Evaluation and optimization of sequence-based gene regulatory deep learning models
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ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae
PROVIDER: GSE254493 | GEO | 2024/02/03
REPOSITORIES: GEO
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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.
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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.
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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.
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