Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Transcriptional Response to Inducers for E. coli and B. subtilis

ABSTRACT: Sequencing technologies, in particular RNASeq, have become critical tools in the design, build, test, learn cycle for synthetic biology. They provide a better understanding of synthetic designs and they help identify ways to improve and select designs. While this data is beneficial to design, its collection and analysis is a complex, multi-step process that has implications both on discovery and reproducibility of experiments. Additionally, tool parameters, experimental metadata, and normalization of data and standardization of file formats present challenges that are computationally intensive. This calls for high-throughput pipelines expressly designed to handle the combinatorial and longitudinal nature of synthetic biology. In this paper, we present a pipeline to maximize analytical reproducibility of RNASeq for synthetic biologists. We also explore the impact of reproducibility on the validation of machine learning models.

ORGANISM(S): Bacillus subtilis subsp. subtilis str. 168 Escherichia coli str. K-12 substr. MG1655

PROVIDER: GSE206047 | GEO | 2022/06/16

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

The MILE (Microarray Innovations In LEukemia) study pre-phase

Project description:An international standardization program towards the application of gene expression profiling in routine leukaemia diagnostics: The MILE study pre-phase. A total of 11 laboratories from three continents performed 204 analyses using a whole-genome expression microarray. In an effort to standardize the microarray procedure, each participating centre was equipped with identical hardware, software, and reagents. Designated study operators received intensive individual training on the sample preparation protocol. Keywords: Standardization and reproducibility of microarray analysis

2008-06-24 | GSE11135 | GEO

Holistic engineering of cell-free systems through proteome-reprogramming synthetic circuits

Project description:Synthetic biology has focused on engineering genetic modules that operate orthogonally from the host cells. A synthetic circuit, however, can be designed to reprogram the host proteome, which in turn enhances the function of the synthetic circuit. Here, we apply this holistic synthetic biology concept by exploiting the crosstalk between metabolic networks in cells, leading to a protein environment more favorable for protein synthesis. Specifically, we show that a local module expressing translation machinery can reprogram the bacterial proteome, changing the expression levels of more than 780 proteins. The integration of the proteins synthesized by the local modules and the reprogramed proteome generate a cell-free system that can synthesize a diverse set of proteins in different reaction formats, with up to 5-fold higher expression level than classical cell-free systems. Our work demonstrates a holistic approach that integrates synthetic and systems biology concepts. This approach has the potential to achieve outcomes not possible by only local, orthogonal circuits.

2020-05-14 | PXD018858 | Pride

A unique pair of microarray datasets for microRNA profiling: data with careful design (Dataset A)

Project description:We set out to demonstrate the logistic feasibility of careful study design in microarray studies and the level of scientific benefits it can provide, in comparison with post-hoc data adjustment such as normalization, for preventing confounding handling effects and improving the accuracy and reproducibility of disease-relevant biomarker detection. Towards this end, we conducted a study of microRNA expression using endometroid endometrial cancer tumors (n=96) and serous ovarian cancer tumors (n=96) that were all primary, untreated, and collected in 2000-2012 at Memorial Sloan Kettering Cancer Center. The same set of tumor tissue samples were profiled twice using the Agilent microRNA microarrays with different study designs.

2018-01-08 | GSE108838 | GEO

Iterative deep learning-design of human enhancers exploits condensed sequence grammar to achieve cell type-specificity [scRNA-Seq]

Project description:An important and largely unsolved problem in synthetic biology is how to target gene expression to specific cell types. Here, we apply iterative deep learning to design synthetic enhancers with strong differential activity between two human cell lines. We initially train models on published datasets of enhancer activity and chromatin accessibility and use them to guide the design of synthetic enhancers that maximize predicted specificity. We experimentally validate these sequences, use the measurements to re-optimize the predictor, and design a second generation of enhancers with improved specificity. Our design methods embed relevant transcription factor binding site (TFBS) motifs with higher frequencies than comparable endogenous enhancers while using a more selective motif vocabulary, and we show that enhancer activity is correlated with transcription factor expression at the single cell level. Finally, we characterize causal features of top enhancers via perturbation experiments and show enhancers as short as 50bp can maintain specificity.

2024-06-14 | GSE269037 | GEO

Iterative deep learning-design of human enhancers exploits condensed sequence grammar to achieve cell type-specificity [RNA-Seq]

2024-06-14 | GSE269036 | GEO

Design and deep learning of synthetic B- cell-specific promoters

Project description:Flexible regulation of gene expression is essential and highly sought for synthetic biology and biotechnology. Designing regulators with specific functions remains a challenge due to the limited understanding of specific regulatory mechanisms. We design and synthesize 23,640 B-cell-specific promoters, following the design-build-test-learn pipeline in synthetic biology. Synthetic promoters exhibit B-cell-specific expression and lead to diverse expression patterns in B-cells. By conducting MPRA testing, we uncovered the factors that influence promoter strength, including core motifs and motif syntax, which shape B-cell-specific promoter strength. Finally, we developed a deep leaning model capable of predicting promoter activity directly from the sequence, and to predict promoter activity for 26,193 variants identified in the global population, indicating that polymorphisms in IgV gene promoters can influence gene expression. Our work helps to decipher the regulatory code in immunoglobulin genes and offers thousands of non-repetitive promoter elements for B-cell engineering.

2023-05-16 | GSE232161 | GEO

multiplexage : dual-target hybridization microarray experiment-New developments towards a genetic and diagnosis use of plant mircoarrays

Project description:gnp06-01_agriarray - multiplexage : dual-target hybridization microarray experiment - Several factors affect today a more extended use of microarrays for research and as a diagnosis tool. These factors are the experimental cost, the reproducibility of the measurements and the format of the analyses. This project aims at bringing solutions in these three domains by optimizing multiplexed analyses in order to reduce cost and enhance the number of samples treated simultaneously; by proposing new couples flurophores / slide surface in order to increase the signal precision and by developing new applications such as Comparative Genomic Hybridization (GGH) or Promoter Arrays to bring the plant microarray beyond transcriptome analyses. Altogether, these approaches will allow us to construct an optimized diagnostic tool based on 96 well microplate microarrays. This multidisciplinary project bring together biologists, chemists, physicists and mathematicians to develop innovative solutions for future application of DNA microarrays. - Dual-target hybridization microarray experiment with doble factors comparison : two mutants and its wild-types at different times. The aim of this part is to compare the classical design with the new design with a third dye.

2011-12-08 | GSE29104 | GEO

Project description:Preventing Evolutionary Failure in Synthetic Biology

| PRJNA345231 | ENA

Transcription profiling of Arabidopsis self vs self experiment Col-0 samples performed at VIB

Project description:otpimization and standardization experiment using "yellow"-type design

2005-04-06 | E-CAGE-23 | biostudies-arrayexpress

Transcription profiling of Arabidopsis self vs self experiment Col-0 samples performed at SLU

Project description:Optimization and standardization experiment using "yellow"-type design

2005-04-06 | E-CAGE-24 | biostudies-arrayexpress