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47 results for “16S rDNA”
16s rDNA-sequencing of gastric flora in mice fed with high-salt diets
GEO Series GSE134237. mouse gut metagenome. 10 samples. Type: Other.
16s rDNA-sequencing of gastric flora in mouse fed with high salt with or without buckwheat
GEO Series GSE132412. mouse gut metagenome. 36 samples. Type: Other.
16S rDNA (v3-v4 region) sequencing of the cecal bacteria in control (CTL) and chronic nonbacterial prostatitis (CNP) rats
GEO Series GSE179639. rat gut metagenome. 12 samples. Type: Other.
Metataxonomics 16S rDNA of maize rhizospheric soil with seed bacterization with Bacillus thuringiensis RZ2MS9 WT and mutant daiiA
<p>The plant growth-promoting bacteria (PGPB) <em>Bacillus thuringiensis</em> (Bt) RZ2MS9 have been described as a potential bioinoculant for maize (<em>Zea mays</em> L.). Quorum quenching (QQ) is an important system that has evolved in some bacteria as a competitive advantage for niche colonization. The <em>aiiA</em> gene, a homoserine lactonase involved in QQ, was identified in Bt RZ2MS9 genome and the ability to degrade quorum sensing (QS) molecules was validated through a QQ biosensor assay. The knockout of RZ2MS9 <em>aiiA</em> was performed using the CRISPR-Cas9 system, confirming this gene function <em>in vitro</em>. The influence of QQ activity of RZ2MS9 for maize growth promotion and its implications in the persistence of the PGPB in the microbial community were investigated in a greenhouse experiment. Maize seeds were inoculated with the strains RZ2MS9 WT and RZ2MS9 <em>ΔaiiA</em> and sowed in pots. Twenty five days after sowing (DAS), samples of rhizospheric soil were collected for total DNA extraction and 16S rDNA sequencing by Illumina MiSeq (2x250 bp) and plant phenotypic traits were evaluated. Our aim was to investigate whether the ability of RZ2MS9 interfering in QS communication is relevant for the establishment of the PGPB in a native microbial community and, consequently, for plant growth promotion.</p>
Metataxonomics 16S rDNA of maize bulk and rhizospheric soil samples with seed bacterization with Azospirillum brasilense Ab-V5 in a dilution-to-extinction of native microbial diversity
<p><strong>The beneficial plant-microbes association has been widely explored with the purpose of understanding the mechanisms involved in this interaction, in order to develop bioinoculants for agriculture aiming at sustainability and reduction of chemical inputs use. Plant growth promoting bacteria (PGPBs) have been reported as an interesting alternative to mineral fertilizers for a range of crops. The genus Azospirillum has shown potential in plant growth promotion for maize. However, the lack of holistic comprehension about PGPBs-plant-native soil microbiota can lead to inconsistency of results in field conditions. Recent ecological theories revealed that plant microbiomes are organized as microbial hubs with keystone species and helpers. The objective of this work is to characterize the microbial community associated with a commercial maize genotype (30A37PW) under influence of the potential keystone PGPB A. brasilense Ab-v5 to support the original development of a synthetic minimum microbiome including this strain as keystone for plant growth promotion. Aiming to understand the PGPB-plant-microbiome system, we conducted a greenhouse experiment in which the synergistic effect of Ab-V5 and soil native microbial community were evaluated through soil and rhizosphere metataxonomics, metagenomics and plant phenomics. Our results revealed that maize seed bacterization with the PGPB Ab-V5 promotes plant growth. This effect is boosted by the interaction with specific microbial groups present in the soil native community. The strain Ab-V5 persists in the maize rhizosphere until 25 days after sowing. In order to understand which microbial groups are important during the interaction Ab-V5-plant-native soil microbial community, 16S rDNA metataxonomics and metagenomics analysis will be conducted in maize soil and rhizosphere samples. This unprecedented omics approach for the holistic view of the complex system composed by inoculant-plant-microbiota and data integration will allow the development of new strategies for optimization of plant biostimulants recommendation with enhanced efficiency for establishment in the field. </strong></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>
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>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.