Saccharomyces cerevisiae promoter library
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ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae
PROVIDER: GSE48860 | GEO | 2013/09/01
REPOSITORIES: GEO
Similar Datasets
Project description:Combinatorial promoter expression level estimation via cell sorting The purpose of this experiment was to determine the expression level of a library of synthetic promoters. The promoters were cloned in front of a GFP reporter and the resulting library transformed into yeast, sorted by FACS into six fluorescence bins, and the contents of the bins sequenced to determine the distribution of each promoter among each fluorescence bin. This was then used to calculate an expression level for each promoter with enough data. The promoters were sorted into six bins and these, along with the unsorted library were barcoded and sequenced on a single lane of an Illumina HiSeq. The following Series supplementary files are provided: allPromoters.fsa.txt: the sequences of the promoters corresponding to the names in allPromoters.txt, not actually fasta format. Promoter sequence starts at pos 155 (0 indexed). allPromoters.txt: the names of all the promoters, corresponding to the sequences in allPromoters.fsa.txt barcodes.txt: the sequncing barcodes, corresponding to read2 from the sequencing files.
2013-09-01 | E-GEOD-48860 | biostudies-arrayexpress
Project description:Genetically identical cells exhibit large variability (noise) in gene expression, with important consequences for cellular function. Although the amount of noise decreases with and is thus partly determined by the mean expression level, the extent to which different promoter sequences can deviate away from this trend is not known. Here, we study how different noise levels are encoded by the promoter sequence using massively parallel noise measurements of thousands of synthetically designed promoters. We find that the noise levels of promoters with similar mean expression levels can vary over more than one order of magnitude, with nucleosome-disfavoring sequences resulting in lower noise and more transcription factor binding sites resulting in higher noise. We devised a computational model that can accurately predict the mean-independent component of the noise from DNA sequence alone. Our model suggests that the effect of promoters on noise is partly mediated by the combination of non-specific DNA binding and one-dimensional sliding along the DNA that occurs when transcription factors search for their target sites. Overall, our results demonstrate that small changes in the DNA sequence of promoters can allow tuning of noise levels in a manner that is largely predictable and partly decoupled from effects on the mean expression levels. These insights may assist in designing promoters with desired noise levels. Expression measurements of a collection of synthetic promoters collection that was published in Sharon et al. Nature Biotechnology 2012(doi: 10.1038/nbt.2205). Two replicates of the promoter library integrated into a plasmid in yeast were measured in SC-Glu-URA medium. The promoter library was measured as described in Sharon et. al.(Sharon et al. 2012), except for the differences below. Briefly, a large collection of synthetic promoter reporter gene strains was generated by a pooled ligation of 6500 fully designed DNA oligos (obtained by synthesis on a microarray(LeProust et al. 2010) by Agilent Technologies, Santa Clara, California). The oligos were ligated upstream to a yellow fluorescent protein (YFP) gene with a short (100 bp) core promoter sequence taken from HIS3 gene promoter and into a low copy plasmid which also contains a TEF2 promoter deriving red fluorescent protein (mCherry). The resulting plasmids were then transformation into yeast (S. cerevisiae). Next, the pool of cells was grown in amino acid starvation condition (SCD without amino acid except Histidine), and sorted according to their YFP expression level into 32 expression bins (mCherry was used for gating one plasmid copy cells and for normalization). The DNA of the promoters in each bin were then amplified and sent to multiplexed parallel sequencing. Each sequencing result was mapped to a specific promoter and expression bin, resulting in a distribution of cells that contain each promoter across all expression bins. The following differences were applied relative to the description in Sharon et. al.(Sharon et al. 2012). The medium used both for growing the cells and for their sorting was SC-Glu-URA (synthetic complete media with 2% glucose and without uracil) medium without amino acids, except for Histidine. In order to achieve expression distributions with high resolution that would allow good assessment of expression noise, the library cells were sorted into 32 bins according to their ratio of YFP and mCherry expression level, thereby normalizing for extrinsic noise effects. Each of the two extreme expression bins contained 2% of the library cells and each of the remaining 30 bins contained 3.2%. We collected a total of 10,000,000 cells. As previously described, the mapping of cells to bins involves parallel sequencing of the amplified promoter regions. For this purpose, Illumina Hi-Seq 2000 was used to obtain >30,000,000 mapped reads. The two replicates were separately generated from the ssDNA oligo library and separately measured as described above.
2014-07-12 | E-GEOD-55346 | biostudies-arrayexpress
Project description:The cross compatibility of functional PhoQ-PhoP mutants with altered specificity residues was analyzed by sort seq. We cloned and combinatorially combined 79 PhoQ* and 71 PhoP* variants, producing a library with a theoretical diversity of 5,609. The library was then subjected to Sort-Seq, using fluorescence-activated cell sorting (FACS) and deep sequencing to quantify the signal responsiveness of variants in the library. To gauge the phosphorylation of PhoP in vivo, we used a fluorescent transcriptional reporter, PmgrB-yfp. In the presence of low extracellular Mg2+, functional PhoQ promotes the phosphorylation of PhoP and the production of YFP, whereas in the presence of high concentrations of Mg2+, PhoQ drives the dephosphorylation of PhoP, limiting the accumulation of YFP). The library was grown in each condition for 6 hours before sorting and sequencing. To identify variants that are signal responsive and drive YFP production specifically in low Mg2+, we sorted cells from each condition into 8 separate bins and deep sequenced the randomized regions of variants collected in each bin (Fig. 2b). We then calculated the frequency of each variant in each bin to yield the distributions of individual variants in low and high Mg2+, which were fit to Gaussians. From these fits, we assessed the mean level of YFP in each condition and the fold-induction, or signal responsiveness, of each variant detected in the library. The 11 codons / amino acids listed in this dataset refer to codons 12, 14, 15, 18, and 19 in PhoP and codons 284, 288, 289, 292, 302, and 303 in PhoQ, in that order.
2019-07-27 | GSE120786 | GEO
Project description:Despite extensive research, our understanding of the rules according to which cis-regulatory sequences are converted into gene expression is limited We devised a method for obtaining parallel, highly accurate gene expression measurements from thousands of designed promoters and applied it to measure the effect of systematic changes in the location, number, orientation, affinity and organization of transcription-factor binding sites and nucleosome-disfavoring sequences. Our analyses reveal a clear relationship between expression and binding-site multiplicity, as well as dependencies of expression on the distance between transcription-factor binding sites and gene starts which are transcription-factor specific, including a striking ~10-bp periodic relationship between gene expression and binding-site location. We show how this approach can measure transcription-factor sequence specificities and the sensitivity of transcription-factor sites to the surrounding sequence context, and we compare the activity of 75 yeast transcription factors. Our method can be used to study both cis and trans effects of genotype on transcriptional, post-transcriptional and translational control. Expression profiling of 6500 synthetic promoters in yeast (Saccharomyces cerevisiae). The results were achieved using a method for obtaining pooled and highly accurate expression measurements (closely related to MPRA). In short, the synthetic promoters, which contain unique barcodes, are inserted upstream to a yellow florescence reporter gene, the cells are sorted by their florescence level (using FACS) to expression bins, the promoters in each expression bin are amplified and are send to parallel sequencing (SOLiD) to determine the percent of cells of each promoter in each expression bin; finally an mean expression value is extracted from the raw results. The measurements contain two replicates of exponentially growing yeast cells in SC-Gal-URA (synthetic complete media with 2% galactose and without uracil).
2012-05-20 | E-GEOD-37851 | biostudies-arrayexpress
Project description:The specificity of the kinase-regulator interaction is driven by a limited set of interfacial residues in each protein that strongly. To identify combinations of these interface residues that are functional and potentially insulated from existing two-component signaling pathways in E. coli, we constructed a dual library of mutants in which the key, coevolving interface residues of a canonical two-component system, PhoQ and PhoP, were randomized. We used NNS codons to randomize six residues in PhoQ and five residues in PhoP, all of which lie at the interface formed by the two proteins in complex and that are critical to determining partner specificity in all two-component signaling pathways. To identify functional combinations of residues, we first grew the library of PhoQ-PhoP variants overnight in medium with low Mg2+, which activates PhoQ. Because cells must phosphorylate PhoP to grow when extracellular Mg2+ is limiting, this step enriches for functional PhoQ-PhoP variants. Variants that survived selection in limiting Mg2+ were then subjected to Sort-Seq, using fluorescence-activated cell sorting (FACS) and deep sequencing to quantify the signal responsiveness of variants in the library. To gauge the phosphorylation of PhoP in vivo, we used a fluorescent transcriptional reporter, PmgrB-yfp. In the presence of low extracellular Mg2+, functional PhoQ promotes the phosphorylation of PhoP and the production of YFP, whereas in the presence of high concentrations of Mg2+, PhoQ drives the dephosphorylation of PhoP, limiting the accumulation of YFP). The library was grown in each condition for 6 hours before sorting and sequencing. To identify variants that are signal responsive and drive YFP production specifically in low Mg2+, we sorted cells from each condition into 8 separate bins and deep sequenced the randomized regions of variants collected in each bin. We then calculated the frequency of each variant in each bin to yield the distributions of individual variants in low and high Mg2+, which were fit to Gaussians. From these fits, we assessed the mean level of YFP in each condition and the fold-induction, or signal responsiveness, of each variant detected in the library. The 11 codons / amino acids listed in this dataset refer to codons 12, 14, 15, 18, and 19 in PhoP and codons 284, 288, 289, 292, 302, and 303 in PhoQ, in that order.
2019-09-20 | GSE120780 | GEO
Project description:As training dataset for Random Promoter DREAM Challenge 2022, we generated ~6.7 million synthetic promoters (in yeast) comprised of random DNA (N80) and measured their expression by FACS (sorting into 18 bins). As test dataset for Random Promoter DREAM Challenge 2022, we generated ~71k synthetic promoters (in yeast) comprised of designed DNA (NBT) and measured their expression by FACS (sorting into 18 bins).
2024-02-03 | GSE254493 | GEO
Project description:We developed a spatially resolved method to profile the spatial transcriptome of biofilm. In detail, we used fluorescent dyes to label the different regions of biofilm cultured in a microfluidic chip. After staining, the bacterial cells in biofilm were sorted into relevant bins according to their spatial information marked by the fluorescent pattern. Finally, miniBac-seq (RNA-seq) method was applied to capture the transcriptome of each bin.
2023-01-01 | GSE197541 | GEO
Project description:We present a combined experimental/computational technology to reveal a catalogue of functional regulatory elements embedded in 3’UTRs of human transcripts. We used a bidirectional reporter system coupled with flow cytometry and high-throughput sequencing to measure the effect of short, non-coding vertebrate-conserved RNA sequences on transcript stability and translation. Information-theoretic motif analysis of the resulting sequence-to-gene-expression mapping revealed linear and structural RNA cis-regulatory elements that positively and negatively modulate the post-transcriptional fates of human transcripts. We explored the functional role of 16332 short human 3'UTR sequences (C3U Library) evolutionarily conserved with P. troglodytes, M. musculus and G. gallus. For sorting the C3U library into subpopulations, we gated the population using FACS into bins each containing 10% of the total number of cells. We collected cells for the top four high expression bins (H10, H20, H30, H40) and the bottom four low expression bins (L10, L20, L30, L40) Multiple samples (C3U library subpopulations) were pooled for each illumina index (decribed n the 'library construction protocol' field). For example, six samples (A-L10 I & II, B-L10 I & II, A-L20 I & II) were all pooled in illumina index CGATGTAT. Therefore, each individual fastq file corresponds to multiple samples. The individual samples were sorted in the processing step using identifiers inside the sequence reads. One single pooled sample was run on both lanes (the extra lane was run for additional reads). The expression level (H: high, L:low or background) and library replicates (A or B) of the pooled library subpopulations are indicated in the sample title.
2014-03-21 | E-GEOD-55396 | biostudies-arrayexpress
Project description:In order to generate data produce a generalizable sequence-to-expression model, we randomly sampled ~21 million 80 bp sequences and tested their activity as promoters (in yeast) by measuring expression level by FACS (sorting into 18 bins).
2021-12-23 | GSE163045 | GEO
Project description:<p>In order to evaluate whether rare regulatory variants in the vicinity of promoters are likely to impact gene expression, we conducted a novel burden test for rare variants at the extreme of expression. After targeted sequencing of 2kb promoter regions of 472 genes in 410 healthy adults, burden tests were performed by calculating the summed rare allele counts in ranked expression level bins using Illumina whole blood microarray gene expression data. The results clearly show an enrichment of rare variants at both extremes of gene expression. The rare regulatory variant effects were also partitioned into subsets of genes based on their regulatory functions, positions relative to transcription start sites, disease relatedness, with some intriguing biases. The enrichment of rare regulatory variants in extremely expressed genes was replicated in an independent sample of 75 individuals with RNASeq and whole genome sequence information. Participants were from the Emory-Georgia Tech Center for Health Discovery and Well Being (CHDWB) longitudinal cohort study, but only baseline gene expression data was used.</p>
| phs001021 | dbGaP