Project description:The pathogenesis of Colorectal cancer (CRC) metastasis remains unclear.We collect clinical data from our center and use Integrative omics to analyze and predict candidate biomarkers of colorectal cancer and distant metastasis.
Project description:we conducted integrative multiple levels of omics data including transcriptome, phosphoproteome, proteome and metabolome in different time-course of sepsis-associated liver dysfunction (SALD). This is the first trial to suggest the statistical pathway of integrative multi-omics data in sepsis. Given the increasing number of studies collecting multi-omics data but limited overview of the methodological framework for integrative analyses (Liu, Ding et al. 2013, Petersen, Zeilinger et al. 2014, Shah, Bonder et al. 2015), integrative approach in sepsis with liver dysfunction in this study will provide novel insights into the development of sepsis and ultimately offer new tools for overcoming the present diagnostic limitation. Therefore, a combined multi-omics dataset will give better accessibility of adoption in disease, and insight to identify the promising candidates for therapeutic strategies.
Project description:38 paires of tumor tissues and adjacent non-tumor tissues from HCC patients The number of known lncRNAs increased sharply upon the tiling microarrays and RNA-sequencing were applied to identify lncRNAs. However, only about a dozen of lncRNAs have been well characterized and demonstrated to be tightly associated with development and progression of HCC. A major challenge remains to identify functional lncRNAs associated with HCC. Previous reports mainly selected differentially expressed lncRNAs in cancer tissue or cell lines as candidates for further validation and characterizing. Here, based on mRNA and lncRNA gene expression profiles data collected from tumor and adjacent normal tissues of thirty-eight HCC patients, we adapted integrative omics strategy to identify HCC-associated lncRNAs.
Project description:38 paires of tumor tissues and adjacent non-tumor tissues from HCC patients The number of known lncRNAs increased sharply upon the tiling microarrays and RNA-sequencing were applied to identify lncRNAs. However, only about a dozen of lncRNAs have been well characterized and demonstrated to be tightly associated with development and progression of HCC. A major challenge remains to identify functional lncRNAs associated with HCC. Previous reports mainly selected differentially expressed lncRNAs in cancer tissue or cell lines as candidates for further validation and characterizing. Here, based on mRNA and lncRNA gene expression profiles data collected from tumor and adjacent normal tissues of thirty-eight HCC patients, we adapted integrative omics strategy to identify HCC-associated lncRNAs.
Project description:DNA methylation array data generated from epidermal samples (suction blister roofs) of healthy female subjects between 21 and 76 years. Aim of the project was the investigation of non-linearities in the human aging progression using an integrative multi-omics analysis. DNA was extracted from suction blisters taken from the volar forearms of each subject, bisulfite converted, and profiled using Illumina Infinium HumanMethylation450 BeadChip arrrays.
Project description:DNA methylation array data generated from epidermal samples (suction blister roofs) of healthy female subjects between 21 and 76 years. Aim of the project was the investigation of non-linearities in the human aging progression using an integrative multi-omics analysis. DNA was extracted from suction blisters taken from the volar forearms of each subject, bisulfite converted, and profiled using Illumina Infinium MethylationEPIC BeadChip arrrays.
Project description:The main goal of the project is to develop a new generation of bioinformatics resources for the integrative analysis of multiple types of omics data. These resources include both novel statistical methodologies as well as user-friendly software implementations. STATegra methods address many aspects of the omics data integration problem such as the design of multiomics experiments, integrative transcriptional and regulatory networks, integrative variable selection, data fusion, integration of public domain data, and integrative pathway analysis. To support method development STATegra uses a model biological system, namely the differentiation process of mouse pre-B-cells. The STATegra consortium generated data focused on a critical step in the differentiation of B lymphocytes, which are key components of the adaptive immune system. Transcription factors of the Ikaros family are central to the normal differentiation of B cell progenitors and their expression increases in response to developmental stage-specific signals to terminate the proliferation of B cell progenitors and to initiate their differentiation. In particular, a novel biological system that models the transition from the pre-BI stage to the pre-BII subsequent stage, where B cell progenitors undergo growth arrest and differentiation, was used. The approach involves a pre-B cell line, B3 , and an inducible version of the Ikaros transcription factor, Ikaros-ERt2. Ikaros factors act to down-regulate genes that drive proliferation and to simultaneously up-regulate the expression of genes that promote the differentiation of B cell progenitors. Hence, in the B3 system, before induction of Ikaros, cells proliferate and their gene expression pattern is similar to proliferating B cell progenitors in vivo. Following Ikaros induction, B3 cells undergo gene expression changes that resemble those that occur in vivo during the transition from cycling to resting pre-B cells, followed by a marked reduction in cellular proliferation and by G1 arrest. On this system the consortium has created a high-quality data collection consisting of a replicated time course using seven different omics platforms: RNA-seq, miRNA-seq, ChIP-seq, DNase-seq, Methyl-seq, proteomics and metabolomics, which is used to assess and to validate STATegra methods.