Special Issue "Network Visualization and Visual Network Analysis: Cytoscape Apps & Co"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (15 October 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Frank Kramer

IT Infrastructure for Translational Medicine, Department of Computer Science, University of Augsburg, 86159 Augsburg, Germany
Website | E-Mail
Interests: network visualization;network modeling;pathway knowledge;pathway analysis

Special Issue Information

Dear Colleagues,

Using networks to visualize knowledge and results helps readers to understand complex molecular interactions and relationships more easily. A single pathway sketch can contain dozens of interconnected molecules or chemicals and can still be understood by a human. Recently-established high-throughput technologies have led to a surge in newly-generated knowledge on molecular interactions in biology and medicine. The computational representation of biological networks facilitates new opportunities of data and knowledge exchange between researchers, and asserts a common vocabulary and understanding of underlying principles. Standards, methods and tools to visualize networks are continuously evolving in order to keep up with biomedical research and technological advances. In this Special Issue, we would like to invite submissions of original research and short communications on software tools, as well as review articles on topics related to “Network Visualization and Visual Network Analysis”. We look forward to receiving your contributions.

Dr. Frank Kramer
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • network visualization
  • network analysis
  • pathway knowledge
  • cytoscape
  • bioinformatics

Published Papers (4 papers)

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Research

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Open AccessArticle Two-State Co-Expression Network Analysis to Identify Genes Related to Salt Tolerance in Thai Rice
Genes 2018, 9(12), 594; https://doi.org/10.3390/genes9120594
Received: 4 October 2018 / Revised: 8 November 2018 / Accepted: 19 November 2018 / Published: 29 November 2018
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Abstract
Khao Dawk Mali 105 (KDML105) rice is one of the most important crops of Thailand. It is a challenging task to identify the genes responding to salinity in KDML105 rice. The analysis of the gene co-expression network has been widely performed to prioritize
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Khao Dawk Mali 105 (KDML105) rice is one of the most important crops of Thailand. It is a challenging task to identify the genes responding to salinity in KDML105 rice. The analysis of the gene co-expression network has been widely performed to prioritize significant genes, in order to select the key genes in a specific condition. In this work, we analyzed the two-state co-expression networks of KDML105 rice under salt-stress and normal grown conditions. The clustering coefficient was applied to both networks and exhibited significantly different structures between the salt-stress state network and the original (normal-grown) network. With higher clustering coefficients, the genes that responded to the salt stress formed a dense cluster. To prioritize and select the genes responding to the salinity, we investigated genes with small partners under normal conditions that were highly expressed and were co-working with many more partners under salt-stress conditions. The results showed that the genes responding to the abiotic stimulus and relating to the generation of the precursor metabolites and energy were the great candidates, as salt tolerant marker genes. In conclusion, in the case of the complexity of the environmental conditions, gaining more information in order to deal with the co-expression network provides better candidates for further analysis. Full article
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Open AccessFeature PaperArticle Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression
Genes 2018, 9(11), 525; https://doi.org/10.3390/genes9110525
Received: 28 September 2018 / Revised: 22 October 2018 / Accepted: 22 October 2018 / Published: 29 October 2018
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Abstract
Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and
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Obesity is a global epidemic identified as a major risk factor for multiple chronic diseases and, consequently, diet-induced weight loss is used to counter obesity. The adipose tissue is the primary tissue affected in diet-induced weight loss, yet the underlying molecular mechanisms and changes are not completely deciphered. In this study, we present a network biology analysis workflow which enables the profiling of the cellular processes affected by weight loss in the subcutaneous adipose tissue. Time series gene expression data from a dietary intervention dataset with two diets was analysed. Differentially expressed genes were used to generate co-expression networks using a method that capitalises on the repeat measurements in the data and finds correlations between gene expression changes over time. Using the network analysis tool Cytoscape, an overlap network of conserved components in the co-expression networks was constructed, clustered on topology to find densely correlated genes, and analysed using Gene Ontology enrichment analysis. We found five clusters involved in key metabolic processes, but also adipose tissue development and tissue remodelling processes were enriched. In conclusion, we present a flexible network biology workflow for finding important processes and relevant genes associated with weight loss, using a time series co-expression network approach that is robust towards the high inter-individual variation in humans. Full article
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Open AccessArticle mully: An R Package to Create, Modify and Visualize Multilayered Graphs
Genes 2018, 9(11), 519; https://doi.org/10.3390/genes9110519
Received: 1 October 2018 / Revised: 18 October 2018 / Accepted: 18 October 2018 / Published: 23 October 2018
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Abstract
The modelling of complex biological networks such as pathways has been a necessity for scientists over the last decades. The study of these networks also imposes a need to investigate different aspects of nodes or edges within the networks, or other biomedical knowledge
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The modelling of complex biological networks such as pathways has been a necessity for scientists over the last decades. The study of these networks also imposes a need to investigate different aspects of nodes or edges within the networks, or other biomedical knowledge related to it. Our aim is to provide a generic modelling framework to integrate multiple pathway types and further knowledge sources influencing these networks. This framework is defined by a multi-layered model allowing automatic network transformations and documentation. By providing a tool that generates this model, we aim to facilitate the data integration, boost the reproducibility and increase the interoperability between different sources and databases in the field of pathways. We present mully R package that allows the user to create, modify and visualize graphs with multi-layers. The package is implemented with features to specifically handle multilayered graphs. Full article
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Review

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Open AccessReview Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine
Received: 3 August 2018 / Revised: 25 August 2018 / Accepted: 30 August 2018 / Published: 31 August 2018
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Abstract
Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss
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Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes. Full article
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