A database which provides ribosome related data services to the scientific community, including online data analysis, rRNA derived phylogenetic trees, and aligned and annotated rRNA sequences. It specifically contains information on quality-controlled, aligned and annotated bacterial and archaean 16S rRNA sequences, fungal 28S rRNA sequences, and a suite of analysis tools for the scientific community. Most of the RDP tools are now available as open source packages for users to incorporate in their local workflow.

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/

These fastas were generated by the following commands using the dada2 R package version 1.35.4:

## RDP data: https://sourceforge.net/projects/rdp-classifier/files/RDP_Classifier_TrainingData/

path <- "~/tax/rdp/v19"
fn.out.rdp <- "~/Desktop/rdp_19_toGenus_trainset.fa.gz"
dada2:::makeTaxonomyFasta_RDP(file.path(path, "trainset19_072023_speciesrank.fa"),
file.path(path, "trainset19_db_taxid.txt"),
fn.out.rdp, include.species=FALSE,
compress=TRUE)

fn.out.spc.rdp <- "~/Desktop/rdp_19_toSpecies_trainset.fa.gz"
dada2:::makeTaxonomyFasta_RDP(file.path(path, "trainset19_072023_speciesrank.fa"),
file.path(path, "trainset19_db_taxid.txt"),
fn.out.spc.rdp, include.species=TRUE,
compress=TRUE)

RDP ver 11.5 database formatted for mg_classifier

RDP database dereplicated, only the V3-V4 region. For use with mg_classifier; metagenomic sequence classifier (https://github.com/GenomicaMicrob/mg_classifier).

I used a machine with Ubuntu 16.04 and DADA2 (vsn 1.5.2) to coax the Ribosomal Database Project's Training set 11 and the current release of the RDP database (for the unaligned fasta) into a DADA2 compatible format. If you use this, please cite the original sources too!

1 Wrangle the RDP trainingsets and unaligned data into the downloads folder either by manually downloading them or by executing this from a terminal and move the file somewhere with <30GB free

I used wget in a terminal: wget https://sourceforge.net/projects/rdp-classifier/files/RDP_Classifier_TrainingData/RDPClassifier_fungiLSU_trainsetNo11_rawtrainingdata.zip/download
wget http://rdp.cme.msu.edu/download/current_Fungi_unaligned.fa.gz

2 Unzip the trainingset file and replace Us with Ts in the fasta by executing in terminal

awk 'NR%2==0 {gsub(/[uU]/,"T"); print} NR%2==1' /media/lauren/96BA-19E6/fungallsu/RDPClassifier_fungiLSU_trainsetNo11_rawtrainingdata/fungiLSU_train_012014.fa > /media/lauren/96BA-19E6/fungallsu/RDPClassifier_fungiLSU_trainsetNo11_rawtrainingdata/fungiLSU_train_012014_lsu_fixed.fa

3 In R.Studio, summon the dada2 pkg

library(dada2);packageVersion("dada2")

4 Transform the RDP formatted training fastas

path<-"~"#change this to where your files sit on your computer dada2:::makeTaxonomyFasta_RDP(file.path(path, "fungiLSU_train_012014_lsu_fixed.fa"), file.path(path, "fungiLSU_taxid_012014.txt"),"fungiLSU_trainset_012014.fa.gz") dada2:::makeSpeciesFasta_RDP("current_Fungi_unaligned.fa", "rdp_species_assignment_LSU.fa")

This dataset contains a list of external partners sharing data with the City of New York through the Recovery Data Partnership (RDP). RDP is a first-of-its-kind effort for community, non-profit, and private organizations to share data with the City to aid in Covid-19 response and recovery efforts. For more information, visit nyc.gov/recoverydata.

These two combined bacterial and archaeal 16S rRNA gene sequence databases were collated from various sources and formatted for the purpose of using the "assignTaxonomy" command within the DADA2 pipeline.

1. RefSeq+RDP: This database contains 14676 bacterial & 660 archaea full 16S rRNA gene sequences. It was compiled in 14/05/2018 from predominantly the NCBI RefSeq 16S rrna database (https://www.ncbi.nlm.nih.gov/refseq/targetedloci/16S_process/) and was supplemented with extra sequences from the RDP database (https://rdp.cme.msu.edu/misc/resources.jsp).
2. Genome Taxonomy Database (GTDB): our dada2 formatted GTDB reference sequence set contains 20486 bacteria and 1073 archaea full 16S rRNA gene sequences. The database was downloaded from (http://gtdb.ecogenomic.org/downloads) on 20/11/2018.

The formatting to DADA2 format of the databases was done using a locally written python 2.7 script. The script takes as input a taxonomy .txt file and a fasta file as provided by the core databases creators and then these two files are matched according to a unique sequence identifier available in both files. 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 ID be it NCBI/RDP or GTDB ID to the species entry. We see this as an important QC step to highlight the issues/confidence associated with short read taxonomy assignment at the more finer rank levels.

RDP database .qza file for use with Qiime2's feature-classifier command. Created from https://figshare.com/s/7881ff1724948e513162 & https://figshare.com/s/86d9b343729e4c67ea08See https://forum.qiime2.org/t/rdp-reference-database-in-qiime2-format/15146/10 for more.

The tpm metabarcoding DNA sequence database for taxonomic allocations using the Mothur and DADA2 bio-informatic tools

A.C.M. Pozzi1, R. Bouchali1, L. Marjolet1, B. Cournoyer1

1 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.

Corresponding authors:

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: adrien.meynier_pozzi@vetagro-sup.fr

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 benoit.cournoyer@vetagro-sup.fr

Keywords:

BACtpm, Bacteria, tpm, thiopurine-S-methyltransferase EC:2.1.1.67, Nucleotide sequences, PCR products, Next-Generation-Sequencing, OTHU

Description:

The tpm gene codes for the thiopurine-S-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).

The tpm database, named BACtpm, was designed to apply the tpm-metabarcoding analytical scheme published in Aigle et al. (2021). It includes the full tpm identifiers, GenBank accession numbers, complete taxonomic records (domain down to strain code) of about 215 nucleotide-long tpm sequences of 840 unique taxa belonging to 139 genera.

Nucleotide sequences of tpm (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 tpm sequences.

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 tpm 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.

Acknowledgments:

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, www.graie.org/othu/), 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.

References:

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

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

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

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

Degeneracy code: R = A/G, Y = C/T, W = A/T, H = A/C/T.Phylum and order level coverage for bacteria and archaea are provided in Figure S1.

880 Global export shipment records of Rdp with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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180 Global import shipment records of Rdp with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

39 Global import shipment records of Rdp with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Brazil Sales: Steel: Re-rolled, Drawing Plants (RDP) data was reported at 55,837,807.130 BRL th in 2022. This records an increase from the previous number of 53,375,902.804 BRL th for 2021. Brazil Sales: Steel: Re-rolled, Drawing Plants (RDP) data is updated yearly, averaging 10,186,723.061 BRL th from Dec 2005 (Median) to 2022, with 18 observations. The data reached an all-time high of 55,837,807.130 BRL th in 2022 and a record low of 8,496,312.000 BRL th in 2006. Brazil Sales: Steel: Re-rolled, Drawing Plants (RDP) data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.BAC004: Industrial Sales: CNAE 2.0.

16S rRNA custom database derived from RDP (release 11, update 5) containing only high quality, classified sequences from bacterial isolates. Titles of the sequences are renamed according to taxonomy of each isolate. After building a database by makeblastdb command, this can be used for BLASTing community data.Database created 01/18.

Discover the booming RDP VPS Server market! This comprehensive analysis reveals market size, CAGR, key drivers, trends, and regional insights (North America, Europe, Asia-Pacific) from 2019-2033. Learn about leading companies and future growth projections for Windows & Linux RDP VPS solutions.

Brazil Sales: Steel: RDP: Profiles of Cold Carbon Steels data was reported at 4,975,901.777 BRL th in 2022. This records an increase from the previous number of 3,975,132.106 BRL th for 2021. Brazil Sales: Steel: RDP: Profiles of Cold Carbon Steels data is updated yearly, averaging 1,215,494.241 BRL th from Dec 2005 (Median) to 2022, with 18 observations. The data reached an all-time high of 4,975,901.777 BRL th in 2022 and a record low of 403,740.000 BRL th in 2005. Brazil Sales: Steel: RDP: Profiles of Cold Carbon Steels data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.WAI001: Iron and Steel Sales.

Brazil Production: Steel: RDP: Accessories for Rails for Railways data was reported at 443,419.767 BRL th in 2022. This records an increase from the previous number of 358,266.959 BRL th for 2021. Brazil Production: Steel: RDP: Accessories for Rails for Railways data is updated yearly, averaging 132,786.458 BRL th from Dec 2006 (Median) to 2022, with 17 observations. The data reached an all-time high of 443,419.767 BRL th in 2022 and a record low of 31,638.000 BRL th in 2009. Brazil Production: Steel: RDP: Accessories for Rails for Railways data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Global Database’s Brazil – Table BR.BAA068: Industrial Production: CNAE 2.0.

12 Global export shipment records of Rdp with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.