Project description:Inferring in humans biological responses to external cues such as drugs, chemicals, viruses and hormones, is an essential question in biomedicine and cannot be easily studied in humans. Thus, biomedical research has continuously relied on animal models for studying the impact of these compounds and attempted to M-^StranslateM-^T the results to humans. In this context, the Systems Biology Verification for Industrial Methodology for Process Verification in Research (SBV IMPROVER) initiative had run a Species Translation Challenge for the scientific community to explore and understand the limit of translatability from rodent to human using systems biology. Therefore, a multi-layer omics dataset was generated that comprised of phosphoproteomics, transcriptomics and cytokine data derived from normal human (NHBE) and rat (NRBE) bronchial epithelial cells exposed in parallel to more than 50 different stimuli under identical conditions. The present manuscript describes in detail the experimental settings, the generation, processing and quality control analysis of the multi-layer omics dataset. The datasets are accessible in public repositories could be leveraged for further translation studies.
Project description:The Quartet Project aims to provide resources for QC of multiple types of omic technologies and the effective integration of diverse datasets from various scenarios. Large quantities of multi-omics materials, datasets, and best practices for their QC utilities were developed for whole process QC of large-scale, multi-center, and longitudinal multi-omics profiling.
Project description:Despite early clinical success, the mechanisms of action of low-dose interleukin-2 (LD-IL-2) immunotherapy remain only partly understood. This dataset was generated using samples from the DILfrequency clinical trail, to examine the effects of interval administration of low-dose recombinant IL-2 (iLD-IL-2) using high-resolution, single-cell multi-omics.
Project description:The development of single cell transcriptomic technologies yields large datasets comprising multimodal informations such as transcriptomes and immunophenotypes. Currently however, there is no software to easily and simultaneously analyze both types of data. Here, we introduce Single-Cell Virtual Cytometer, an open-source software for flow cytometry-like visualization and exploration of multi-omics single cell datasets. Using an original CITE-seq dataset of PBMC from an healthy donor, we illustrate its use for the integrated analysis of transcriptomes and epitopes of functional maturation in peripheral T lymphocytes from healthy donors. So this free and open-source algorithm constitutes a unique resource for biologists seeking for a user-friendly analytic tool for multimodal single cell datasets.
Project description:To further reveal the major cell types of developing pIVC embryos and underlying epigenetic dynamics, the optimized single-cell based multi-omics sequencing method scChaRM-seq was performed (Yan et al., 2021b). 1,862 single cells Bisulfite-seq datasets were further analyzed. We then performed multi-omics profiling analysis using data obtained from9 pIVC embryos at 8 sequential developmental stages.
Project description:To further reveal the major cell types of developing pIVC embryos and underlying epigenetic dynamics, the optimized single-cell based multi-omics sequencing method scChaRM-seq was performed (Yan et al., 2021b). After stringent filtration, 3,682 single cells RNA-seq datasets were further analyzed We then performed multi-omics profiling analysis using data obtained from9 pIVC embryos at 8 sequential developmental stages.
Project description:Time course extraction of the Yeast Metabolic cycle, followed by ATAC seq processing. These files are used in a multi-omics study to complement a series of datasets that cover other moluecular layers of the Yeast Metabolic Cycle, including metabolomics, gene expression and histone modificaiton datasets. Our goal in this project is to create statistical integratory tools to comprehend YMC regulatory mechanism.
Project description:Total RNA-seq of blasts derived 100 adult T-ALL cases, 211 AML cases and 13 mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). In addition, CD34+ HSPCs derived from 9 healthy donors are used as a control. Due to patient confidentiality considerations, the raw data files for this dataset have been deposited to the EGA controlled-access archive under the accession numbers EGAS00001007094 (study); EGAD00001011054, EGAD00001007646, EGAD00001007581 (datasets).
Project description:A Cartes d'Identite des Tumeurs (CIT) project from the french Ligue Nationale Contre le Cancer (http://cit.ligue-cancer.net: Oligodendroglial tumours (OT) are a heterogeneous group of gliomas. Three molecular subgroups are currently distinguished based on the IDH mutation and the 1p/19q co-deletion. Here we performed an integrated analysis of the transcriptome, genome and methylome of 156 OT. Beyond the 3 well-known molecular classes, our multi-omics classification revealed 3 subgroups within 1p/19q co-deleted tumours, associated with different expression patterns of oligodendroglial progenitor cell (OPC), astrocytic, oligodendrocytic and neuronal lineage genes. The validity of these 3 subgroups was confirmed on public datasets. The OPC-like group was associated with a more aggressive histological and genomic profile and with MYC activation that occurred through various alterations including MYC locus genomic gain, MYC exon 3 hypo-methylation and down-regulation of microRNA-34b/c. In the lower grade glioma TCGA dataset, the OPC-like group was associated with a poorer outcome independently from histological grade. Our study unravels previously unrecognized heterogeneity among 1p/19q co-deleted tumours.
Project description:A Cartes d'Identite des Tumeurs (CIT) project from the french Ligue Nationale Contre le Cancer (http://cit.ligue-cancer.net: Oligodendroglial tumours (OT) are a heterogeneous group of gliomas. Three molecular subgroups are currently distinguished based on the IDH mutation and the 1p/19q co-deletion. Here we performed an integrated analysis of the transcriptome, genome and methylome of 156 OT. Beyond the 3 well-known molecular classes, our multi-omics classification revealed 3 subgroups within 1p/19q co-deleted tumours, associated with different expression patterns of oligodendroglial progenitor cell (OPC), astrocytic, oligodendrocytic and neuronal lineage genes. The validity of these 3 subgroups was confirmed on public datasets. The OPC-like group was associated with a more aggressive histological and genomic profile and with MYC activation that occurred through various alterations including MYC locus genomic gain, MYC exon 3 hypo-methylation and down-regulation of microRNA-34b/c. In the lower grade glioma TCGA dataset, the OPC-like group was associated with a poorer outcome independently from histological grade. Our study unravels previously unrecognized heterogeneity among 1p/19q co-deleted tumours.