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27 results for “dada2”
Silva SSU taxonomic training data formatted for DADA2 (Silva version 138)
<p>These DADA2-formatted training fasta files were derived from the Silva Project's version 138 release. See https://www.arb-silva.de/documentation/release-138/ for database and citation information. The Silva 138 database is licensed under Creative Commons Attribution 4.0 (CC-BY 4.0); see file "SILVA_LICENSE.txt". The fasta files were generated and checked for consistency with version 132 using the R code in the R-markdown document "silva-v138.Rmd".</p> <p>Version 2 removes the dependence on preprocessed files from mothur, which results in a greater number of bacterial and archeal sequences. It also includes a new version of the assignTaxonomy training set that goes through the species level for use with longer amplicons obtained from long-read amplicon sequencing.</p> <p>If you use these files, please cite one or both of the Silva references below (or at the above link) and the DADA2 paper (reference below). I also recommend citing or linking to the Zenodo record for this specific version in your Methods or published source code to record the specific taxonomic database files used in your analysis.</p> <p><strong>NOTE:</strong><strong> </strong>These Version 2 files are intended for use in classifying prokaryotic 16S sequencing data and are not appropriate for classifying eukaryotic ASVs. The new method implemented within DADA2 for constructing these files only includes 100 eukaryotic sequences for use as an outgroup.</p> <p><strong>NOTE:</strong> These Version 2 files have a known problem in 10/883 families and 114/3838 genera. See https://github.com/mikemc/dada2-reference-databases/blob/main/silva-138/v2/bad-taxa.csv for a list of affected taxa and https://github.com/benjjneb/dada2/issues/1293 for more information.</p>
DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea
<p><strong><em>This version is to stay up to date with the improvements and increase in 16S rRNA gene sequences (SSU) added to the GTDB release 220. Please read this post for the stats on the updates. </em></strong><strong><em>https://gtdb.ecogenomic.org/stats/r220 </em></strong><strong><em>.</em></strong><strong><em> </em></strong></p> <p><strong><em>There has been no change to the RDP-RefSeq reference database please use previous versions.</em></strong></p> <p><strong><em>If anyone has concerns with MAG extracted 16S rRNA gene contamination concerns, then I suggest that they contact the curators of GTDB themselves because it is outside of my role with these resources designed for DADA2 usage only. </em></strong></p> <p><strong><em>Another concern that was raised was the orientation of the DB sequences, to get past this problem please use the tryRC = TRUE argument in the assignTaxonomy command within DADA2, this will search your ASVs in the reverse complement as well. </em></strong></p> <p>The bacterial and archaeal 16S rRNA gene sequence databases were collated from various sources and formatted to use the "assignTaxonomy" command within the DADA2 pipeline. The data was converted to suite DADA2 format by Alishum Ali.</p> <ol> <li>Genome Taxonomy Database (GTDB): The new version of our dada2 formatted GTDB reference sequences now contains 58102 bacteria and 3672 archaea full 16S rRNA gene sequences. If you wonder why there are fewer species with 16S rRNA, that is because some metagenomics-assembled genomes (MAGs) lack the 16S gene and thus cannot be extracted. The database was downloaded from <a href="https://data.ace.uq.edu.au/public/gtdb/data/releases/release95/">https://data.ace.uq.edu.au/public/gtdb/data/releases/</a> on 24/10/2024. Please read the release notes and file descriptions. </li> </ol> <p>The formatting to DADA2 was done using simple awk bash scripts. The script takes as input a fasta file and a tab-delimited taxonomy file (slightly edited to remove special characters) and then it outputs a fasta file with all 7 taxonomy ranks separated by ";" as required for DADA2 compatibility. Additionally, we have concatenated the unique sequence GTDB ID to the species entry (but replaced the "." with an " _". We see this as an important QC step to highlight the issues/confidence associated with short-read taxonomy assignment at the finer rank levels.</p> <p>Also, this update includes two other files that you can use with the assignTaxonomy and addSpecies commands in DADA2.</p>
DADA2 formatted eHOMD 16S rRNA gene sequences databse
<p>eHOMD Refseq database (V15.22) formated to be used with dada2 <em>i.e.</em>, dada2::assignTaxonomy(seqs, "eHOMD_RefSeq_dada2_V15.22.fasta.gz" ) and dada2::addSpecies(taxa, "eHOMD_RefSeq_dada2_assign_species_V15.22.fasta.gz", verbose=TRUE)</p> <p>Alternatively, you could use the metabaRpipe R package to directly update the taxonomy of a phyloseq object see: https://github.com/fconstancias/metabaRpipe#2-addingreplacing-taxonomical-table-in-a-phyloseq-object</p> <p>Example below:<br> source("https://raw.githubusercontent.com/fconstancias/metabaRpipe-source/master/Rscripts/functions.R")</p> <p>readRDS("dada2/phyloseq.RDS") %>%<br> phyloseq_dada2_tax(physeq = .,<br> threshold = 60,<br> db ="~/metabaRpipe/databases/eHOMD_RefSeq_dada2_V15.22.fasta.gz",<br> db_species ="~/metabaRpipe/databases/eHOMD_RefSeq_dada2_assign_species_V15.22.fasta.gz",<br> nthreads = 2,<br> full_return = FALSE) -> physeq_eHOMD_tax</p> <p> </p> <p> </p>
The tpm metabarcoding DNA sequence database for taxonomic allocations using RDP classifier implemented in DADA2.
<p><strong>The </strong><em>tpm</em><strong> metabarcoding DNA sequence database for taxonomic allocations using the Mothur and DADA2 bio-informatic tools</strong></p> <p>A.C.M. Pozzi<sup>1</sup>, R. Bouchali<sup>1</sup>, L. Marjolet<sup>1</sup>, B. Cournoyer<sup>1</sup></p> <p><sup>1 </sup><em>University of Lyon, UMR Ecologie Microbienne Lyon (LEM), CNRS 5557, INRAE 1418, Université Claude Bernard Lyon 1, VetAgro Sup, Research Team “Bacterial Opportunistic Pathogens and Environment” (BPOE), 69280 Marcy L’Etoile, France.</em></p> <p><strong>Corresponding authors: </strong></p> <ul> <li>A.C.M. Pozzi, UMR Microbial Ecology, CNRS 5557, CNRS 1418, VetAgro Sup, Main building, aisle 3, 1st floor, 69280 Marcy-L’Etoile, France. Tel. (+33) 478 87 39 47. Fax. (+33) 472 43 12 23. Email: <a href="mailto:adrien.meynier_pozzi@vetagro-sup.fr">adrien.meynier_pozzi@vetagro-sup.fr</a></li> <li>B. Cournoyer, UMR Microbial Ecology, CNRS 5557, CNRS 1418, VetAgro Sup, Main building, aisle 3, 1st floor, 69280 Marcy-L’Etoile, France. Tel. (+33) 478 87 56 47. Fax. (+33) 472 43 12 23. Email: and <a href="mailto:benoit.cournoyer@vetagro-sup.fr">benoit.cournoyer@vetagro-sup.fr</a></li> </ul> <p><strong>Keywords:</strong></p> <p>BACtpm, Bacteria, <em>tpm</em>, thiopurine-<em>S</em>-methyltransferase EC:2.1.1.67, Nucleotide sequences, PCR products, Next-Generation-Sequencing, OTHU</p> <p><strong>Description:</strong></p> <ul> <li>The <em>tpm</em> gene codes for the thiopurine-<em>S</em>-methyltransferase (TPMT), an enzyme that can detoxify metalloid-containing oxyanions and xenobiotics (Cournoyer et al., 1998). Bacterial TPMTs radiated apart from human and animal TPMTs, and showed a vertical evolution in line with the 16S rRNA gene molecular phylogeny (Favre‐Bonté et al., 2005).</li> <li>The <em>tpm</em> database, named BACtpm, was designed to apply the <em>tpm</em>-metabarcoding analytical scheme published in Aigle et al. (2021). It includes the full <em>tpm</em> identifiers, GenBank accession numbers, complete taxonomic records (domain down to strain code) of about 215 nucleotide-long <em>tpm</em> sequences of 840 unique taxa belonging to 139 genera.</li> <li>Nucleotide sequences of <em>tpm</em> (range: 190-233 nucleotides) were either retrieved from public repositories (GenBank) or made available by B. Cournoyer’s research group. Colin et al. (2020) described the PCR and high throughput Illumina Miseq DNA sequencing procedures used to produce <em>tpm</em> sequences.</li> <li>BACtpm v.2.0.1 (June 2021 release) is made available under the Creative Commons Attribution 4.0 International Licence. It can be used for the taxonomic allocations of <em>tpm </em>sequences down to the species and strain levels. Data is stored in the csv format enabling future user to reformat it to fit their specific needs.</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>We thank the worldwide community of microbiologists who made contributions to public databases in the past decades, and made possible the elaboration of the BACtpm database. We also thank the Field Observatory in Urban Hydrology (OTHU, <a href="http://www.graie.org/othu/">www.graie.org/othu/</a>), Labex IMU (Intelligence des Mondes Urbains), the Greater Lyon Urban Community, the School of Integrated Watershed Sciences H2O'LYON, and the Lyon Urban School for their support in the development of this database. This work was funded by the French national research program for environmental and occupational health of ANSES under the terms of project “Iouqmer” EST 2016/1/120, l'Agence Nationale de la Recherche through ANR-16-CE32-0006, ANR-17-CE04-0010, ANR-17-EURE-0018 and ANR-17-CONV-0004, by the MITI CNRS project named Urbamic, and the French water agency for the Rhône, Mediterranean and Corsica areas through the Desir and DOmic projects. We thank former BPOE lab members who contributed to start and expand the BACtpm database: Céline COLINON, Romain MARTI, Emilie BOURGEOIS, Sébastien RIBUN and Yannick COLIN.</p> <p><strong>References:</strong></p> <p>Aigle, A., Colin, Y., Bouchali, R., Bourgeois, E., Marti, R., Ribun, S., Marjolet, L., Pozzi, A.C.M., Misery, B., Colinon, C., Bernardin-Souibgui, C., Wiest, L., Blaha, D., Galia, W., Cournoyer, B., 2021. Spatio-temporal variations in chemical pollutants found among urban deposits match changes in thiopurine S-methyltransferase-harboring bacteria tracked by the tpm metabarcoding approach. Sci. Total Environ. 767, 145425. https://doi.org/10.1016/j.scitotenv.2021.145425</p> <p>Colin, Y., Bouchali, R., Marjolet, L., Marti, R., Vautrin, F., Voisin, J., Bourgeois, E., Rodriguez-Nava, V., Blaha, D., Winiarski, T., Mermillod-Blondin, F., Cournoyer, B., 2020. Coalescence of bacterial groups originating from urban runoffs and artificial infiltration systems among aquifer microbiomes. Hydrol. Earth Syst. Sci. 24, 4257–4273. https://doi.org/10.5194/hess-24-4257-2020</p> <p>Cournoyer, B., Watanabe, S., Vivian, A., 1998. A tellurite-resistance genetic determinant from phytopathogenic pseudomonads encodes a thiopurine methyltransferase: evidence of a widely-conserved family of methyltransferases1The International Collaboration (IC) accession number of the DNA sequence is L49178.1. Biochim. Biophys. Acta BBA - Gene Struct. Expr. 1397, 161–168. https://doi.org/10.1016/S0167-4781(98)00020-7</p> <p>Favre‐Bonté, S., Ranjard, L., Colinon, C., Prigent‐Combaret, C., Nazaret, S., Cournoyer, B., 2005. Freshwater selenium-methylating bacterial thiopurine methyltransferases: diversity and molecular phylogeny. Environ. Microbiol. 7, 153–164. https://doi.org/10.1111/j.1462-2920.2004.00670.x</p>
DADA2 formatted taxonomy from GTDBr95
<p>DADA2 requires taxonomy files in a specific format. This datasets are the files required to assign taxonomy using Genome Taxonomy Database (GTDB) database.</p> <p>GTDB release 95.0 is the latest version of database released on July 17th, 2020. GTDB-r95 contains 30,238 bacterial and 1,672 archaeal species clusters which span 194,600 genomes. FASTA file of 16S rRNA gene sequences identified within the representative genomes of bacteria (21965) and Archaea (1126) were downloaded from this <a href="https://data.ace.uq.edu.au/public/gtdb/data/releases/release95/95.0/genomic_files_reps/">link</a> on 24-12-2020. The link provides resources for GTDB species representatives hence, limiting one sequence per organism. The sequence headers were modified according to DADA2 requirements using regular expression based replace in Notepad++ (I was too lazy to do the same through awk/sed). </p> <p>Files <a href="https://zenodo.org/api/files/8b1de4c7-ce83-488b-b399-cc27dbadfaed/GTDBr95-Genus.fna">GTDBr95-Genus.fna</a> and <a href="https://zenodo.org/api/files/8b1de4c7-ce83-488b-b399-cc27dbadfaed/GTDBr95-Species.fna">GTDBr95-Species.fna</a> are to be used with assignTaxonomy and addSpecies commands of DADA2, respectively.</p> <p>Prepared files were checked and found compatible when run on DADA2 v1.14.0 (R v3.6.3).</p>
ASV Tables inferred by DADA2 from the TARA Oceans v9 metabarcoding dataset
<p>Tables of exact amplicon sequence variants (ASVs) were generated from the TARA Oceans metabarcoding data (~766 million reads from 334 plankton samples, V9 region of the 18S rRNA gene, DOI: 10.5281/zenodo.15600) by DADA2 on a 2016 Macbook Pro. The R script used to process the data is included, alongside 3 ASV tables: the observed ASVs before chimera removal (sta), after consensus chimera removal (st.consensus, recommended) and after pooled chimera removal (st.nochim).</p> <p>The ASV tables are available in two formats. The first format is as matrices (rows named by sample, columns named by sequence variant) stored in RDS format -- these can be read back into R with the readRDS command. The second format is as biom-format files (json).</p>
Silva taxonomic training data formatted for DADA2 (Silva version 138.2)
<p>These DADA2-formatted training fasta files were derived from the Silva Project's version 138.2 release: https://www.arb-silva.de/</p> <p>These fastas were generated by the following commands (using the dada2 R package version 1.35.4):</p> <blockquote> <p>path <- "~/tax/Silva/v138_2"<br>fn.out.slv <- "~/Desktop/silva_nr99_v138.2_toGenus_trainset.fa.gz"<br>dada2:::makeTaxonomyFasta_SilvaNR(file.path(path, "SILVA_138.2_SSURef_NR99_tax_silva.fasta.gz"), <br> file.path(path, "tax_slv_ssu_138.2.txt"), <br> fn.out.slv)<br> <br>fn.out.spc.slv <- "~/Desktop/silva_nr99_v138.2_toSpecies_trainset.fa.gz"<br>dada2:::makeTaxonomyFasta_SilvaNR(file.path(path, "SILVA_138.2_SSURef_NR99_tax_silva.fasta.gz"), <br> file.path(path, "tax_slv_ssu_138.2.txt"), <br> fn.out.spc.slv, include.species=TRUE)</p> <p>fn.out.aS.slv <- "~/Desktop/silva_v138.2_assignSpecies.fa.gz"<br>dada2:::makeSpeciesFasta_Silva("~/tax/silva/v138_2/SILVA_138.2_SSURef_tax_silva.fasta.gz", <br> fn.out.aS.slv)</p> </blockquote>
Greengenes2 training data formatted for DADA2 (Greengenes2 release version 2024.09)
<p>These DADA2-formatted training fasta files were derived from the Greengenes2 version 2024.09 release. https://ftp.microbio.me/greengenes_release/2024.09/</p> <p>These fastas were generated by the following commands (using the dada2 R package version 1.35.4):</p> <blockquote> <p>path <- "~/tax/GG2/2024_09"<br>fn <- file.path(path, "5b42d9b6-2f24-4f01-b989-9b4dafca7d5e/data/dna-sequences.fasta")<br>txfn <- file.path(path, "b7c3e691-ea51-4547-94dd-f79f49e41a36/data/taxonomy.tsv")</p> <p>fn.out.gg <- "~/Desktop/gg2_2024_09_toGenus_trainset.fa.gz"<br>dada2:::makeTaxonomyFasta_GG2(fn, txfn, fn.out.gg, include.species=FALSE, compress=TRUE)</p> <p>fn.out.spc.gg <- "~/Desktop/gg2_2024_09_toSpecies_trainset.fa.gz"<br>dada2:::makeTaxonomyFasta_GG2(fn, txfn, fn.out.spc.gg, include.species=TRUE, compress=TRUE)</p> </blockquote>
RDP taxonomic training data formatted for DADA2 (RDP release 19 - update 2023-08-23)
<p>These DADA2-formatted training fasta files were derived from the Ribosomal Database Project's Training Set 19 and the 2023-08-23 release of the RDP database. https://sourceforge.net/projects/rdp-classifier/files/RDP_Classifier_TrainingData/</p> <p>These fastas were generated by the following commands using the dada2 R package version 1.35.4:</p> <blockquote> <p>## RDP data: https://sourceforge.net/projects/rdp-classifier/files/RDP_Classifier_TrainingData/</p> <p>path <- "~/tax/rdp/v19"<br>fn.out.rdp <- "~/Desktop/rdp_19_toGenus_trainset.fa.gz"<br>dada2:::makeTaxonomyFasta_RDP(file.path(path, "trainset19_072023_speciesrank.fa"), <br> file.path(path, "trainset19_db_taxid.txt"), <br> fn.out.rdp, include.species=FALSE,<br> compress=TRUE)</p> <p>fn.out.spc.rdp <- "~/Desktop/rdp_19_toSpecies_trainset.fa.gz"<br>dada2:::makeTaxonomyFasta_RDP(file.path(path, "trainset19_072023_speciesrank.fa"), <br> file.path(path, "trainset19_db_taxid.txt"), <br> fn.out.spc.rdp, include.species=TRUE,<br> compress=TRUE)</p> </blockquote>
SILVA_123 Eukaryota taxonomic training data formatted for DADA2; plus version with (uncurrated) additional sequences
<p>This is the Silva123 taxonomy reformatted to work with eukaryote sequences for dada2:</p> <p><a href="https://zenodo.org/api/files/4f873b5b-fe02-4344-8c8b-c723c3caba67/SILVA_123_dada2.fasta">SILVA_123_dada2.fasta </a></p> <p>It has been created using a script available <a href="https://github.com/derele/AA_Hyena/blob/master/R/convert_silva_taxonomy.r">here</a>.</p> <p>To increase coverage (Silva has a low coverage for eukaryotes). Uncurrated additional sequences similar to ASVs found in the intestine of hyenas (BLAST) have been added. The script used for taxonomic annotation of these sequences is available <a href="https://github.com/derele/AA_Hyena/blob/master/scripts/blast2alltax_outfmt11.pl">here</a>. The file containing these additional (uncurrated!) sequences is:</p> <p><a href="https://zenodo.org/api/files/4f873b5b-fe02-4344-8c8b-c723c3caba67/SILVA_123_dada2_exp.fasta?versionId=78a7526a-72e0-4707-ab5c-297671b5ff13">SILVA_123_dada2_exp.fasta </a></p> <p>This expanded file has been used for taxonomic annotation in: </p> <p><a href="https://doi.org/10.3389/fcimb.2017.00262">Heitlinger, E., Ferreira, S., Thierer, D., Hofer, H., & East, M. L. (2017). The intestinal eukaryotic and bacterial biome of spotted hyenas: the impact of social status and age on diversity and composition. <em>Frontiers in cellular and infection microbiology</em>, <em>7</em>, 262.</a></p>
Tara Oceans (2009-2013) rDNA 18S V9 ASV table (DADA2) with nf-core/ampliseq
<p>This repository contains datasets describing the DADA2 ASVs generated from <em>Tara</em> Oceans 18S V9 rDNA data. The ASVs were generated using the nf-core workflow <a href="https://nf-co.re/ampliseq" target="_blank" rel="noopener">ampliseq</a>. Please refer to the readme file (README.html) for more details.</p>
Tara Oceans (2009-2013) rDNA 18S V4 ASV table (DADA2) with nf-core/ampliseq
<p>This repository contains datasets describing the DADA2 ASVs generated from <em>Tara</em> Oceans 18S V4 rDNA data. The ASVs were generated using the nf-core workflow <a href="https://nf-co.re/ampliseq" target="_blank" rel="noopener">ampliseq</a>. Please refer to the readme file (README.html) for more details.</p>
The RDP and GreenGenes taxonomic training sets formatted for DADA2
<p>These DADA2-formatted training fastas were derived from the Ribosomal Database Project (RDP Training Set 14) and the GreenGenes Database Consortium (Version 13.8).</p>
HITdb v1.00 for Dada2
<p>HITdb is a reference taxonomy for Human <strong>Intestinal</strong> 16S rRNA genes as described in</p> <blockquote> <p>Ritari J, Salojärvi J, Lahti L & de Vos WM. Improved taxonomic assignment of human intestinal 16S rRNA sequences by a dedicated reference database. BMC Genomics. 2015 Dec 12;16(1):1056. doi: 10.1186/s12864-015-2265-y.</p> </blockquote> <p>This is version converted to be used with dada2 (http://benjjneb.github.io/dada2). After extracting your sequence table you can use HITdb via:</p> <p>taxa_hdb <- assignTaxonomy(seqtab_nochim, "hitdb_v1.00.fa.gz")<br> colnames(taxa_hdb) <- c("Phylum", "Class", "Order", "Family", "Genus", "Species")</p> <p>Please be aware that HITdb is specific for intestinal samples, thus it might give you arbitrarily wrong results for non-intestinal samples.<br> </p>
RDP taxonomic training data formatted for DADA2 (RDP trainset 16/release 11.5)
<p>These DADA2-formatted training fasta files were derived from the Ribosomal Database Project's Training Set 16 and the 11.5 release of the RDP database.</p> <p>These fastas were generated by the following commands (using the dada2 R package version 1.5.1):</p> <blockquote> <p>path <- "~/Desktop/RDP/RDPClassifier_16S_trainsetNo16_rawtrainingdata"<br> dada2:::makeTaxonomyFasta_RDP(file.path(path, "trainset16_022016.fa"), file.path(path, "trainset16_db_taxid.txt"), "~/tax/rdp_train_set_16.fa.gz")</p> <p>dada2:::makeSpeciesFasta_RDP("~/Desktop/RDP/current_Bacteria_unaligned.fa", "~/tax/rdp_species_assignment_16.fa.gz")</p> </blockquote>
RDP LSU taxonomic training data formatted for DADA2 (trainingset 11)
<p>#Format RDP taxonomic training set for DADA2<br> #1 Wrangle the RDP trainingsets and unaligned data into the downloads folder by executing this from a terminal and move the file somewhere with >30GB free<br> wget https://sourceforge.net/projects/rdp-classifier/files/RDP_Classifier_TrainingData/RDPClassifier_fungiLSU_trainsetNo11_rawtrainingdata.zip/download<br> wget http://rdp.cme.msu.edu/download/current_Fungi_unaligned.fa.gz<br> #2 Unzip the trainingset file and replace Us with Ts in the fasta by executing in terminal<br> awk 'NR%2==0 {gsub(/[uU]/,"T"); print} NR%2==1' /media/lauren/96BA-19E6/RDPClassifier_fungiLSU_trainsetNo11_rawtrainingdata/fungiLSU_train_012014.fa > /media/lauren/96BA-19E6/RDPClassifier_fungiLSU_trainsetNo11_rawtrainingdata/fungiLSU_train_012014_lsu_fixed_v2.fa<br> #3 Summon the dada2 pkg<br> library(dada2);packageVersion("dada2")<br> #4 Transform the DADA2 formatted training fastas<br> path<-"/media/lauren/96BA-19E6/RDPClassifier_fungiLSU_trainsetNo11_rawtrainingdata"<br> dada2:::makeTaxonomyFasta_RDP(file.path(path, "fungiLSU_train_012014_lsu_fixed_v2.fa"), file.path(path, "fungiLSU_taxid_012014.txt"),"/media/lauren/96BA-19E6/Upload/RDP_LSU_fixed_train_set_v2.fa",compress=FALSE)<br> dada2:::makeSpeciesFasta_RDP("/media/lauren/96BA-19E6/RDPClassifierLSU/current_Fungi_unaligned.fa", "/media/lauren/96BA-19E6/Upload/rdp_species_assignment_LSU_v2.fa", compress=FALSE)</p> <p>#5 Make the compressed DADA2 formatted training fastas in gz and zip format<br> dada2:::makeTaxonomyFasta_RDP("/media/lauren/96BA-19E6/RDPClassifier_fungiLSU_trainsetNo11_rawtrainingdata/fungiLSU_train_012014_lsu_fixed_v2.fa", file.path(path, "fungiLSU_taxid_012014.txt"),"/media/lauren/96BA-19E6/Upload/RDP_LSU_fixed_train_set_v2.fa.gz",compress=TRUE)<br> dada2:::makeTaxonomyFasta_RDP("/media/lauren/96BA-19E6/RDPClassifier_fungiLSU_trainsetNo11_rawtrainingdata/fungiLSU_train_012014_lsu_fixed_v2.fa", file.path(path, "fungiLSU_taxid_012014.txt"),"/media/lauren/96BA-19E6/Upload/RDP_LSU_fixed_train_set_v2.fa.zip",compress=TRUE)<br> dada2:::makeSpeciesFasta_RDP("/media/lauren/96BA-19E6/RDPClassifierLSU/current_Fungi_unaligned.fa", "/media/lauren/96BA-19E6/Upload/rdp_species_assignment_LSU_v2.fa.gz", compress=TRUE)<br> dada2:::makeSpeciesFasta_RDP("/media/lauren/96BA-19E6/RDPClassifierLSU/current_Fungi_unaligned.fa", "/media/lauren/96BA-19E6/Upload/rdp_species_assignment_LSU_v2.fa.zip", compress=TRUE)</p> <p> </p>
SILVA v128 and v132 dada2 formatted 18s 'train sets'
<p>These are species-level taxonomy classification training sets for the assignTaxonomy function from the dada2 R package.</p> <p>The v132 training set includes every Eukaryotic organism from SILVA's v132 database, clustered at 99% similarity.</p> <p>The v128 training set includes every Eukaryotic organism from SILVA's v128 database, clustered at 99% similarity. Additionally, it includes corrected species labels for the Blastocystis clade, and 37 Entamoeba sequences sourced from GenBank not present in the original v128 db. The v128 training set is modified specifically to allow for better species-level assignments for those two clades in mammalian gut microbiome studies.</p>
DADA2 formatted Silva SSU taxonomic training data (Silva version 138.1) with emended description of the genus Lactobacillus Beijerinck 1901
<p>These training fasta files are derived from the Silva 138.1 prokaryotic SSU taxonomic training data formatted for DADA2 (from <a href="https://zenodo.org/record/4587955">https://zenodo.org/record/4587955</a>). The species assignment file contains changes in species names according to <a href="https://doi.org/10.1099/ijsem.0.004107">Zheng et al. 2020</a> based on data from <a href="https://github.com/swuyts/lactotax/tree/master">Lactotax</a> (file <a href="https://github.com/swuyts/lactotax/raw/master/data/2023_05_30.xlsx">2023_05_30.xlsx</a>). The script in R for making changes in species names is in the silva.R file.</p> <p><br> Please cite one or both of the Silva references, the DADA2 paper (reference below), and the Zenodo record for this specific version in your Methods or published source code to record the specific taxonomic database files used in your analysis.</p>
SOMLIT-Astan time-series (2009-2016) rDNA 18S V4 ASV table (dada2)
<p>This repository contains a rDNA 18S V4 ASV table (astan-18sv4_dada2_v1.0.filtered.table.with.taxo.lulu.tsv.gz) for SOMLIT-Astan time-series (2009-2016). 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>sequence</strong> = ASV nucleic acid sequence; <strong>total</strong> = total number of reads for the entire dataset; <strong>spread</strong> = number of samples in which the ASV is detected; <strong>RAXXXXXX-X</strong> = number of reads in each of the 375 SOMLIT-Astan time-series samples. Sample ids contain information about the sampling date and the size fraction. The six digits after RA indicate the date (year, month and day), and the value after - indicate the size fraction, 02 for 0.2 to 3 µm and 3 for superior to 3 µm.</p> <p>How this table has been generated:</p> <p>The procedures used for DNA extraction and amplification of the 18S V4 region of the ribosomal operon are described in <a href="https://doi.org/10.1111/mec.16539">https://doi.org/10.1111/mec.16539</a>. The eukaryote-specific primers used were TAReuk454FWD1 (5’-CCAGCASCYGCGGTAATTCC-3’, Saccharomyces cerevisiae position 565‐584) and TAReukREV3 (5’-ACTTTCGTTCTTGATYRA-3’, Saccharomyces cerevisiae position 964‐981) (Stoeck et al., 2010). Raw sequences are available at the European Nucleotide Archive (ENA) under the project id PRJEB48571.</p> <p>The paired-end fastq files obtained from sequencing were demultiplexed and primers were removed using Cutadapt v2.8, filtering out untrimmed reads. Then, forward and reverse reads were trimmed at position 210 and 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 version 1.22 with R version 4.1.1 . For each run, error rates were defined using the function learnErrors(), reads were dereplicated using the function derepFastq() function and denoised using the dada() function with default options before being merged. Remaining chimaeras were removed using the function removeBimeraDenovo(). Only amplicon sequence variants (ASVs) with at least three reads in two samples were retained. ASVs were taxonomically assigned using IDTAXA with default parameters with the PR2 database version 4.14. Finally, the LULU curation approach was applied to the ASV table to remove remaining erroneous amplicons. For more details relative to the bioinformatic pipeline used to generate the ASV tables, see <a href="https://gitlab.sb-roscoff.fr/nhenry/rosko-naples-bioinfo">https://gitlab.sb-roscoff.fr/nhenry/rosko-naples-bioinfo</a>.</p>
RDP taxonomic training data formatted for DADA2 (RDP trainset 18/release 11.5)
<p>These DADA2-formatted training fasta files were derived from the Ribosomal Database Project's Training Set 18 and the 11.5 release of the RDP database.</p> <p>These fastas were generated by the following commands (using the dada2 R package version 1.19.1):</p> <blockquote> <p>## The RDP trainset data was downloaded from: https://sourceforge.net/projects/rdp-classifier/files/RDP_Classifier_TrainingData/<br> path <- "~/Desktop/RDP/RDPClassifier_16S_trainsetNo18_rawtrainingdata"<br> dada2:::makeTaxonomyFasta_RDP(file.path(path, "trainset18_062020.fa"), <br> file.path(path, "trainset18_db_taxid.txt"), <br> "~/tax/rdp_train_set_18.fa.gz")<br> ## Download "ten_16s.100.fa" from Robert Edgar's taxonomy testing page: https://drive5.com/taxxi/doc/fasta_index.html<br> dada2:::tax.check("~/tax/rdp_train_set_18.fa.gz", "~/Desktop/ten_16s.100.fa")</p> <p>## This function creates the dada2 assignSpecies fasta file for the RDP from the RDP's _Bacteria_unaligned.fa file.<br> dada2:::makeSpeciesFasta_RDP("~/Desktop/RDP/current_Bacteria_unaligned.fa", "~/tax/rdp_species_assignment_18.fa.gz")<br> dada2:::tax.check("~/tax/rdp_species_assignment_18.fa.gz", "~/Desktop/ten_16s.100.fa", mode="species")</p> </blockquote>
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