-138/">Silva 138.1 SSU Ref NR 99</a> database using Qiime2 (version 2023.2) and the <a href="https://github.com/BenKaehler/q2-clawback">q2-clawback plugin.</a> Includes weighted classifiers for two Earth Microbiome Project Ontology (EMPO) 3 habitat types: "sediment (saline)" and "water (saline)" , with data downloaded from <a href="https://qiita.ucsd.edu/">Qiita</a>. Sequences were dereplicated with Rescript --p-mode 'uniq' , retaining identical sequence records that have differing taxonomies.</p> <p>Primers used:</p> <p>EMP 16S 515f: GTGYCAGCMGCCGCGGTAA</p> <p>EMP 16S 926r: CCGYCAATTYMTTTRAGTTT</p> <p><strong>Stats</strong></p> <p>286,948 unique sequences</p> <p>309,567 total sequences</p> <p>46,254 unique taxa (Level 7)</p> <table> <caption>File description</caption> <thead> <tr> <th scope="col"> <table> <thead> <tr> <th>File</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>make new 16S silva V4-V5 database.md</td> <td>Markdown with code used to generate databases</td> </tr> <tr> <td>silva-138-99-seqs.qza</td> <td>Full length Silva 138.1 SSU 99 sequences</td> </tr> <tr> <td>silva-138-99-tax.qza</td> <td>Taxa for full length Silva 138.1 SSU 99 database</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-seqs.qza</td> <td>Sequences for 16S V4-V5 (primers 515f, 926r), extracted from Silva 138.1 SSU 99, generated by qiime2-2023.2 (forward compatible), dereplicated</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-taxa.qza</td> <td>Taxa for silva-138_1-99-515f_926r-seqs.qza database, dereplicated</td> </tr> <tr> <td>uniform-silva-138_1-99-515f_926r-uniq-classifier.qza</td> <td>Unweighted (uniform) naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-sediment-saline-classifier.qza</td> <td>Weighted naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, weighted for sediment-saline, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-q2_2023_2-uniq-sediment-saline-weights.qza</td> <td>Weights used to generate silva-138_1-99-515f_926r-q2_2023_2-sediment-saline-classifier.qza</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-water-saline-classifier.qza</td> <td>Weighted naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, weighted for water-saline, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-water-saline-weights.qza</td> <td>Weights used to generate silva-138_1-99-515f_926r-water-saline-classifier.qza</td> </tr> </tbody> </table> </th> <th scope="col"> </th> </tr> </thead> <tbody> </tbody> </table> <p> </p>
Row fastq files generated by 16S rRNA sequencing for the metabarcoding analysis of Hidalgo-Villeda et al. paper
<p>Fastq files used for the microbiome profiling of the terminal ileal and caecum content.</p> <p>Sequencing of the terminal ileal content was performed at the Institute Hospitalo-Universitaire Méditerannée Infection, Marseille, France.</p> <p>Sequencing of the caecum content was performed at Genoscreen, Lille, France</p> <p>The sequencing methodology is described on the methods part of the paper "A physiological mouse model of severe acute malnutrition reveals prolonged dysbiosis and altered immunity under nutritional intervention, Hidalgo-Villeda et al.".</p> <p>The metadata.xlsx table present the description and correspondance of each fastq files.</p>
Screening of bacterial diversity by 16S rRNA metabarcoding in Orbicella faveolata healthy and with Black Band Disease
<p>Screening of bacterial diversity by 16S rRNA metabarcoding in Orbicella faveolata healthy and with Black Band Disease</p>
16S metabarcoding tutorial
<p>Tutorial for the analysis of 16S rRNA metabarcoding sequences using the MiSeq SOP pipeline. https://github.com/AndresICM/16S-rRNA-Metabarcoding-analysis</p>
Supplementary material 1 from: Sogawa S, Tsuchiya K, Nagai S, Shimode S, Kuwahara VS (2022) Annual dynamics of eukaryotic and bacterial communities revealed by 18S and 16S rRNA metabarcoding in the coastal ecosystem of Sagami Bay, Japan. Metabarcoding and Metagenomics 6: e78181. https://doi.org/10.3897/mbmg.6.78181
Figures S1–S8
Supplementary material 4 from: Hintikka S, Carlsson JE, Carlsson J (2022) The bacterial hitchhiker's guide to COI: Universal primer-based COI capture probes fail to exclude bacterial DNA, but 16S capture leaves metazoa behind. Metabarcoding and Metagenomics 6: e80416. https://doi.org/10.3897/mbmg.6.80416
COI library ASV tax
Supplementary material 1 from: Hintikka S, Carlsson JE, Carlsson J (2022) The bacterial hitchhiker's guide to COI: Universal primer-based COI capture probes fail to exclude bacterial DNA, but 16S capture leaves metazoa behind. Metabarcoding and Metagenomics 6: e80416. https://doi.org/10.3897/mbmg.6.80416
File S1
Supplementary material 3 from: Hintikka S, Carlsson JE, Carlsson J (2022) The bacterial hitchhiker's guide to COI: Universal primer-based COI capture probes fail to exclude bacterial DNA, but 16S capture leaves metazoa behind. Metabarcoding and Metagenomics 6: e80416. https://doi.org/10.3897/mbmg.6.80416
16S library ASV tax
Supplementary material 5 from: Hintikka S, Carlsson JE, Carlsson J (2022) The bacterial hitchhiker's guide to COI: Universal primer-based COI capture probes fail to exclude bacterial DNA, but 16S capture leaves metazoa behind. Metabarcoding and Metagenomics 6: e80416. https://doi.org/10.3897/mbmg.6.80416
Unassigned COIASVs krona
Supplementary material 2 from: Hintikka S, Carlsson JE, Carlsson J (2022) The bacterial hitchhiker's guide to COI: Universal primer-based COI capture probes fail to exclude bacterial DNA, but 16S capture leaves metazoa behind. Metabarcoding and Metagenomics 6: e80416. https://doi.org/10.3897/mbmg.6.80416
Metadata all
16S metabarcoding, a powerful tool to compare environmental and culture-derived bacterial communities, assess media selectivity and detect rare environmental bacterial species
<p>Processed raw data reads</p>
Tara Pacific metabarcoding sequencing (16S, 18S, ITS2) reference & replication tables version 1
<p>The tables in this dataset associate metabarcoding sequencing files generated by Genoscope (i.e. fastq.gz files with informative filename structures; with sequencing file pairs associated to unique 'readset' identifiers; housed on their FTP server at www.genoscope.cns.fr/sadc/tarapacific/METABARCODING; with the 'METAB' sequence strategy identifier as part of the sequencing file name) with their associated 'sample-id_source' identifier; as detailed in the 'TARA-PACIFIC_samples-provenance' file part of the 'Tara Pacific samples provenance and environmental context' Zenodo publication (DOI: 10.5281/zenodo.4068292) part of the 'tarapacific' Zenodo community). The main purpose of these tables is to act as a reference to identify samples (i.e., with a single 'sample-id_source') for which sequencing replication exists, per primer set (i.e., more than one set of fastq.gz sequencing files were generated, pre primer set). In the case of replication, these tables classify the replication into three classes color coded as green (same DNA extraction, same PCR, same sequencing run, different sequencing lane), yellow (same DNA extraction, same PCR, different sequencing run) and red (different DNA extraction and/or different PCR). It should be noted that in the vast majority of cases a 'sample-id_source' associates to only one readset per primer set. For a full description of the dataset, please see the included README.</p>
Single cell rDNA 16S V4V5 and 18S V9 metabarcoding tables (dada2) from the Tara Oceans expedition (2009-2013)
<p>Single cell rDNA 16S V4V5 and 18S V9 metabarcoding tables (dada2) from the Tara Oceans expedition (2009-2013). The code to generate the ASV tables is here: https://gitlab.com/tara-and-friends-euk-metab/tara-single-cell-metab</p>
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International Brain Laboratory public data
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OpenNeuro
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