Visualization of orthogonal (disjoint) or overlapping datasets is a common task in bioinformatics. Few tools exist to automate the generation of extensively-customizable, high-resolution Venn and Euler diagrams in the R statistical environment. To fill this gap we introduce VennDiagram, an R package that enables the automated generation of highly-customizable, high-resolution Venn diagrams with up to four sets and Euler diagrams with up to three sets. The VennDiagram package offers the user the ability to customize essentially all aspects of the generated diagrams, including font sizes, label styles and locations, and the overall rotation of the diagram. We have implemented scaled Venn and Euler diagrams, which increase graphical accuracy and visual appeal. Diagrams are generated as high-definition TIFF files, simplifying the process of creating publication-quality figures and easing integration with established analysis pipelines. The VennDiagram package allows the creation of high quality Venn and Euler diagrams in the R statistical environment.

https://escholarship.org/content/qt81v9826m/qt81v9826m.pdf?t=qaej4g

VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R

Xenograft studies indicate that some solid tumors and leukemias are organized as cellular hierarchies sustained by cancer stem cells (CSCs). Despite the promise of the CSC model, its relevance in humans remains uncertain. Here we show that acute myeloid leukemia (AML) follows a CSC model on the basis of sorting multiple populations from each of 16 primary human AML samples and identifying which contain leukemia stem cells (LSCs) using a sensitive xenograft assay. Analysis of gene expression from all functionally validated populations yielded an LSC-specific signature. Similarly, a hematopoietic stem cell (HSC) gene signature was established. Bioinformatic analysis identified a core transcriptional program shared by LSCs and HSCs, revealing the molecular machinery underlying 'stemness' properties. Both stem cell programs were highly significant independent predictors of patient survival and were found in existing prognostic signatures. Thus, determinants of stemness influence the clinical outcome of AML, establishing that LSCs are clinically relevant and not artifacts of xenotransplantation.

Stem cell gene expression programs influence clinical outcome in human leukemia

https://www.linkgroup.hu/docs/13PharmTher.pdf

Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review

Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

/pdf/the-biopax-community-standard-for-pathway-data-sharing-3n69qjgpjh.pdf

The BioPAX community standard for pathway data sharing

Several recent studies in a number of model systems including zebrafish, Arabidopsis, and mouse have revealed phenotypic differences between knockouts (i.e., mutants) and knockdowns (e.g., antisense-treated animals). These differences have been attributed to a number of reasons including off-target effects of the antisense reagents. An alternative explanation was recently proposed based on a zebrafish study reporting that genetic compensation was observed in egfl7 mutant but not knockdown animals. Dosage compensation was first reported in Drosophila in 1932, and genetic compensation in response to a gene knockout was first reported in yeast in 1969. Since then, genetic compensation has been documented many times in a number of model organisms; however, our understanding of the underlying molecular mechanisms remains limited. In this review, we revisit studies reporting genetic compensation in higher eukaryotes and outline possible molecular mechanisms, which may include both transcriptional and posttranscriptional processes.

/pdf/genetic-compensation-a-phenomenon-in-search-of-mechanisms-5faa0c6mrl.pdf

Genetic compensation: A phenomenon in search of mechanisms.

Background
MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome – referred to as the micronome – to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal — mirDIP (http://ophid.utoronto.ca/mirDIP).

/pdf/navigating-the-micronome-using-multiple-microrna-prediction-3yk4gzenqh.pdf

NAViGaTing the Micronome – Using Multiple MicroRNA Prediction Databases to Identify Signalling Pathway-Associated MicroRNAs

The cyclin D1 (CCND1) and cyclin D3 (CCND3) are frequently co-overexpressed in pancreatic ductal adenocarcinoma (PDAC). Here we examine their differential roles in PDAC. CCND1 and CCND3 expression were selectively suppressed by shRNA in PDAC cell lines with expression levels of equal CCND1 and CCND3 (BxPC3), enhanced CCND1 (HPAC) or enhanced CCND3 (PANC1). Suppression of cell proliferation was greater with CCND3 than CCND1 downregulation. CCND3 suppression led to a reduced level of phosphorylated retinoblastoma protein (Ser795p-Rb/p110) and resulted in decreased levels of cyclin A mRNA and protein. A global gene expression analysis identified deregulated genes in D1- or D3-cyclin siRNA-treated PANC1 cells. The downregulated gene targets in CCND3 suppressed cells were significantly enriched in cell cycle associated processes (p < 0.005). In contrast, focal adhesion/actin cytoskeleton, MAPK and NF B signaling appeared to characterize the target genes and their interacting proteins in CCND1 suppressed PANC1 cells. Our results suggest that CCND3 is the primary driver of the cell cycle, in cooperation with CCND1 that integrates extracellular mitogenic signaling. We also present evidence that CCND1 plays a role in tumor cell migration. The results provide novel insights for common and differential targets of CCND1 and CCND3 overexpression during pancreatic duct cell carcinogenesis.

/pdf/differential-roles-of-cyclin-d1-and-d3-in-pancreatic-ductal-2mpuhot0gv.pdf

Differential roles of cyclin D1 and D3 in pancreatic ductal adenocarcinoma

Drug modes of action are complex and still poorly understood. The set of known drug targets is widely acknowledged to be biased and incomplete, and so gives only limited insight into the system-wide effects of drugs. But a high-throughput assay unique to yeast-barcode-based chemogenomic screens-can measure the individual drug response of every yeast deletion mutant in parallel. NetwoRx (http://ophid.utoronto.ca/networx) is the first resource to store data from these extremely valuable yeast chemogenomics experiments. In total, NetwoRx stores data on 5924 genes and 466 drugs. In addition, we applied data-mining approaches to identify yeast pathways, functions and phenotypes that are targeted by particular drugs, compute measures of drug-drug similarity and construct drug-phenotype networks. These data are all available to search or download through NetwoRx; users can search by drug name, gene name or gene set identifier. We also set up automated analysis routines in NetwoRx; users can query new gene sets against the entire collection of drug profiles and retrieve the drugs that target them. We demonstrate with use case examples how NetwoRx can be applied to target specific phenotypes, repurpose drugs using mode of action analysis, investigate bipartite networks and predict new drugs that affect yeast aging.

/pdf/networx-connecting-drugs-to-networks-and-phenotypes-in-48bx54hj53.pdf

NetwoRx: connecting drugs to networks and phenotypes in Saccharomyces cerevisiae

Summary: NAViGaTOR is a powerful graphing application for the 2D and 3D visualization of biological networks. NAViGaTOR includes a rich suite of visual mark-up tools for manual and automated annotation, fast and scalable layout algorithms and OpenGL hardware acceleration to facilitate the visualization of large graphs. Publication-quality images can be rendered through SVG graphics export. NAViGaTOR supports community-developed data formats (PSI-XML, BioPax and GML), is platform-independent and is extensible through a plug-in architecture. Availability: NAViGaTOR is freely available to the research community from http://ophid.utoronto.ca/navigator/. Installers and documentation are provided for 32- and 64-bit Windows, Mac, Linux and Unix.

/pdf/navigator-network-analysis-visualization-and-graphing-125gfge4u6.pdf

NAViGaTOR: Network Analysis, Visualization and Graphing Toronto.

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NAViGaTing the Micronome – Using Multiple MicroRNA Prediction Databases to Identify Signalling Pathway-Associated MicroRNAs

Differential roles of cyclin D1 and D3 in pancreatic ductal adenocarcinoma

NetwoRx: connecting drugs to networks and phenotypes in Saccharomyces cerevisiae

NAViGaTOR: Network Analysis, Visualization and Graphing Toronto.