TraIT Cell Line use case
Ontology highlight
ABSTRACT:
PROVIDER: EGAS00001001476 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Eleven NSCLC cell lines with widely divergent gefitinib sensitivities were compared using gene expression. Genes associated with gefitinib response were used to classify additional NSCLC lines with unknown gefitnib sensitivity. A subset of the test set data was tested for gefitinib sensitivity, and results correlated strongly with the gene expression-based predictions All eleven training set lines, and seven test set lines had both HGU133A and B chips done, while other test set lines had only HGU133As. Keywords: cell response comparison
2006-08-31 | GSE4342 | GEO
Project description:The goal of the proposed project is to generate high-quality detailed transcript and chromatin status datasets from a comprehensive set of bovine tissues, developmental stages, and cells through a set of rationally selected assays. Experimental data generated by the project have been analyzed using well-established bioinformatics software and pipelines to discover and annotate the functional elements of the bovine genome, including enhancers, promoters, insulators, and small and large RNA transcripts, among others. Datasets and findings will be made publicly available though database and genome information centers.
2022-10-13 | E-MTAB-12052 | biostudies-arrayexpress
Project description:Experimental set accompanying Giacomini et al publication "A legacy gene-expression signature of genetic instability in colon cancer". Includes 18 colon cancer cell line training set, 13 colon cancer cell line test set, and 3 cell lines (HCT116, HCT116+ch2, HCT116+ch3) used to evaluate signature after correcting underlying genetic instability. Experiments were performed by comparing mRNA from each colon cancer cell line (Cy5; channel 2) to a "universal" mRNA reference (Cy3; channel 1). A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Series type: disease_state_design Series_overall_design: Using regression correlation Keywords: other
2005-12-06 | GSE2591 | GEO
Project description:Conformation capture-approaches like Hi-C can elucidate chromosome structure at a genome-wide scale. Hi-C datasets are large and require specialised software. Here, we present GENOVA: a user-friendly software package to analyse and visualise conformation capture data. GENOVA is an R-package that includes the most common Hi-C analyses, such as compartment and insulation score analysis. It can create annotated heatmaps to visualise the contact frequency at a specific locus and aggregate Hi-C signal over a user-specified genomic regions such as ChIP-seq data. Finally, our package supports output from the major mapping-pipelines. We demonstrate the capabilities of GENOVA by analysing Hi-C data from HAP1 cell lines in which the cohesin-subunits SA1 and SA2 were knocked out. We find that ΔSA1 cells gain intra-TAD interactions and increase compartmentalisation. ΔSA2 cells have longer loops and a less compartmentalised genome. These results suggest that cohesinSA1 forms longer loops, while cohesinSA2 plays a role in forming and maintaining intra-TAD interactions. The differences in loop-forming activity affect whole chromosome organisation consistent with a model where loops and compartments counterbalance each other. We show that GENOVA is an easy to use R-package, that allows researchers to explore Hi-C data in great detail.
2021-06-03 | GSE160490 | GEO
Project description:To test whether hide or fish glue was used to fix bark decorations to the body of a wood bow (of a different species) found during the archaeological excavation of at the site “Parkhaus-Opéra” in Zurich (Switzerland), dated by dendrochronology to 3175 - 3157 BC, we attempted extraction of collagen residues from wood flakes collected from two areas of the bow surface under the bark and their identification by on-line nanoflow reversed-phase C18 liquid chromatography tandem mass spectrometry.
2015-08-20 | PXD001727 | Pride
Project description:Despite the widespread adoption of ChIP-seq there is still no consensus on quality assessment metrics. No single published metric can reliably discriminate the success or failure of an experiment, thus hampering objectivity and reproducibility of quality control. We introduce a new framework for ChIP-seq data quality assessment that overcomes the limitation of previous solutions. Our tool called "ChIC" incorporates a novel set of quality control metrics integrated into one single score summarizing the sample quality and a reference compendium with thousands of published ChIP-seq samples, for easier evaluation of new data. This test dataset contain an example of succesfull and non-succesfull ChIP-seq sample for mouse H3K27me3.
2024-12-12 | GSE119932 | GEO
Project description:To understand expression phenotypes in Arabidopsis thaliana at high temperature, a set of the Multiparent Advanced Generation Inter-Cross (MAGIC) recombinant inbred lines were grown under long day conditions (16 hours light: 8 hours dark) at 20˚C and then shifted to 30˚C for 48 hours. RNA was collected at the 4th true leaf stage, and RNA-seq data was generated using the Illumina method. The MAGIC lines included are largely a subset of lines for which comparable RNA-seq data was collected at a constant 20˚C (allowing the same genotypes to be compared at 20˚C and after the high temperature shift at approximately the same developmental stage and under otherwise the same growth conditions). The read data for the analogous low temperature study can be found under GEO submission GSE94107.
2017-04-22 | GSE96812 | GEO
Project description:Hass2017-PanRTK model for single cell
line
The model structure comprises
heterodimerization and receptor trafficking as described in detail
in the article below. For ligand input, set a respective
event. The illustrated event sets the EGF concentration to 2.5 nMol
in the model file.
This model is described in the article:
Predicting ligand-dependent
tumors from multi-dimensional signaling features.
Hass H, Masson K, Wohlgemuth S,
Paragas V, Allen JE, Sevecka M, Pace E, Timmer J, Stelling J,
MacBeath G, Schoeberl B, Raue A.
NPJ Syst Biol Appl 2017; 3: 27
Abstract:
Targeted therapies have shown significant patient benefit in
about 5-10% of solid tumors that are addicted to a single
oncogene. Here, we explore the idea of ligand addiction as a
driver of tumor growth. High ligand levels in tumors have been
shown to be associated with impaired patient survival, but
targeted therapies have not yet shown great benefit in
unselected patient populations. Using an approach of applying
Bagged Decision Trees (BDT) to high-dimensional signaling
features derived from a computational model, we can predict
ligand dependent proliferation across a set of 58 cell lines.
This mechanistic, multi-pathway model that features receptor
heterodimerization, was trained on seven cancer cell lines and
can predict signaling across two independent cell lines by
adjusting only the receptor expression levels for each cell
line. Interestingly, for patient samples the predicted tumor
growth response correlates with high growth factor expression
in the tumor microenvironment, which argues for a co-evolution
of both factors in vivo.
This model is hosted on
BioModels Database
and identified by:
MODEL1708210000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
| MODEL1708210000 | BioModels