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763 results for “Saccharomyces”
Associated data from: An end-to-end workflow to study newly synthesized mRNA following rapid protein depletion in Saccharomyces cerevisiae
<p>This dataset includes two custom BED files described in "An end-to-end workflow to study newly synthesized mRNA following rapid protein depletion in <em>Saccharomyces cerevisiae</em>" (Ridenour and Donczew, submitted), which were used to define counting windows for processing SLAM-seq data in SLAM-DUNK (version 0.4.3) [1]. The BED files contain all annotated open reading frames (ORFs) in the<em> Saccharomyces cerevisiae</em> genome or the <em>Schizosaccharomyces</em><em> pombe</em> genome and were created using BEDOPS (version 2.4.3) [2]. All ORFs were then extended 250 bp beyond their stop position to capture 3′ untranslated regions (UTRs) using SAMtools (version 1.14) [3] and BEDTools (version 2.30.0) [4]. The reference genome annotations for <em>S. cerevisiae</em> strain S288C (version R64-3-1, RefSeq Assembly GCF_000146045.2) and <em>S. pombe</em> strain 972h- (version ASM294v2, RefSeq Assembly GCF_000002945.1) were retrieved from the NCBI Datasets repository. The <em>S. cerevisiae </em>chromosome names were modified to reflect standard nomenclature (https://www.yeastgenome.org/).</p>
The pan-genome of Saccharomyces cerevisiae
<p>These datasets are related to 'The pan-genome of <em>Saccharomyces cerevisiae</em>' (Li G., Ji B., and Nielsen J.).</p> <p>This deposition contains following datasets:</p> <p>(1) Genomes.tar.gz: a compressed file containing 1392 <em>Saccharomyces cerevisiae </em>genome assembles analyzed</p> <p>(2) genome_information_2.0.tsv: a tab-separated text file that contains the basic information of above genomes, including genomeSize, contigNums, N50, busco_C(%), busco_S(%), busco_D(%), busco_F(%), busco_M(%), busco_n, number_of_genes, number_of_partial_genes, download_from, Eco_Source, Ploidy, Aneuploidies.</p> <p>(3) ClusterFasta.tar.gz: a compressed file that contains a list of fasta files. Each fasta file contains protein sequences in a cluster. The name of the fasta file is the name of the representative sequence of that cluster. </p> <p>(4) sc_gene_cluster_info_0.7_v4.tsv: a tab-separated text file that contains the properties of gene clusters.</p> <p>(5) gene_presence_absence_v4.tsv: a tab-separated text file that contains the gene-presence/absence information. Each columns is a gene cluster. Each row is a genome. Y/N is used to present presence/absence.</p> <p>(6) gene_num_in_clusters_of_each_strain_v4.tsv: a tab-sparated text file that contains the gene number of each genome in each cluster (copy number). Each columns is a gene cluster. Each row is a genome. </p> <p>(7) feature_importances_cv5_pa_cnv.tsv: a tab-separated file that contains the feature importance from a random forest classifier in a 5-fold cross-validation approach. The classifier was trained on gene presence/absence table (PA) or copy number table (CNV). The columns 'pa_x' indicate the feature importance in each fold of cross-validation on PA dataset. The columns 'cnv_x' indicate the feature importance in each fold of cross-validation on CNV dataset. </p>
Relief from nitrogen starvation entails quick unexpected down-regulation of glycolytic/lipid metabolism genes in enological Saccharomyces cerevisiae
<p>Data and code supporting the manuscript "Relief from nitrogen starvation entails quick unexpected down-regulation of glycolytic/lipid metabolism genes in enological Saccharomyces cerevisiae" by Tesnière et al. (2019) PLoS ONE 14(4): e0215870. https://doi.org/10.1371/journal.pone.0215870</p> <p>README.pdf or README.md files contain information about the files in this archive.</p>
Inferelator Saccharomyces Cerevisiae Data Set
<p>This data is associated with the Inferelator package. It consists of an expression data set, a prior data matrix generated from ATAC-seq data, and a gold standard derived from YEASTRACT. It was initially used in Tchourine, K., Vogel, C., and Bonneau, R. (2018). Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks. Cell Reports 23, 376–388.</p>
ScRAPv20230731: Telomere-to-telomere assemblies of 142 strains characterize the genome structural landscape in Saccharomyces cerevisiae
<p><strong><em>Saccharomyces cerevisiae </em>Reference Assembly Panel (ScRAP) v20230731 </strong>></p> <p>The haplotype-resolved and/or collapsed T2T genome assemblies for 142 <em>S. cerevisiae</em> strains isolated from diverse geographical and ecological niches.</p>
Insights into intraspecific diversity of central carbon metabolites in Saccharomyces cerevisiae during wine fermentation
<p>Supplementary data including the data set used for the " Insights into intraspecific diversity of central carbon metabolites in <em>Saccharomyces cerevisiae</em> during wine fermentation" publication.</p> <p>Abstract:</p> <p><em>Saccharomyces cerevisiae</em>, as the workhorse of alcoholic fermentation, is a major actor in winemaking. In this context, this yeast species performs alcoholic fermentation to convert sugars from the grape must into ethanol and CO2 with outstanding efficiency as it reaches on average 92% of the maximum theoretical yield of conversion. Primary metabolites produced during fermentation have a great importance in wine where they significantly impact wine characteristics. While ethanol content contributes to the overall profile, others metabolites also have significant impacts, even when present in lower concentrations: glycerol, succinate, acetate, ⍺-ketoglutarate, lactate… <em>S. cerevisiae</em> is known for its great genetic diversity and plasticity that is directly related to its living environment, natural or technological and therefore to domestication. This leads to a wide phenotypic diversity of metabolites production. However, the range of metabolic diversity is variable and depends on the pathway considered. With the aim to improve wine quality, the selection, development and use of strains with dedicated metabolites production without genetic modifications can rely on the already existing natural diversity. Here we detail a screening experiment that aims to assess the diversity of primary metabolites production in a set of 51 <em>S. cerevisiae</em> strains from various genetic backgrounds (wine, flor, rum, West African, sake…). To approximate winemaking conditions, we used a synthetic grape must as fermentation medium and measured seven metabolites by HPLC. Results pointed out great yield differences between strains depending on the metabolite considered. Ethanol appeared as the one with the smallest variation among our set of strains, although it was by far the most produced. A clear negative correlation between ethanol and glycerol was observed, confirming glycerol synthesis as a suitable lever to reduce ethanol yield. Genetic groups were linked to specific metabolic yields such as high α-ketoglutarate and low acetate yields for wine strains. This study thus helps to characterise the phenotypic diversity of <em>S. cerevisiae</em> in a wine-like context and comforts the use of natural diversity in the development of new strains. Finally, it provides a detailed data set usable to study diversity of well known (ethanol, glycerol, acetate) or little-known (lactate) primary metabolites production, including in common commercial wine strains.</p> <p> </p> <p> </p>
YeastExM: Expansion Microscopy on Saccharomyces cerevisiae
<p>Here we provide a video protocol showing how to proceed throughout the protocol of ExM on Yeast as described here:</p> <p><strong>"Expansion microscopy on Saccharomyces cerevisiae" </strong></p> <p>(DOI:<strong> </strong>10.20944/preprints202203.0146.v1)</p> <p>Artemis G. Korovesi<sup>§1</sup>, Leonor Morgado<sup>§1</sup>, Marco Fumasoni<sup>1</sup>, Ricardo Henriques<sup>1,2</sup>, Hannah S. Heil<sup>1*</sup>, Mario Del Rosario<sup>1*</sup></p> <p>Correspondance to H.S.H.: <a href="mailto:hsheil@igc.gulbenkian.pt">hsheil@igc.gulbenkian.pt</a> & M.D.R.: <a href="mailto:mrosario@igc.gulbenkian.pt">mrosario@igc.gulbenkian.pt</a></p>
Growth and metabolome data of Saccharomyces uvarum grown in synthetic wine must with different nitrogen sources
<p><em>Raw data: Metabolome of S. uvarum (Su) and S. cerevisiae (Sc) in wine fermentations in 13 different nitrogen conditions. Concentrations of compounds expressed in mg/L at 60 g/L CO2 sampling point and at the end of fermentation. The volatile compounds are grouped according to the chemical functional group (ethyl esters, acetate esters, higher alcohols, medium chain fatty acids (MCFA) and branched-chain fatty acids (BCFA)). The central carbon metabolites (CCM) and sugars conform the last group. Each value is the mean of three biological replicates. The raw data is reported in a processed form in the manuscript entitled “The growth and metabolome of Saccharomyces uvarum in wine fermentations is strongly influenced by the route of nitrogen assimilation” </em></p>
Transcriptome data of Saccharomyces uvarum grown in synthetic wine must with different nitrogen sources Created Jun 9, 2022 12:06:22 PM, modified Jun 9, 2022 12:07:57 PM
<p>Transcriptome analysis of S. uvarum grown on synthetic wine must with different nitrogen sources: Ammonium, Phenylalanine, Asparagine, or Methionine. This is a dataset for the thesis of Angela Coral (Autumn 2022) , which can be accessed at www.ucc.ie. The work will also be submitted for publication and this will be a supplementary data file.</p>
Saccharomyces cerevisiae Bud-Annotation pipeline: Napari Example Data
<p>Example dataset for bud-annotation plugin Napari.</p> <p>Here we provide two sets of 3 images of single molecule mRNA FISH on Saccheromyces cerevisiae (BY4741) strains containing an mRNA bud localization reporter at the DOA1 locus. <br> The image set labeled EXPERIMENT contains images of the strain in which a localization element was present in the reporter mRNA. The reporter can be seen to localize to the bud. <br> The image set labeled CONTROL contains images of the control strain without localization element present in the reporter mRNA. The reporter can be seen to be randomly distributed throughout the cell. </p> <p>Images were acquired as 41 z-stacks per fluorescence channel CY5, CY3.5, CY3 and DAPI. For each fluorescence image a corresponding DIC image was acquired as well. </p> <p>The following smFISH probes were used: <br> - CY5: probes targeting the endogenous ASH1 and CLB2 mRNA to be used as bud marker (Quasar 670)<br> - CY3.5: probes targeting the DOA1 mRNA reporter (CAL Fluor Red 610)<br> - CY3: probes targeting the MS2v6 sequence inserted at the 3' end of the DOA1 mRNA reporter (Quasar 570) </p> <p>FISH-QUANT spot analysis results for the CY3.5 channel from these images are included. This data can be used to extract the spot information and assign spots to either mother cell or bud. Examples of nuclear, bud and cell masks for all images are provided as well.</p>
Glucose-1-phosphate metabolism (Saccharomyces cerevisiae)
<p>The glucose-1-phosphate metabolism pathway in Saccharomyces cerevisiae from 2017</p>
Relief from nitrogen starvation triggers a transient destabilization of glycolytic mRNAs in Saccharomyces cerevisiae cells
<p>Dataset supporting "Relief from nitrogen starvation triggers a transient destabilization of glycolytic mRNAs in Saccharomyces cerevisiae cells" (2018) Molecular Biology of the Cell 29:377-522. DOI: 10.1091/mbc.E17-01-0061</p>
Figure 2 in EVALUATION OF THE EFFECTIVENESS OF USING A UNIVERSAL METHOD FOR ISOLATING GENOMIC dsDNA BY SALTING OUT TECHNIQUE ACCORDING TO THE S.M. ALJANABI AND I. MARTINEZ PROTOCOL FOR YEAST SACCHAROMYCES CEREVISIAE
Figure 2. Quantification of extracted Saccharomyces cerevisiae DNA by UVVis Spectrophotometry (DNA samples: 1–15).
Figure 4 in Saccharomyces cerevisiae OS303 expression of an alkaline protease from a newly isolated Bacillus subtilis D9
Figure 4. Analysis of expression cloning vector (digested and purified cloning vector pRS426/GAL1p-207-Glu-MS ligated with alkaline protease called in this study pRS426/GAL1p-207-Glu-MS/ alkaline-protease plasmids. Lane 1: DNA size marker hyperladder I; Lane 2: pRS426/GAL1p-207-Glu-MS/alkaline-protease linear plasmid around 8000bp; Lane 3: pRS426/GAL1p-207-Glu-MS vector. Lane 4: pRS426/GAL1p-MS vector.
Figure 1 in Saccharomyces cerevisiae OS303 expression of an alkaline protease from a newly isolated Bacillus subtilis D9
Figure 1. The electrophoresis of PCR product of alkaline protease gene from Bacillus subtilis D9. After PCR, 5Μl of the product was run on 1% agarose gel electrophoresis. The expected band of approximately1300bp was observed. Lane 1: DNA size marker hyperladder I; Lane 2: PCR product sample.
Inferelator Saccharomyces Cerevisiae Single-Cell Data Set
<p>This data is associated with the Inferelator package. It has an expression data set (103118_SS_Data.tsv.gz), which is a [Cells x Genes] TSV file which has 5 included metadata columns [Genotype, Genotype_Group, Replicate, Condition, tenXBarcode]. It also contains a prior data matrix generated from the YEASTRACT database (YEASTRACT_Both_20181118.tsv), a gold standard derived from the YEASTRACT database (gold_standard.tsv), a list of transcription factors (tf_names_restrict.tsv), and a list of protein-coding genes (orfs.tsv). It was initially used in Jackson, C.A., Castro, D.M., Saldi, G.-A., Bonneau, R., and Gresham, D. (2019). Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments. BioRxiv 581678.</p>
Dataset: A quantitative 1H NMR approach for evaluating the metabolic response of Saccharomyces cerevisiae to mild heat stress
<p>In this study, the effect of growth temperature on the yeast (<em>Saccharomyces cerevisiae</em>) metabolome has been analyzed by one-dimensional proton NMR spectroscopy (<sup>1</sup>H NMR).</p> <p>Yeast cells were grown either at 30 or 37°C. A non-targeted chemometric evaluation of the spectra was performed in order to detect potential biomarkers. Moreover, an exhaustive assignment for most of the detected NMR signals was carried out, corresponding to 38 identified metabolites. Resonances from these identified metabolites were integrated, and univariate and multivariate data analyses were applied on the matrices of these relative concentrations. Observed changes in metabolite concentrations were consistent with the expected process of temperature acclimation, showing alterations in amino acid cellular pools, nucleotide metabolism and lipid composition.</p>
SO2 and copper tolerance exhibit an evolutionary trade-off in Saccharomyces cerevisiae
<p>Copper tolerance and sulfite tolerance are two well-studied phenotypic traits of <em>Saccharomyces cerevisiae</em>. The genetic bases of these traits are derived from allelic expansion at the CUP1 locus and reciprocal translocation at the SSU1 locus, respectively. Previous work identified a negative association between sulfite and copper tolerance in <em>S. cerevisiae</em> wine yeasts. Here we probe the relationship between sulfite and copper tolerance and show that an increase in <em>CUP1</em> copy number does not impart copper tolerance in all <em>S. cerevisiae</em> wine yeast. Bulk-segregant QTL analysis was used to identify variance at <em>SSU1</em> as a causative factor in copper sensitivity, which was verified by reciprocal hemizygosity analysis in a strain carrying 20 copies of <em>CUP1</em>. Transcriptional and proteomic analysis demonstrated that <em>SSU1</em> over-expression did not suppress <em>CUP1</em> transcription or constrain protein production but suggested that <em>SSU1</em> overexpression induced sulfur limitation during exposure to copper. Finally, an <em>SSU1</em> over-expressing strain exhibited increased sensitivity to moderately elevated copper concentrations in sulfur-limited medium, demonstrating that <em>SSU1</em> over-expression burdens the sulfate assimilation pathway. Over-expression of MET 3/14/16, genes upstream of H<sub>2</sub>S production in the sulfate assimilation pathway increased the production of SO<sub>2</sub> and H<sub>2</sub>S but did not improve copper sensitivity in an <em>SSU1</em> overexpressing background. We conclude that copper and sulfite tolerance are conditional traits in <em>S. cerevisiae</em> and provide evidence of the metabolic basis for their mutual exclusivity. These findings suggest an evolutionary basis for the extreme amplification of <em>CUP1</em> observed in some yeasts.</p>
Time lapse microscopy images of Saccharomyces cerevisiae's full life cycle
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SO2 and copper tolerance exhibit an evolutionary trade-off in Saccharomyces cerevisiae
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