GenomicScape: an easy-to-use web tool for gene expression data analysis. Application to investigate the molecular events in the differentiation of B cells into plasma cells.
ABSTRACT: DNA microarrays have considerably helped to improve the understanding of biological processes and diseases. Large amounts of publicly available microarray data are accumulating, but are poorly exploited due to a lack of easy-to-use bioinformatics resources. The aim of this study is to build a free and convenient data-mining web site (www.genomicscape.com). GenomicScape allows mining dataset from various microarray platforms, identifying genes differentially expressed between populations, clustering populations, visualizing expression profiles of large sets of genes, and exporting results and figures. We show how easily GenomicScape makes it possible to construct a molecular atlas of the B cell differentiation using publicly available transcriptome data of naïve B cells, centroblasts, centrocytes, memory B cells, preplasmablasts, plasmablasts, early plasma cells and bone marrow plasma cells. Genes overexpressed in each population and the pathways encoded by these genes are provided as well as how the populations cluster together. All the analyses, tables and figures can be easily done and exported using GenomicScape and this B cell to plasma cell atlas is freely available online. Beyond this B cell to plasma cell atlas, the molecular characteristics of any biological process can be easily and freely investigated by uploading the corresponding transcriptome files into GenomicScape.
Project description:The amount of biological data, generated with (single cell) omics technologies, is rapidly increasing, thereby exacerbating bottlenecks in the data analysis and interpretation of omics experiments. Data mining platforms that facilitate non-bioinformatician experimental scientists to analyze a wide range of experimental designs and data types can alleviate such bottlenecks, aiding in the exploration of (newly generated or publicly available) omics datasets. Here, we present BIOMEX, a browser-based software, designed to facilitate the Biological Interpretation Of Multi-omics EXperiments by bench scientists. BIOMEX integrates state-of-the-art statistical tools and field-tested algorithms into a flexible but well-defined workflow that accommodates metabolomics, transcriptomics, proteomics, mass cytometry and single cell data from different platforms and organisms. The BIOMEX workflow is accompanied by a manual and video tutorials that provide the necessary background to navigate the interface and get acquainted with the employed methods. BIOMEX guides the user through omics-tailored analyses, such as data pretreatment and normalization, dimensionality reduction, differential and enrichment analysis, pathway mapping, clustering, marker analysis, trajectory inference, meta-analysis and others. BIOMEX is fully interactive, allowing users to easily change parameters and generate customized plots exportable as high-quality publication-ready figures. BIOMEX is open source and freely available at https://www.vibcancer.be/software-tools/biomex.
Project description:small RNA profiles of 6 human tonsillar B cell populatios (naive B cells, pre-germinal center B cells, centrocytes, centroblasts, memory B cells, and plasma cells) were determined by deep sequencing. These samples were compared to mouse developing lymphocytes, various hematopoietic cell lineages, and tissues. small RNA expression profiles of 6 well defined B cell populations isolated from human tonsils.
Project description:small RNA profiles of 6 human tonsillar B cell populatios (naive B cells, pre-germinal center B cells, centrocytes, centroblasts, memory B cells, and plasma cells) were determined by deep sequencing. These samples were compared to mouse developing lymphocytes, various hematopoietic cell lineages, and tissues. Overall design: small RNA expression profiles of 6 well defined B cell populations isolated from human tonsils.
Project description:Beef quality is a complex phenotype that can be evaluated only after animal slaughtering. Previous research has investigated the potential of genetic markers or muscle-derived proteins to assess beef tenderness. Thus, the use of low-invasive biomarkers in living animals is an issue for the beef sector. We hypothesized that publicly available data may help us discovering candidate plasma biomarkers. Thanks to a review of the literature, we built a corpus of articles on beef tenderness. Following data collection, aggregation, and computational reconstruction of the muscle secretome, the putative plasma proteins were searched by comparison with a bovine plasma proteome atlas and submitted to mining of biological information. Of the 44 publications included in the study, 469 unique gene names were extracted for aggregation. Seventy-one proteins putatively released in the plasma were revealed. Among them 13 proteins were predicted to be secreted in plasma, 44 proteins as hypothetically secreted in plasma, and 14 additional candidate proteins were detected thanks to network analysis. Among these 71 proteins, 24 were included in tenderness quantitative trait loci. The in-silico workflow enabled the discovery of candidate plasma biomarkers for beef tenderness from reconstruction of the secretome, to be examined in the cattle plasma proteome.
Project description:A gene expression atlas is an essential resource to quantify and understand the multiscale processes of embryogenesis in time and space. The automated reconstruction of a prototypic 4D atlas for vertebrate early embryos, using multicolor fluorescence in situ hybridization with nuclear counterstain, requires dedicated computational strategies. To this goal, we designed an original methodological framework implemented in a software tool called Match-IT. With only minimal human supervision, our system is able to gather gene expression patterns observed in different analyzed embryos with phenotypic variability and map them onto a series of common 3D templates over time, creating a 4D atlas. This framework was used to construct an atlas composed of 6 gene expression templates from a cohort of zebrafish early embryos spanning 6 developmental stages from 4 to 6.3 hpf (hours post fertilization). They included 53 specimens, 181,415 detected cell nuclei and the segmentation of 98 gene expression patterns observed in 3D for 9 different genes. In addition, an interactive visualization software, Atlas-IT, was developed to inspect, supervise and analyze the atlas. Match-IT and Atlas-IT, including user manuals, representative datasets and video tutorials, are publicly and freely available online. We also propose computational methods and tools for the quantitative assessment of the gene expression templates at the cellular scale, with the identification, visualization and analysis of coexpression patterns, synexpression groups and their dynamics through developmental stages.
Project description:Whole genome gene expression microarrays made it possible to pick up genes, whose products could be involved in a biological pathway. The aim of this study is to provide easy tools to interrogate whole genome transcriptome changes in the process of plasma cell generation, starting from naive B cells and comprising centroblasts, centrocytes, memory B cells, preplasmablasts, plasmablasts, early plasma cells, and mature bone marrow plasma cells.
Project description:Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.
Project description:High throughput bisulfite sequencing (BS-seq) is an important technology to generate single-base DNA methylomes in both plants and animals. In order to accelerate the data analysis of BS-seq data, toolkits for visualization are required.ViewBS, an open-source toolkit, can extract and visualize the DNA methylome data easily and with flexibility. By using Tabix, ViewBS can visualize BS-seq for large datasets quickly. ViewBS can generate publication-quality figures, such as meta-plots, heat maps and violin-boxplots, which can help users to answer biological questions. We illustrate its application using BS-seq data from Arabidopsis thaliana.ViewBS is freely available at: https://github.com/xie186/ViewBS.firstname.lastname@example.org.Supplementary data are available at Bioinformatics online.
Project description:BACKGROUND:Fractalkine/CX(3)CL1, a surface chemokine, binds to CX(3)CR1 expressed by different lymphocyte subsets. Since CX(3)CL1 has been detected in the germinal centres of secondary lymphoid tissue, in this study we have investigated CX(3)CR1 expression and function in human naïve, germinal centre and memory B cells isolated from tonsil or peripheral blood. METHODOLOGY/PRINCIPAL FINDINGS:We demonstrate unambiguously that highly purified human B cells from tonsil and peripheral blood expressed CX(3)CR1 at mRNA and protein levels as assessed by quantitative PCR, flow cytometry and competition binding assays. In particular, naïve, germinal centre and memory B cells expressed CX(3)CR1 but only germinal centre B cells were attracted by soluble CX(3)CL1 in a transwell assay. CX(3)CL1 signalling in germinal centre B cells involved PI3K, Erk1/2, p38, and Src phosphorylation, as assessed by Western blot experiments. CX(3)CR1(+) germinal centre B cells were devoid of centroblasts and enriched for centrocytes that migrated to soluble CX(3)CL1. ELISA assay showed that soluble CX(3)CL1 was secreted constitutively by follicular dendritic cells and T follicular helper cells, two cell populations homing in the germinal centre light zone as centrocytes. At variance with that observed in humans, soluble CX(3)CL1 did not attract spleen B cells from wild type mice. OVA immunized CX(3)CR1(-/-) or CX(3)CL1(-/-) mice showed significantly decreased specific IgG production compared to wild type mice. CONCLUSION/SIGNIFICANCE:We propose a model whereby human follicular dendritic cells and T follicular helper cells release in the light zone of germinal centre soluble CX(3)CL1 that attracts centrocytes. The functional implications of these results warrant further investigation.
Project description:Determine the expression pattern of in vitro cultured human centrocytes, plasma and memory B cells. Keywords: Memory B cells, In vitro culture, Human Overall design: Cy3-labelled lymphoma cell lines and Cy5-labelled treated sample were hybridized to a Lymphochip microarray.