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ABSTRACT:
PROVIDER: PRJNA1073055 | ENA |
REPOSITORIES: ENA
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Project description:The phi X 174 bacteriophage was first sequenced in 1977, and has since become the most widely used standard in molecular biology and next-generation sequencing. However, with the advent of affordable DNA synthesis and de novo gene design, we considered whether we could engineer a synthetic genome, termed SynX, specifically tailored for use as a universal molecular standard. The SynX genome encodes 21 synthetic genes that can be in vitro transcribed to generate matched mRNA controls, and in vitro translated to generate matched protein controls. This enables the use of SynX as a matched control to compare across genomic, transcriptomic and proteomic experiments. The synthetic genes provide qualitative controls that measure sequencing accuracy across k-mers, GC-rich and repeat sequences, as well as act as quantitative controls that measure sensitivity and quantitative accuracy. We show how the SynX genome can measure DNA sequencing, evaluate gene expression in RNA sequencing experiments, or quantify proteins in mass spectrometry. Unlike previous spike-in controls, the SynX DNA, RNA and protein controls can be independently and sustainably prepared by recipient laboratories using common molecular biology techniques, and widely shared as a universal molecular standard.
2024-03-22 | PXD035035 | Pride
Project description:The Transcript Integrity Index (TII) provides a standard measure of sperm RNA
| PRJNA828260 | ENA
Project description:To address the neglected possibility for global mRNA changes in microarray experiments we developed a simple method to generate external controls for Affymetrix microarrays to allow these platforms to measure absolute mRNA expression at the global level. We used publicly available data to select probesets that never detect endogenous transcripts, and used PCR and IVT to generate synthetic mRNAs corresponding to them. After quality control and testing, these control transcripts were spiked into total RNA samples from plants before and after 24 h of cold treatment. Due to changes in the proportion of mRNA, these data reveal intensity-dependent bias in expression estimates based on standard all-gene normalizations. When not accounted for, this leads to false classification of the differential expression for thousands of genes.
2008-10-09 | E-MEXP-1345 | biostudies-arrayexpress
Project description:This experiment compares mRNA expression in cells with and without mitochondrial DNA (ρ+ and ρ0 cells respectively). We used global RNA profiling to measure mRNA expression in ρ+ and ρ0 cells harvested at log phase from synthetic medium with glucose (CM + 2% glucose, "CMD"), and at 4 hours after transfer to the same medium without glucose (CM). This profiling provided new clues about the ways in which nutirent signaling is reprogrammed in cells lacking mitocondrial DNA.
2013-12-31 | GSE49002 | GEO
Project description:This is the model of the in vitro DNA oscillator called oligator with the optmized set of parameters described in the article:
Programming an in vitro DNA oscillator using a molecular networking strategy.
Montagne K, Plasson R, Sakai Y, Fujii T, Rondelez Y. Mol Syst Biol. 2011 Feb 1;7:466. PubmedID:21283142, Doi:10.1038/msb.2010.120
Abstract:
Living organisms perform and control complex behaviours by using webs of chemical reactions organized in precise networks. This powerful system concept, which is at the very core of biology, has recently become a new foundation for bioengineering. Remarkably, however, it is still extremely difficult to rationally create such network architectures in artificial, non-living and well-controlled settings. We introduce here a method for such a purpose, on the basis of standard DNA biochemistry. This approach is demonstrated by assembling de novo an efficient chemical oscillator: we encode the wiring of the corresponding network in the sequence of small DNA templates and obtain the predicted dynamics. Our results show that the rational cascading of standard elements opens the possibility to implement complex behaviours in vitro. Because of the simple and well-controlled environment, the corresponding chemical network is easily amenable to quantitative mathematical analysis. These synthetic systems may thus accelerate our understanding of the underlying principles of biological dynamic modules.
The model reproduces the time courses in fig 2B. The parameter identifiers of the reaction constants are not the same as in the supplemental material, but are just called kXd and kXr for the forward and backwards constant of reaction X respectively.
This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2024-09-02 | BIOMD0000000315 | BioModels
Project description:The high throughput mRNA-seq technology was used to measure the effect of cpc-1 deletion on transcriptome change of Neurospora crassa grown under different culture conditions using standard Illumina protocols.
2020-07-23 | GSE150287 | GEO
Project description:This experiment compares mRNA expression in cells with and without mitochondrial DNA (M-OM-^A+ and M-OM-^A0 cells respectively). We used global RNA profiling to measure mRNA expression in M-OM-^A+ and M-OM-^A0 cells harvested at log phase from synthetic medium with glucose (CM + 2% glucose, "CMD"), and at 4 hours after transfer to the same medium without glucose (CM). This profiling provided new clues about the ways in which nutirent signaling is reprogrammed in cells lacking mitocondrial DNA. mRNA expression was assessed by analyzing the RNA from three biological replicates obtained under four conditions: M-OM-^A+ cells in glucose, M-OM-^A+ cells no glucose, M-OM-^A0 cells in glucose, M-OM-^A0 cells no glucose.
2013-12-31 | E-GEOD-49002 | biostudies-arrayexpress