Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters The analysis module and visualization module were combined into a reusable workflow Currently, clusterProfiler supports three species, including humans, mice, and yeast Methods provided in this package can be easily extended to other species and ontologies The clusterProfiler package is released under Artistic-20 License within Bioconductor project The source code and vignette are freely available at http://bioconductororg/packages/release/bioc/html/clusterProfilerhtml

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clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters

A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein-protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein-protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.

/pdf/the-string-database-in-2017-quality-controlled-protein-1u6rx6raii.pdf

The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible.

Tumours grow within an intricate network of epithelial cells, vascular and lymphatic vessels, cytokines and chemokines, and infiltrating immune cells. Different types of infiltrating immune cells have different effects on tumour progression, which can vary according to cancer type. In this Opinion article we discuss how the context-specific nature of infiltrating immune cells can affect the prognosis of patients.

The immune contexture in human tumours: impact on clinical outcome

Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer.

Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade

We have developed ClueGO, an easy to use Cytoscape plug-in that strongly improves biological interpretation of large lists of genes. ClueGO integrates Gene Ontology (GO) terms as well as KEGG/BioCarta pathways and creates a functionally organized GO/pathway term network. It can analyze one or compare two lists of genes and comprehensively visualizes functionally grouped terms. A one-click update option allows ClueGO to automatically download the most recent GO/KEGG release at any time. ClueGO provides an intuitive representation of the analysis results and can be optionally used in conjunction with the GOlorize plug-in.

/pdf/cluego-a-cytoscape-plug-in-to-decipher-functionally-grouped-5f5ajymwp8.pdf

ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks

We have tested whether a direct correlation of sequence information and staining properties of chromosomes is possible and whether this combined information can be used to precisely map any position on the chromosome. Despite huge differences of compaction between the naked DNA and the DNA packed in chromosomes we found a striking correlation when visualizing the GGCC density on both levels. Software was developed that allows one to superimpose chromosomal fluorescence intensity profiles generated by chromolysin A3 (CMA3) staining with GGCC density extracted from the Ensembl database. Thus, any position along the chromosome can be defined in megabase pairs (Mb) besides the cytoband information, enabling direct alignment of chromosomal information with the sequence data. The mapping tool was validated using 13 different BAC clones, resulting in a mean difference from Ensembl data of 2 Mb (ranging from 0.79 to 3.57 Mb). Our results indicate that the sequence density information and information gained with sequence-specific fluorochromes are superimposable. Thus, the visualized GGCC motif density along the chromosome (sequence bands) provides a unique platform for comparing different types of genomic information.

Gabriela-Luana Bindea

Papers

ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks

A new platform linking chromosomal and sequence information