Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Data Storage Based on Combinatorial Synthesis of DNA Shortmers

ABSTRACT: Data Storage Based on Combinatorial Synthesis of DNA Shortmers - Proof of concept

PROVIDER: PRJEB63282 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Efficient DNA based data storage using shortmer combinatorial encoding

Project description:Combinatorial DNA storage

| PRJEB71104 | ENA

GNPS-CMMC-library - Combinatorial Synthesis with Decarboxylated Amino Acids

Project description:Combinatorial synthesis of tryptamine, tyramine, serotonin, dopamine, putrescine, cadaverine, agmatine, histamine, and phenethylamine with acetic anhydride, butyric anhydride, acryloyl chloride, hexanoyl chloride, decanoyl chloride, palmitoyl chloride, and stearoyl chloride respectively. The files also contain the synthesis of tryptamine specifically with acetic anhydride and butyric anhydride respectively.

2023-12-29 | MSV000093735 | GNPS

Nanowell-based single-cell combinatorial indexed ATAC-seq

Project description:Nanowell-based single-cell combinatorial indexed ATAC-seq

| PRJNA906077 | ENA

Combinatorial blood platelets-derived circRNA and mRNA signature for early-stage lung cancer

Project description:Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often 30 exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results 31 in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an Area Under the Curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 40 circRNA) enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection.

2023-06-23 | GSE225787 | GEO

A modular dCas9-based recruitment platform for combinatorial epigenome editing

Project description:A modular dCas9-based recruitment platform for combinatorial epigenome editing

| PRJNA1008191 | ENA

Transcriptome changes in soybean seeds resulting from RNAi suppression of seed storage proteins

Project description:We report the changes in transcriptome that results from RNAi suppression of 7S and 11S storage protein synthesis in soybean seeds, Proteomic results show that other proteins compensate for the storage protein shortfall. The transcriptome analysis show numerous changes result from suppressing storage proteins but compensating proteins are accumulated without parallel changes in the protein's transcription. Total RNA samples prepared from midmaturation wildtype and storage protein RNAi (SP-) soybeans were used to produce cDNA that was subjected to Illumina sequencing. The resulting sequences were assembled and analyzed to characterize the changes in transcriptome that results from suppressing storage protein synthesis/accumulation.

2010-05-01 | E-GEOD-21116 | biostudies-arrayexpress

Transcriptome changes in soybean seeds resulting from RNAi suppression of seed storage proteins

2010-05-01 | GSE21116 | GEO

Metabolic profiling and transcriptome analysis provide insights into the accumulation of flavonoids in chayote fruit during storage

Project description:Chayote (Sechium edule) fruits are rich in flavonoids, folate, and low-calorie food. However, studies about the flavonoids and regulatory mechanism of flavonoid synthesis in chayote fruits was still unclear. In present study, a transcriptome analysis and metabolite profiling of chayote fruits at three different storage stages were conducted to explore the flavonoid compositions and gene expression associated with flavonoid synthesis. Through the UPLC-MS/MS analysis, a total of 57 flavonoid compounds were detected. Of these, 42 flavonoid glycosides were significantly differential accumulation in chayote fruits at three different storage stages. Many genes associated with flavonoid synthesis were differentially expressed in chayote fruits at three different storage stages through RNA-seq analysis, including structural genes and some TFs. There was a high correlation between RNA-seq analysis and metabolite profiling, and the expression level of candidate genes in the flavonoid synthesis pathway were consistent with the dynamic changes of flavonoids. In addition, one R2R3-MYB transcription factor, FSG0057100, was defined as the critical regulatory gene of flavonoid synthesis. Furthermore, we treated chayote fruits during storage with phenylalanine, and the results show exogenous phenylalanine applications might promote the flavonoid synthesis. Phenylalanine is a effective additive to maintain or improve the total content flavonoids in chayote fruit during storage, can apply the phenylalanine in the postharvest storage of chayote. The above results not only make us better understand the molecular mechanism of flavonoid synthesis in chayote fruits, but also contribute to the promotion and application of chayote products.

2022-10-05 | GSE214641 | GEO

Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility

Project description:Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility

| PRJNA509250 | ENA

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Project description:Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

| PRJNA723762 | ENA