Sample 1
Ontology highlight
ABSTRACT:
TISSUE(S): ['cavernous Body']
SUBMITTER: Yifan Zhao
PROVIDER: OEX00013837 | NODE | 2022-03-29
REPOSITORIES: NODE
Similar Datasets
Project description:Microbiome sample-material model is a Named Entity Recognition (NER) model that identifies and annotates the material of microbiome samples in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with sample-material metadata from literature.
For more information, please refer to the following blogs:
http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html
https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications
2022-02-21 | MODEL2202170001 | BioModels
Project description:Swarming sample vs non-swarming sample
| PRJNA634263 | ENA
Project description:soil sample and root sample Raw sequence reads
| PRJNA859432 | ENA
Project description:This sample is part of a study that compares small sample amplification technologies. The analysis looks at differential gene expression when compared to one round of T7 amplification. A tumor cell line was used in comparison to a human reference RNA in this study. Keywords = amplification Keywords = small sample Keywords = Affymetrix Keywords: other
2005-06-03 | GSE2723 | GEO
Project description:Sample multiplexed scRNA-seq is a promising strategy to overcome current barriers in high cost and potential technical variations by multiple scRNA-seq tests. In this study, we developed a highly efficienct novel sample barcode labeling method using DNA-encoded Lipid Nanoparticles ('Nanocoding') that could label cells with minimal dependence on their type or sample conditions. This method provids a roubust and general protocol for sample barcoding and multiplexing in scRNA-seq. We demonstrated the performance of Nanocoding through three scRNA-seq studies, which include: 1. mouse spleen cells mix (one dataset including 6 mouse spleen tissues samples); 2. HeLa-mouse Stromal Vascular Fraction(SVF) cells mix (one dataset containing mixed HeLa cell and SVF cell); 3. Aged-Young SVF cells mix (one dataset containing two SVF samples) tests. These studies showcased the biomodal distribution of barcode counts in different models with high signal-to-background ratio, as well as pan-cell labeling activity for efficient and accurate sample-multiplexing. By using Nanocoding, we profiled obsity and age related change in lipid metabolism associated genes or inflammatory related features, in various cell types from spleen or adipose tissues.
2024-12-09 | GSE280262 | GEO