Project description:Pre-clinical studies reported the immunogenic or immunomodulatory effects of traditional cancer therapies. However, the publicly available well-curated and harmonized breast cancer datasets, such as TCGA, METABRIC, and MetaGxBreast, lack careful curation of treatment regimens. Hence, limited exploration of the impact of therapies on the prognostic/predictive value of breast cancer biomarkers. Herein, we describe a pooled and treatment-curated gene-expression dataset to investigate the impact of treatments on the prognostic/predictive value of biomarkers. We searched the gene expression omnibus database to identify potential human breast cancer gene-expression datasets with anthracycline/taxane treatment. Published datasets with the detailed treatment regimen, clinical endpoint, clinical-pathological, and gene-expression data were extracted and harmonized. The dataset described herein would help researchers explore the interaction between gene-expression biomarkers and immunogenic/immunomodulatory treatments in breast cancer.
Project description:This is a collection of diverse publicly available macrophage datasets. The datasets belong to three different platforms and all normalized together using modified CDF files.
Project description:We report a ChIP-seq analysis of the CpG binding protein CFP1 and related proteins to study regulation of chromatin in transcribed genes in erythroblasts and EBV-transformed B-lymphoblasts. We generated multiple ChIP datasets and compared these to publicly available ChIP datasets.
Project description:This is a collection of diverse publicly available mouse macrophage datasets and is also a subset of GSE119085. The datasets belong to GPL1261 platform and all normalized together with samples from GSE119085.
Project description:Exploring the sequence specificity of methylation by DNMT3A and DNMT3B using bisulfite sequencing in E.coli and publicly available mouse and human datasets
Project description:Reference datasets are often used to compare, interpret or validate experimental data and analytical methods. In the field of gene expression, a dozen reference datasets have been published. Typically, they consist of individual baseline or spike-in experiments carried out in a single laboratory and representing a particular set of conditions. For most organisms, however, few or no such reference datasets are publicly available. Here, we describe a new type of datasets highly representative for the spatial, temporal and response dimensions of gene expression. They result from integrating expression data from a large number of globally normalized and quality controlled public experiments and aggregating results by anatomical parts, stages of development, perturbations, drugs, diseases, neoplasms, and genotypes. The proposed datasets were created for human and several model organisms and are publicly available at www.expressiondata.org.