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Network Visualization and Visual Network Analysis: Cytoscape Apps & Co
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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) | Viewed by 34195

Special Issue Editor

Prof. Dr. Frank Kramer

E-Mail Website
Guest Editor
IT Infrastructure for Translational Medicine, Department of Computer Science, University of Augsburg, 86159 Augsburg, Germany
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (6 papers)

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Research

Jump to: Review

16 pages, 1964 KiB  
Article
NetR and AttR, Two New Bioinformatic Tools to Integrate Diverse Datasets into Cytoscape Network and Attribute Files
by Armen Halajyan, Natalie Weingart, Mirza Yeahia and Mariano Loza-Coll
Genes 2019, 10(6), 423; https://doi.org/10.3390/genes10060423 - 1 Jun 2019
Cited by 2 | Viewed by 4258
Abstract
High-throughput technologies have allowed researchers to obtain genome-wide data from a wide array of experimental model systems. Unfortunately, however, new data generation tends to significantly outpace data re-utilization, and most high throughput datasets are only rarely used in subsequent studies or to generate [...] Read more.
High-throughput technologies have allowed researchers to obtain genome-wide data from a wide array of experimental model systems. Unfortunately, however, new data generation tends to significantly outpace data re-utilization, and most high throughput datasets are only rarely used in subsequent studies or to generate new hypotheses to be tested experimentally. The reasons behind such data underutilization include a widespread lack of programming expertise among experimentalist biologists to carry out the necessary file reformatting that is often necessary to integrate published data from disparate sources. We have developed two programs (NetR and AttR), which allow experimental biologists with little to no programming background to integrate publicly available datasets into files that can be later visualized with Cytoscape to display hypothetical networks that result from combining individual datasets, as well as a series of published attributes related to the genes or proteins in the network. NetR also allows users to import protein and genetic interaction data from InterMine, which can further enrich a network model based on curated information. We expect that NetR/AttR will allow experimental biologists to mine a largely unexploited wealth of data in their fields and facilitate their integration into hypothetical models to be tested experimentally. Full article
(This article belongs to the Special Issue Network Visualization and Visual Network Analysis: Cytoscape Apps & Co)
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17 pages, 5522 KiB  
Article
Identification of Novel Interaction Partners of Ets-1: Focus on DNA Repair
by Guillaume Brysbaert, Jérôme de Ruyck, Marc Aumercier and Marc F. Lensink
Genes 2019, 10(3), 206; https://doi.org/10.3390/genes10030206 - 8 Mar 2019
Cited by 3 | Viewed by 3389
Abstract
The transcription factor Ets-1 (ETS proto-oncogene 1) shows low expression levels except in specific biological processes like haematopoiesis or angiogenesis. Elevated levels of expression are observed in tumor progression, resulting in Ets-1 being named an oncoprotein. It has recently been shown that Ets-1 [...] Read more.
The transcription factor Ets-1 (ETS proto-oncogene 1) shows low expression levels except in specific biological processes like haematopoiesis or angiogenesis. Elevated levels of expression are observed in tumor progression, resulting in Ets-1 being named an oncoprotein. It has recently been shown that Ets-1 interacts with two DNA repair enzymes, PARP-1 (poly(ADP-ribose) polymerase 1) and DNA-PK (DNA-dependent protein kinase), through two different domains and that these interactions play a role in cancer. Considering that Ets-1 can bind to distinctly different domains of two DNA repair enzymes, we hypothesized that the interaction can be transposed onto homologs of the respective domains. We have searched for sequence and structure homologs of the interacting ETS(Ets-1), BRCT(PARP-1) and SAP(DNA-PK) domains, and have identified several candidate binding pairs that are currently not annotated as such. Many of the Ets-1 partners are associated to DNA repair mechanisms. We have applied protein-protein docking to establish putative interaction poses and investigated these using centrality analyses at the protein residue level. Most of the identified poses are virtually similar to our recently established interaction model for Ets-1/PARP-1 and Ets-1/DNA-PK. Our work illustrates the potentially high number of interactors of Ets-1, in particular involved in DNA repair mechanisms, which shows the oncoprotein as a potential important regulator of the mechanism. Full article
(This article belongs to the Special Issue Network Visualization and Visual Network Analysis: Cytoscape Apps & Co)
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21 pages, 2459 KiB  
Article
Two-State Co-Expression Network Analysis to Identify Genes Related to Salt Tolerance in Thai Rice
by Apichat Suratanee, Chidchanok Chokrathok, Panita Chutimanukul, Nopphawitchayaphong Khrueasan, Teerapong Buaboocha, Supachitra Chadchawan and Kitiporn Plaimas
Genes 2018, 9(12), 594; https://doi.org/10.3390/genes9120594 - 29 Nov 2018
Cited by 11 | Viewed by 5407
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 [...] Read more.
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
(This article belongs to the Special Issue Network Visualization and Visual Network Analysis: Cytoscape Apps & Co)
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16 pages, 685 KiB  
Article
Profiling Cellular Processes in Adipose Tissue during Weight Loss Using Time Series Gene Expression
by Samar H. K. Tareen, Michiel E. Adriaens, Ilja C. W. Arts, Theo M. De Kok, Roel G. Vink, Nadia J. T. Roumans, Marleen A. Van Baak, Edwin C. M. Mariman, Chris T. Evelo and Martina Kutmon
Genes 2018, 9(11), 525; https://doi.org/10.3390/genes9110525 - 29 Oct 2018
Cited by 4 | Viewed by 5988
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 [...] Read more.
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
(This article belongs to the Special Issue Network Visualization and Visual Network Analysis: Cytoscape Apps & Co)
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7 pages, 2095 KiB  
Article
mully: An R Package to Create, Modify and Visualize Multilayered Graphs
by Zaynab Hammoud and Frank Kramer
Genes 2018, 9(11), 519; https://doi.org/10.3390/genes9110519 - 23 Oct 2018
Cited by 10 | Viewed by 5590
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 [...] Read more.
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
(This article belongs to the Special Issue Network Visualization and Visual Network Analysis: Cytoscape Apps & Co)
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Review

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25 pages, 11120 KiB  
Review
Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine
by Giulia Fiscon, Federica Conte, Lorenzo Farina and Paola Paci
Genes 2018, 9(9), 437; https://doi.org/10.3390/genes9090437 - 31 Aug 2018
Cited by 56 | Viewed by 7987
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 [...] Read more.
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
(This article belongs to the Special Issue Network Visualization and Visual Network Analysis: Cytoscape Apps & Co)
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