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Dataset results
27 results for “DADA2”
The Silva taxonomic training set formatted for DADA2
<p>These DADA2-formatted training fastas were derived from Silva version 123. They are available under the Silva dual-licensing model for academia and commercial users: https://www.arb-silva.de/silva-license-information/</p>
Silva taxonomic training data formatted for DADA2 (Silva version 128)
<p>These DADA2-formatted training fasta files were derived from the Silva Project's version 128 release. They are available under the Silva dual-licensing model for academia and commercial users: https://www.arb-silva.de/silva-license-information/</p> <p>These fastas were generated by the following commands (using the dada2 R package version 1.5.2):</p> <blockquote> <p>path <- "~/Desktop/Silva/Silva.nr_v128"<br> dada2:::makeTaxonomyFasta_Silva(file.path(path, "silva.nr_v128.align"), file.path(path, "silva.nr_v128.tax"), "~/tax/silva_nr_v128_train_set.fa.gz")</p> <p>dada2:::makeSpeciesFasta_Silva("~/Desktop/Silva/SILVA_128_SSURef_tax_silva.fasta.gz", "~/tax/silva_species_assignment_v128.fa.gz")</p> </blockquote> <p>Changes in Version 2: A typo in the genus name Escherichia was corrected. </p>
Silva taxonomic training data formatted for DADA2 (Silva version 132)
<p>These DADA2-formatted training fasta files were derived from the Silva Project's version 132 release. They are available under the Silva dual-licensing model for academia and commercial users: https://www.arb-silva.de/silva-license-information/</p> <p>These fastas were generated by the following commands (using the dada2 R package version 1.7.6):</p> <blockquote> <p>path <- "~/Desktop/Silva/Silva.nr_v132"<br> dada2:::makeTaxonomyFasta_Silva(file.path(path, "silva.nr_v132.align"), file.path(path, "silva.nr_v132.tax"), "~/tax/silva_nr_v132_train_set.fa.gz")</p> <p>dada2:::makeSpeciesFasta_Silva("~/Desktop/Silva/SILVA_132_SSURef_tax_silva.fasta.gz", "~/tax/silva_species_assignment_v132.fa.gz")</p> </blockquote>
rDNA 18S V9 ASVs (DADA2) from the Tara Pacific Expedition
<p>This repository contains datasets describing the DADA2 ASVs generated from the <em>Tara</em> Pacific expedition V9 18S rDNA metabarcoding sequencing data. The ASVs were generated were generated using the nf-core workflow ampliseq. Please refer to the readme file for more details.</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>
Expression data from Saccharomyces cerevisiae Dnup60, Dada2, and Dnup60 Dada2 cells
GEO Series GSE21870. Saccharomyces cerevisiae; Schizosaccharomyces pombe. 12 samples. Type: Expression profiling by array.
Tara Oceans (2009-2013) rDNA 16S V4V5 ASV table (dada2)
<p>This repository contains a rDNA 16S V4V5 ASV table (TARA-Oceans_16S-V4V5_dada2_table.tsv.gz) for <em>Tara</em> Oceans (2009-2013).</p> <p>In the file <strong>TARA-Oceans_16S-V4V5_dada2_table.tsv.gz</strong>, each ASV, one per row, is described by the following fields: <strong>amplicon</strong> = ASV identifier; <strong>taxonomy</strong> = taxonomic path assigned to the ASV using IDTAXA; <strong>confidence</strong> = IDTAXA confidence scores for each taxonomic rank; <strong>total</strong> = total number of reads for the entire dataset; <strong>spread</strong> = number of samples in which the ASV is detected; <strong>sequence</strong> = ASV nucleic acid sequence; <strong>TARA_XXXXXXXXXX</strong> = number of reads in each of the 1,134 <em>Tara</em> Oceans samples.</p> <p>Detailed information about DNA extraction, PCR amplification and Illumina sequencing of metabarcodes, as well as subsequent sequence data cleaning and taxonomic assignment can be found in (Alberti et al. 2017). Briefly, DNA samples were amplified by PCR targeting the hypervariable region V4V5 (primer pair: 15F-Y 5′- GTGYCAGCMGCCGCGGTAA-3′ and 926R 5′- CCGYCAATTYMTTTRAGTTT-3′ 16S primers; (Parada et al. 2016)) of the 16S rRNA marker gene followed by the high-throughput sequencing of the amplicons. The details of PCR mixes, thermocycling and sequencing conditions are provided in Alberti et al. (2017).</p> <p>Resulting paired-end reads were mixed-oriented meaning that both R1 and R2 files are composed by a mix of forward and reverse reads. Paired-end reads were trimmed to remove PCR primer sequences using Cutadapt v2.7 (Martin, 2011) and dispatched into four files, 2 files for the classical orientation (forward reads in R1 and reverse reads in R2) and 2 others for the other orientation (reverse reads in R1 and forward reads in R2). Paired-end reads without both primers were filtered out using the option --discard-untrimmed. Forward and reverse reads were trimmed at position 215 and 190 for R1 and R2 files respectively. Reads with ambiguous nucleotides or with a maximum number of expected errors (maxEE) superior to 2 were filtered out using the function filterAndTrim() from the R package dada2 (Callahan et al., 2016). For each run and read orientation, error rates were defined using the function learnErrors() and denoised using the dada() function with pool = TRUE before being merged using mergePairs() with default parameters. Mixed orientated reads from the same sample and sequencing replicates were summed together. Remaining chimeras were removed using the function removeBimeraDenovo(). Scripts producing the ASV table are publicly available here: https://gitlab.univ-nantes.fr/combi-ls2n/taradada.</p> <p>ASVs were taxonomically assigned using IDTAXA (50% confidence threshold) (Murali, Bhargava, and Wright 2018) with SILVA v138.</p> <p>Alberti, A., Poulain, J., Engelen, S., Labadie, K., Romac, S., Ferrera, I., Albini, G., Aury, J.-M., Belser, C., Bertrand, A., Cruaud, C., Da Silva, C., Dossat, C., Gavory, F., Gas, S., Guy, J., Haquelle, M., Jacoby, E., Jaillon, O., Lemainque, A., Pelletier, E., Samson, G., Wessner, M., Genoscope Technical Team, Bazire, P., Beluche, O., Bertrand, L., Besnard-Gonnet, M., Bordelais, I., Boutard, M., Dubois, M., Dumont, C., Ettedgui, E., Fernandez, P., Garcia, E., Aiach, N.G., Guerin, T., Hamon, C., Brun, E., Lebled, S., Lenoble, P., Louesse, C., Mahieu, E., Mairey, B., Martins, N., Megret, C., Milani, C., Muanga, J., Orvain, C., Payen, E., Perroud, P., Petit, E., Robert, D., Ronsin, M., Vacherie, B., Acinas, S.G., Royo-Llonch, M., Cornejo-Castillo, F.M., Logares, R., Fernández-Gómez, B., Bowler, C., Cochrane, G., Amid, C., Hoopen, P.T., De Vargas, C., Grimsley, N., Desgranges, E., Kandels-Lewis, S., Ogata, H., Poulton, N., Sieracki, M.E., Stepanauskas, R., Sullivan, M.B., Brum, J.R., Duhaime, M.B., Poulos, B.T., Hurwitz, B.L., Tara Oceans Consortium Coordinators, Acinas, S.G., Bork, P., Boss, E., Bowler, C., De Vargas, C., Follows, M., Gorsky, G., Grimsley, N., Hingamp, P., Iudicone, D., Jaillon, O., Kandels-Lewis, S., Karp-Boss, L., Karsenti, E., Not, F., Ogata, H., Pesant, S., Raes, J., Sardet, C., Sieracki, M.E., Speich, S., Stemmann, L., Sullivan, M.B., Sunagawa, S., Wincker, P., Pesant, S., Karsenti, E., Wincker, P., 2017. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci Data 4, 170093. <a href="https://doi.org/10.1038/sdata.2017.93">https://doi.org/10.1038/sdata.2017.93</a></p> <p>Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583. <a href="https://doi.org/10.1038/nmeth.3869">https://doi.org/10.1038/nmeth.3869</a></p> <p>Martin, M., 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet j. 17, 10. <a href="https://doi.org/10.14806/ej.17.1.200">https://doi.org/10.14806/ej.17.1.200</a></p> <p>Murali, A., Bhargava, A., Wright, E.S., 2018. IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences. Microbiome 6, 140. <a href="https://doi.org/10.1186/s40168-018-0521-5">https://doi.org/10.1186/s40168-018-0521-5</a></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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