99 research outputs found

    Neighbourhood graph of the gene BRCA1 according to the Lieberman-Aiden et al. experiment.

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    <p>Gene expression data about colon cancer experiment GDS3160 have been mapped on the graph to show the enhanced description (and prediction) power that the graph representation has in relation to gene co-expression with respect to the approach relying on genomic coordinates.</p

    Proteins in network proximity to HCV targets are highly enriched with lists of proteins proposed as regulators of host response to HCV and involved in HCC.

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    <p>The table lists the log<sub>10</sub> of the <i>p</i>-values that estimate the probability of obtaining, by chance (hypergeometric test), the observed overlap between the list of proteins from the literature (source and description, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113660#pone.0113660.s004" target="_blank">Tab. S2</a>) and the top ranked 1,500 proteins in network proximity to HCV on the basis of <i>s<sub>i</sub></i> or <i>p<sub>i</sub></i>, including or excluding HCV targets; NORM  =  normal, CIR  =  cirrhosis, DYS  =  dysplasia, eHCC  =  early HCC, aHCC  =  advanced HCC.</p><p>Proteins in network proximity to HCV targets are highly enriched with lists of proteins proposed as regulators of host response to HCV and involved in HCC.</p

    Challenges in building an e-Health infrastructure for P5 Medicine

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    <p>e-Health, as a community, is facing Big Data issues from very different viewpoints.. The approaches used in P5 Medicine (Personalized, Predictive, Preventive and Participatory, Population) require a radical change in mentality and a close collaboration between physicians, patients and computer scientists from a variety of research areas. P5 Medicine will<br>shape the four previous “P”s address medical problems in more encompassing integrative ways, creating complete Population perspectives. In this paper we present some of the key challenges involved in building a P5 framework based on the Cloud and Big Data prospective including privacy, anonymity and genetic awareness and education.. This consideration may help in raising awareness about the challenges of P5 in the build-up of personalised medicine.<br><br></p> <p> </p

    NuChart: An R Package to Study Gene Spatial Neighbourhoods with Multi-Omics Annotations

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    <div><p>Long-range chromosomal associations between genomic regions, and their repositioning in the 3D space of the nucleus, are now considered to be key contributors to the regulation of gene expression and important links have been highlighted with other genomic features involved in DNA rearrangements. Recent Chromosome Conformation Capture (3C) measurements performed with high throughput sequencing (Hi-C) and molecular dynamics studies show that there is a large correlation between colocalization and coregulation of genes, but these important researches are hampered by the lack of biologists-friendly analysis and visualisation software. Here, we describe NuChart, an R package that allows the user to annotate and statistically analyse a list of input genes with information relying on Hi-C data, integrating knowledge about genomic features that are involved in the chromosome spatial organization. NuChart works directly with sequenced reads to identify the related Hi-C fragments, with the aim of creating gene-centric neighbourhood graphs on which multi-omics features can be mapped. Predictions about CTCF binding sites, isochores and cryptic Recombination Signal Sequences are provided directly with the package for mapping, although other annotation data in bed format can be used (such as methylation profiles and histone patterns). Gene expression data can be automatically retrieved and processed from the Gene Expression Omnibus and ArrayExpress repositories to highlight the expression profile of genes in the identified neighbourhood. Moreover, statistical inferences about the graph structure and correlations between its topology and multi-omics features can be performed using Exponential-family Random Graph Models. The Hi-C fragment visualisation provided by NuChart allows the comparisons of cells in different conditions, thus providing the possibility of novel biomarkers identification. NuChart is compliant with the Bioconductor standard and it is freely available at <a href="ftp://fileserver.itb.cnr.it/nuchart" target="_blank">ftp://fileserver.itb.cnr.it/nuchart</a>.</p> </div

    Enrichment in HCV targets of differentially expressed genes in preneoplastic and neoplastic liver lesions.

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    <p><i>P</i>-values (<i>p</i>) were computed with Gene Set Enrichment Analysis (GSEA) and hypergeometric (hyper) test.</p><p>Enrichment in HCV targets of differentially expressed genes in preneoplastic and neoplastic liver lesions.</p

    Representation of the OCT4 (official name POU5F1) neighbourhood graphs in four different runs from the Hi-C experiments of Dixon et al. to show inter and intra run modifications.

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    <p>In the panel a) and b) on the top part of the figure, the sequencing runs are from human embryonic stem cells (hESC), while panel c) and d) are from human foetal lung fibroblasts (IMR-90).</p

    Normalization of chromosome 17 Hi-C data according to the Lieberman-Aiden et al. experiment.

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    <p>In panel a) the Hu et al. normalization is shown, while in panel b) the read-based normalization performed with NuChart (threshold 0.9) is presented to show the reproducibility with respect to the Hu et al. approach. Panel c) represents the NuChart read-based normalization performed using a more restrictive threshold (threshold 0.99).</p

    Top ranked proteins in network proximity to HCV targets.

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    <p>The top ranked 1,500 proteins by network proximity score <i>s<sub>i</sub></i> (on the right of the dotted vertical line) or <i>p</i>-values (above the dotted horizontal line); red: HCV targets; black: non-HCV targets; point size is proportional to the number of interactions; labels indicate the top 10 of each ranking.</p
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