Project description:RNA-seq of inner pericarp, outer pericarp and endosperm layers from developing grain of bread wheat cv. Holdfast at 12 days post-anthesis
Project description:Transcriptional profiles of cultured human breast cancer cell lines treated with small molecules: 5 cell lines are treated with 109 small molecules and the expression of 978 representative genes is measured, as a part of the LINCS Joint Project.
Project description:Transcriptional profiles of multiple cells treated with small molecule perturbagens: The expression level is measured for 978 representative genes in 15 cell lines treated with 241 small molecules.
Project description:RNA-Seq was carried out on isolated mRNA obtained from induced Pluripotnent Stem Cells (iPSC) cell lines. These lines were derived from ALS, SMA and Control (unaffected) individuals (three of each).
Project description:Transcriptional profiles of multiple cell and perturbation types: cells are treated with chemical and genetic perturbations. The expression level for 978 representative genes is measured.
Project description:Transcriptional profiles of multiple cell and perturbation types: cells are treated with chemical perturbagens and CRISPR reagents. The expression level for 978 representative genes is measured.
Project description:Each of 914 cell samples either at the control condition or treated with FDA-approved cancer drugs is sequenced by the single-ended 3'-DGE mRNA-sequencing method with a read length of 46 base pairs, and a total of 914 raw sequence data files in the FASTQ format are generated. These sequence data files are then analyzed by a high-performance computational pipeline and ranked lists of gene signatures and biological processes related to drug-induced cardiotoxicity are generated for each drug. The raw sequence datasets and the analysis results have been carefully controlled for data quality, and they are made publicly available at the Gene Expression Omnibus (GEO) database repository of NIH. As such, this broad drug-stimulated transcriptomi dataset is valuable for the prediction of drug toxicities and their mitigations.
