Visualization Of Interaction Networks Research Articles

Exploring the comorbidity mechanisms between psoriasis and obesity based on bioinformatics.

Psoriasis is a chronic, recurrent, immune-mediated inflammatory skin disease characterized by erythematous scaly lesions. Obesity is currently a major global health concern, increasing the risk of diseases such as cardiovascular diseases and diabetes. Since the correlation between psoriasis and obesity, as well as hypertension, diabetes, and cardiovascular diseases, has been clinically evidenced, it is of certain clinical significance to explore the mechanisms underlying the comorbidity of psoriasis with these conditions. Gene targets for both diseases were obtained from the Gene Expression Omnibus (GEO) comprehensive gene expression database. Differential gene analysis, intersection gene analysis, construction and visualization of protein-protein interaction networks (PPI) using R software, Cytoscape 3.8.2 software, online tools such as String, and enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed, with relevant graphics generated. Analysis identified 29 intersecting genes between the two diseases, with 10 key targets such as S100A7 and SERPINB4. Enrichment analysis indicated their involvement in regulating biological processes such as leukocyte chemotaxis, migration, and chronic inflammatory responses through cellular structures such as intracellular vesicles and extracellular matrix. Molecular functions, including RAGE receptor binding, Toll-like receptor binding, and fatty acid binding, were found to simultaneously regulate psoriasis and obesity. Psoriasis and obesity may mutually influence each other through multiple targets and pathways, emphasizing the importance of considering comorbidity treatment and daily care in clinical practice.

CircNetVis: an interactive web application for visualizing interaction networks of circular RNAs

Analyzing the interactions of circular RNAs (circRNAs) is a crucial step in understanding their functional impacts. While there are numerous visualization tools available for investigating circRNA interaction networks, these tools are typically limited to known circRNAs from specific databases. Moreover, these existing tools usually require complex installation procedures which can be time-consuming and challenging for users. There is a lack of a user-friendly web application that facilitates interactive exploration and visualization of circRNA interaction networks. CircNetVis is an interactive online web application to enhance the analysis of human/mouse circRNA interactions. The tool allows three different input formats of circRNAs including circRNA IDs from CircBase, circRNA coordinates (chromosome, start position, end position), and circRNA sequences in the FASTA format. It integrates multiple interaction networks for visualization and investigation of the interplay between circRNA, microRNAs, mRNAs and RNA binding proteins. CircNetVis also enables users to interactively explore the interactions of unknown circRNAs which are not reported from previous databases. The tool can generate interactive plots and allows users to save results as output files for offline usage. CircNetVis is implemented as a web application using R-shiny and freely available for academic use at https://www.meb.ki.se/shiny/truvu/CircNetVis/.

SIGNET: transcriptome-wide causal inference for gene regulatory networks

Gene regulation plays an important role in understanding the mechanisms of human biology and diseases. However, inferring causal relationships between all genes is challenging due to the large number of genes in the transcriptome. Here, we present SIGNET (Statistical Inference on Gene Regulatory Networks), a flexible software package that reveals networks of causal regulation between genes built upon large-scale transcriptomic and genotypic data at the population level. Like Mendelian randomization, SIGNET uses genotypic variants as natural instrumental variables to establish such causal relationships but constructs a transcriptome-wide gene regulatory network with high confidence. SIGNET makes such a computationally heavy task feasible by deploying a well-designed statistical algorithm over a parallel computing environment. It also provides a user-friendly interface allowing for parameter tuning, efficient parallel computing scheduling, interactive network visualization, and confirmatory results retrieval. The Open source SIGNET software is freely available (https://www.zstats.org/signet/).

Exploration of key ferroptosis-related genes and immune infiltration in Crohn’s disease using bioinformatics

Crohn's disease (CD) is a type of inflammatory bowel disease (IBD) that manifests mainly as chronic inflammation in different parts of the gastrointestinal tract, and its incidence has come to be increasing in recent years. Ferroptosis, a novel type of programmed cell death, it seems the role of ferroptosis-related biomarkers in CD has not been mentioned. Thus, the role of ferroptosis in CD and its relationship with immune infiltration were explored in this study. The CD dataset was downloaded from the Gene Expression Omnibus database. The validated ferroptosis genes (FRGs) were retrieved from the public FerrDb database. The gene expression matrix of the CD dataset was analyzed with the “limma” package in R language to obtain differentially expressed genes (DEGs) between diseased and healthy samples. Then, intersecting genes between DEGs and FRGs were identified as differentially expressed ferroptosis-associated genes (DE-FRGs). Protein–protein interaction (PPI) network analysis and visualization were carried out with STRING and Cytoscape, and key CD ferroptosis-related genes (CD-FRGs) were identified along with their Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using the clusterProfiler package. Immune cell infiltration was analyzed with CIBERSORT. The correlation between key CD-FRGs and immune-infiltrated cells in CD was studied by Spearman's correlation method. A total of 37 DE-FRGs and 6 key CD-FRGs (CAV1, CD44, HIF1A, IFNG, TIMP1 and TLR4) were identified. GO and KEGG functional analysis indicated these genes enrichment in programmed cell death and apoptotic process, HIF-1 signaling pathway and IBD. Infiltration matrix analysis of immune cells showed abundant T cells CD4 memory activated, M1 macrophages, M2 macrophages, Mast cells activated and Neutrophils in CD intestinal tissues. The 6 key CD-FRGs were correlated with immune-infiltrated cells in CD based on correlation analysis. Taken together, immune cells with abnormal infiltration can be implicated in CD due to ferroptosis. This study identified 6 key CD-FRGs that may be key biomarkers of ferroptosis in CD; they include CAV1, CD44, HIF1A, IFNG, TIMP1 and TLR4. These findings suggest that the immune response is critical in CD caused by ferroptosis through the interaction between key CD-FRGs and immune infiltrating cells.

McAN: a novel computational algorithm and platform for constructing and visualizing haplotype networks.

Haplotype networks are graphs used to represent evolutionary relationships between a set of taxa and are characterized by intuitiveness in analyzing genealogical relationships of closely related genomes. We here propose a novel algorithm termed McAN that considers mutation spectrum history (mutations in ancestry haplotype should be contained in descendant haplotype), node size (corresponding to sample count for a given node) and sampling time when constructing haplotype network. We show that McAN is two orders of magnitude faster than state-of-the-art algorithms without losing accuracy, making it suitable for analysis of a large number of sequences. Based on our algorithm, we developed an online web server and offline tool for haplotype network construction, community lineage determination, and interactive network visualization. We demonstrate that McAN is highly suitable for analyzing and visualizing massive genomic data and is helpful to enhance the understanding of genome evolution. Availability: Source code is written in C/C++ and available at https://github.com/Theory-Lun/McAN and https://ngdc.cncb.ac.cn/biocode/tools/BT007301 under the MIT license. Web server is available at https://ngdc.cncb.ac.cn/bit/hapnet/. SARS-CoV-2 dataset are available at https://ngdc.cncb.ac.cn/ncov/. Contact: songshh@big.ac.cn (Song S), zhaowm@big.ac.cn (Zhao W), baoym@big.ac.cn (Bao Y), zhangzhang@big.ac.cn (Zhang Z), ybxue@big.ac.cn (Xue Y).

Identification of hub genes and immune infiltration in ulcerative colitis using bioinformatics

Ulcerative colitis (UC) is a chronic inflammatory disease of the intestine, whose pathogenesis is not fully understood. Given that immune infiltration plays a key role in UC progression, our study aimed to assess the level of immune cells in UC intestinal mucosal tissues and identify potential immune-related genes. The GSE65114 UC dataset was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between healthy and UC tissues were identified using the “limma” package in R, while their Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined with the clusterProfiler package. Protein–protein interaction network analysis and visualization were performed with STRING and Cytoscape. Immune cell infiltration was calculated with CIBERSORT. The relationship between hub genes and immune-infiltrated cells in UC was determined by Pearson correlation. A total of 206 DEGs were identified, of which 174 were upregulated and 32 downregulated. GO and KEGG functional classification indicated DEG enrichment in immune response pathways, including Toll-like receptor signaling, IL-17 signaling, and immune system process and chemokine signaling. 13 hub genes were identified. Infiltration matrix analysis of immune cells showed abundant plasma cells, memory B cells, resting CD4 memory T cells, γδ T cells, M0 and M1 macrophages, and neutrophils in UC intestinal tissues. Correlation analysis revealed 13 hub genes associated with immune-infiltrated cells in UC. 13 hub genes associated with immune-infiltrated cells in UC were identified; they included CXCL13, CXCL10, CXCL9, CXCL8, CCL19, CTLA4, CCR1, CD69, CD163, IL7R, PECAM1, TLR8 and TLR2. These genes could potentially serve as markers for the diagnosis and treatment of UC.

Exploration and comparison of molecular mechanisms across diseases using MINERVA Net.

Protein function is often interpreted using molecular interaction diagrams, encoding roles a given protein plays in various molecular mechanisms. Information about disease-related mechanisms can be inferred from disease maps, knowledge repositories containing manually constructed systems biology diagrams. Disease maps hosted on the Molecular Interaction Network VisuAlization (MINERVA) Platform are individually accessible through a REST API interface of each instance, making it challenging to systematically explore their contents. To address this challenge, we introduce the MINERVA Net web service, a repository of open-access disease maps allowing users to publicly share minimal information about their maps. The MINERVA Net repository provides REST API endpoints of particular disease maps, which then can be individually queried for content. In this article, we describe the concept of MINERVA Net and illustrate its use by comparing proteins and their interactions in three different disease maps.

Building, Visualizing, and Analyzing Glycosaminoglycan-Protein Interaction Networks.

This chapter describes how to generate, visualize, and analyze interaction networks of glycosaminoglycans (GAGs), which are linear polyanionic polysaccharides mostly located at the cell surface and in the extracellular matrix. The protocol is divided into three major steps: (1) the collection of GAG-mediated interaction data, (2) the visualization of GAG interaction networks, and (3) the computational enrichment analyses of these networks to identify their overrepresented features (e.g., protein domains, location, molecular functions, and biological pathways) compared to a reference proteome. These analyses are critical to interpret GAG interactomic datasets, decipher their specificities and functions, and ultimately identify GAG-protein interactions to target for therapeutic purpose.

A systematic map of knowledge exchange across the science‐policy interface for forest science: How can we improve consistency and effectiveness?

Abstract Knowledge produced by scientists is essential to the policy and practice of managing natural resources, including forests. However, there has never been systematic mapping of which techniques in knowledge exchange (KE) have been applied in the forest sciences, by whom, and to what effect. We examined KE techniques documented in the forest sciences globally. We used standardized search strings in English and French across two academic search engines (BASE and Scopus) and a specialist website (ResearchGate) to locate relevant items. We screened items, extracted data, conducted qualitative and quantitative analysis, and built a network visualization diagram to demonstrate knowledge flow. Our final map included 122 items published from 1998 to 2020, with most published after 2010. Items mentioned organizations from 66 countries as knowledge producers or users. The interactive network visualization diagram displays linkages between organizations, sectors and countries. We found that most of the KE activity involved the Global North (89%). Governments were the most common knowledge users, and industry was frequently reported as a user but rarely a producer. Academia was both producer and user. Indigenous, local, traditional or community knowledge was included in 24% of items, but these communities were not associated with any coauthor affiliations. Reported funders were universities, governments, non‐profits or foundations. We found 90 unique terms in the items related to KE with less than 25% of terms used in more than one item. Fifteen per cent of item keywords related to KE. The most commonly identified enabling conditions for KE were trust, funding and established relationships, while major barriers were challenges for translation of science and lack of time. To improve searchability of information related to KE and encourage a culture of considering KE in scientific research and forest management work, we recommend a common lexicon of ‘knowledge exchange’/‘échange de connaisances’. We recommend that more effort be given to forest science‐related KE connections between the Global North and South as well as a deliberate collection of evidence for the effectiveness of KE techniques. Researchers and practitioners can use our KE typology to identify their goals and design appropriate evaluation measures.

PathIN: an integrated tool for the visualization of pathway interaction networks

PathIN is a web-service that provides an easy and flexible way for rapidly creating pathway-based networks at several functional biological levels: genes, compounds and reactions. The tool is supported by a database repository of reference pathway networks across a large set of species, developed through the freely available information included in the KEGG, Reactome and Wiki Pathways database repositories. PathIN provides networks by means of five diverse methodologies: (a) direct connections between pathways of interest, (b) direct connections as well as the first neighbours of the given pathways, (c) direct connections, the first neighbours and the connections in between them, and (d) two additional methodologies for creating complementary pathway-to-pathway networks that involve additional (missing) pathways that interfere in-between pathways of interest. PathIN is expected to be used as a simple yet informative reference tool for understanding networks of molecular mechanisms related to specific diseases.

Identification of hub ferroptosis-related genes and immune infiltration in lupus nephritis using bioinformatics

Lupus nephritis (LN) is one of the most severe and more common organ manifestations of the autoimmune disease, systemic lupus erythematosus. Ferroptosis, a novel type of programmed cell death, so far its role in LN remains uncertain. In the present study, we explored the role of ferroptosis in LN and its relationship with the immune response. The GSE112943 LN dataset was downloaded from the Gene Expression Omnibus database. Ferroptosis-Related Genes (FRGs) that drive, suppress or mark ferroptosis were retrieved from the public FerrDb database. The gene expression matrix of the GSE112943 dataset was analyzed with the “limma” package in R to obtain differentially expressed genes (DEGs) between LN and healthy samples. Subsequently, the crossover genes between DEGs and FRGs were identified as differentially expressed ferroptosis-related genes (DE-FRGs). Protein–protein interaction (PPI) network analysis, visualization, and identification of hub lupus nephritis ferroptosis-related genes (LN-FRGs) were performed with STRING and Cytoscape, while their Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined with the clusterProfiler package. Immune cell infiltration was calculated with CIBERSORT. The relationship between hub LN-FRGs and immune-infiltrated cells in LN was determined by Pearson correlation. A total of 96 DE-FRGs and 8 hub LN-FRGs (KRAS, PIK3CA, EGFR, MAPK14, SRC, MAPK3, VEGFA, and ATM) were identified. GO and KEGG functional classification indicated these genes enrichment in apoptotic process, programmed cell death, autophagy-animal, FoxO signaling pathway, relaxin signaling pathway, and VEGF signaling pathway. Infiltration matrix analysis of immune cells showed abundant Monocytes and M0/M1/M2 macrophages in LN kidney tissues. Correlation analysis revealed 8 hub LN-FRGs associated with immune-infiltrated cells in LN. In summary, overproduction of ROS and abnormal infiltration of immune cells would be implicated in the LN caused by ferroptosis. 8 hub lupus nephritis ferroptosis-related genes (LN-FRGs) which might be good biomarkers of ferroptosis in LN were identified in this study. These findings point to the immune response playing an important role in LN caused by ferroptosis via mutual regulation between hub LN-FRGs and immune-infiltrated cells.

TFLink: an integrated gateway to access transcription factor-target gene interactions for multiple species.

Analysis of transcriptional regulatory interactions and their comparisons across multiple species are crucial for progress in various fields in biology, from functional genomics to the evolution of signal transduction pathways. However, despite the rapidly growing body of data on regulatory interactions in several eukaryotes, no databases exist to provide curated high-quality information on transcription factor–target gene interactions for multiple species. Here, we address this gap by introducing the TFLink gateway, which uniquely provides experimentally explored and highly accurate information on transcription factor–target gene interactions (∼12 million), nucleotide sequences and genomic locations of transcription factor binding sites (∼9 million) for human and six model organisms: mouse, rat, zebrafish, fruit fly, worm and yeast by integrating 10 resources. TFLink provides user-friendly access to data on transcription factor–target gene interactions, interactive network visualizations and transcription factor binding sites, with cross-links to several other databases. Besides containing accurate information on transcription factors, with a clear labelling of the type/volume of the experiments (small-scale or high-throughput), the source database and the original publications, TFLink also provides a wealth of standardized regulatory data available for download in multiple formats. The database offers easy access to high-quality data for wet-lab researchers, supplies data for gene set enrichment analyses and facilitates systems biology and comparative gene regulation studies. Database URL https://tflink.net/

Integrated bioinformatics analysis and screening of hub genes in polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is one of the most common endocrine and metabolic disorders, posing a serious threat to the health of women. Herein, we aimed to explore new biomarkers and potential therapeutic targets for PCOS by employing integrated bioinformatics tools. Three gene expression profile datasets (GSE138518, GSE155489, GSE106724) were obtained from the Gene Expression Omnibus database and the differentially expressed genes in PCOS and normal groups with an adjusted p-value < 0.05 and a |log fold change (FC) | > 1.2 were first identified using the DESeq package. The weighted correlation network analysis (WGCNA) R package was used to identify clusters of highly correlated genes or modules associated with PCOS. Protein-protein interaction (PPI) network analysis and visualization of genes in the key module were performed using the STRINGdb database and the NetworkX package (edge > 5), respectively. The genes overlapping among the key module genes and PCOS-associated genes were further analyzed. Ligand molecules with strong binding energy < -10 kJ/mol to GNB3 were screened in the drug library using MTiOpenScreen. AutoDock, ChimeraX, and BIOVIA Discovery Studio Visualizer were further used to elucidate the mechanism of ligand interaction with GNB3. Finally, the relationship between GNB3 and PCOS was verified using experimental models in vivo and in vitro. Of the 11 modules identified by WGCNA, the black module had the highest correlation with PCOS (correlation = 0.96, P = 0.00016). The PPI network of 351 related genes revealed that VCL, GNB3, MYH11, LMNA, MLLT4, EZH2, PAK3, and CHRM1 have important roles in PCOS. The hub gene GNB3 was identified by taking the intersection of PCOS-related gene sets. MTiOpenScreen revealed that five compounds interacted with GNB3. Of these five, compound 1 had the strongest binding ability and can bind amino acids in the WD40 motif of GNB3, which in turn affects the function of the G protein-coupled receptor β subunit. GNB3 was also significantly downregulated in PCOS models. We identified the hub gene GNB3 as the most important regulatory gene in PCOS. We suggest that compound 1 can target the WD40 motif of GNB3 to affect related functions and must be considered as a lead compound for drug development. This study will provide new insights into the development of PCOS-related drugs.

Identifying hub genes and miRNAs in Crohn's disease by bioinformatics analysis.

Introduction: Crohn’s disease (CD) is a disease that manifests mainly as chronic inflammation of the gastrointestinal tract, which is still not well understood in terms of its pathogenesis. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) and miRNAs with diagnostic and therapeutic potential in CD. Materials and methods: Three CD datasets (GSE179285, GSE102133, GSE75214) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and CD tissues were identified using the GEO2R online tool. The Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were conducted using the clusterProfiler function in the R package. Protein-protein interaction network (PPI) analysis and visualization were performed with STRING and Cytoscape. Ten hub genes were identified using cytoHubba’s MCC algorithm and validated with datasets GSE6731 and GSE52746. Finally, the miRNA gene regulatory network was constructed by Cytoscape and NetworkAnalyst to predict potential microRNAs (miRNAs) associated with DEGs. Results: A total of 97 DEGs were identified, consisting of 88 downregulated genes and 9 upregulated genes. The enriched functions and pathways of the DEGs include immune system process, response to stress, response to cytokine and extracellular region. KEGG pathway analysis indicates that the genes were significantly enriched in Cytokine-cytokine receptor interaction, IL-17 signaling pathway, Rheumatoid arthritis and TNF signaling pathway. In combination with the results of the protein-protein interaction (PPI) network and CytoHubba, 10 hub genes including IL1B, CXCL8, CXCL10, CXCL1, CXCL2, CXCL5, ICAM1, IL1RN, TIMP1 and MMP3 were selected. Based on the DEG-miRNAs network construction, 5 miRNAs including hsa-mir-21-5p, hsa-mir-93-5p, hsa-mir-98-5p, hsa-mir-1-3p and hsa-mir-335-5p were identified as potential critical miRNAs. Conclusion: In conclusion, a total of 97 DEGs, 10 hub genes and 5 miRNAs that may be involved in the progression or occurrence of CD were identified in this study, which could be regarded as biomarkers of CD.

Interactive network visualization on the integration of mindsets and sustainability – creating conditions for emergence through a relational narrative

Transdisciplinary research processes often involve the integration of knowledge and stakeholders from various backgrounds. Here, we introduce the story of the research project AMA (A Mindset for the Anthropocene) on the role of mindsets in the context of sustainability and present an interactive visualization tool that we developed for stakeholder mapping and research communication. Through this platform, we provide access and navigation to everyone interested in this field of research and we have simultaneously created a channel for all stakeholders to co-create content. Here, we describe the design and functionalities of the platform and the participatory way it was developed as part of our stakeholder engagement. We discuss upon how such a design allows for reflection of potential biases in transdisciplinary research processes and simultaneously catalyzing self-organization in stakeholder networks.

Identification of Differentially Expressed Genes and miRNAs for Ulcerative Colitis Using Bioinformatics Analysis

Introduction: Ulcerative colitis (UC) is a chronic inflammatory disease of the intestine whose cause and underlying mechanisms are not fully understood. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in UC. Materials and methods: Three UC datasets (GSE179285, GSE75214, GSE48958) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and UC tissues were identified using the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed using Metascape. Protein-protein interaction network (PPI) analysis and visualization using STRING and Cytoscape. Finally, the miRNA gene regulatory network was constructed by Cytoscape to predict potential microRNAs (miRNAs) associated with DEGs. Results: A total of 446 DEGs were identified, consisting of 309 upregulated genes and 137 downregulated genes. The enriched functions and pathways of the DEGs include extracellular matrix, regulation of cell adhesion, inflammatory response, response to cytokine, monocarboxylic acid metabolic process, response to toxic substance. The analysis of KEGG pathway indicates that the DEGs were significantly enriched in Complement and coagulation cascades, Amoebiasis, TNF signaling pathway, bile secretion, and Mineral absorption. Combining the results of the PPI network and CytoHubba, 9 hub genes including CXCL8, ICAM1, CXCR4, CD44, IL1B, MMP9, SPP1, TIMP1, and HIF1A were selected. Based on the DEG-miRNAs network construction, 7 miRNAs including miR-335-5p, mir-204-5p, miR-93-5p, miR106a-5p, miR-21-5p, miR-146a-5p, and miR-155-5p were identified as potential critical miRNAs. Conclusion: In summary, we identified DEGs that may be involved in the progression or occurrence of UC. A total of 446 DEGs,9 hub genes and 7 miRNAs were identified, which may be considered as biomarkers of UC. Further studies, however, are needed to elucidate the biological functions of these genes in UC.

CompositeView: A Network-Based Visualization Tool.

Large networks are quintessential to bioinformatics, knowledge graphs, social network analysis, and graph-based learning. CompositeView is a Python-based open-source application that improves interactive complex network visualization and extraction of actionable insight. CompositeView utilizes specifically formatted input data to calculate composite scores and display them using the Cytoscape component of Dash. Composite scores are defined representations of smaller sets of conceptually similar data that, when combined, generate a single score to reduce information overload. Visualized interactive results are user-refined via filtering elements such as node value and edge weight sliders and graph manipulation options (e.g., node color and layout spread). The primary difference between CompositeView and other network visualization tools is its ability to auto-calculate and auto-update composite scores as the user interactively filters or aggregates data. CompositeView was developed to visualize network relevance rankings, but it performs well with non-network data. Three disparate CompositeView use cases are shown: relevance rankings from SemNet 2.0, an open-source knowledge graph relationship ranking software for biomedical literature-based discovery; Human Development Index (HDI) data; and the Framingham cardiovascular study. CompositeView was stress tested to construct reference benchmarks that define breadth and size of data effectively visualized. Finally, CompositeView is compared to Excel, Tableau, Cytoscape, neo4j, NodeXL, and Gephi.

PathwAX II: network-based pathway analysis with interactive visualization of network crosstalk.

MotivationPathway annotation tools are indispensable for the interpretation of a wide range of experiments in life sciences. Network-based algorithms have recently been developed which are more sensitive than traditional overlap-based algorithms, but there is still a lack of good online tools for network-based pathway analysis.ResultsWe present PathwAX II—a pathway analysis web tool based on network crosstalk analysis using the BinoX algorithm. It offers several new features compared with the first version, including interactive graphical network visualization of the crosstalk between a query gene set and an enriched pathway, and the addition of Reactome pathways.Availability and implementationPathwAX II is available at http://pathwax.sbc.su.se.Supplementary information Supplementary data are available at Bioinformatics online.

SINAPs: A Software Tool for Analysis and Visualization of Interaction Networks of Molecular Dynamics Simulations.

As long as the structural study of molecular mechanisms requires multiple molecular dynamics reflecting contrasted bioactive states, the subsequent analysis of molecular interaction networks remains a bottleneck to be fairly treated and requires a user-friendly 3D view of key interactions. Structural Interaction Network Analysis Protocols (SINAPs) is a proprietary python tool developed to (i) quickly solve key interactions able to distinguish two protein states, either from two sets of molecular dynamics simulations or from two crystallographic structures, and (ii) render a user-friendly 3D view of these key interactions through a plugin of UCSF Chimera, one of the most popular open-source viewing software for biomolecular systems. Through two case studies, glucose transporter-1 (GLUT-1) and A2A adenosine receptor (A2AR), SINAPs easily pinpointed key interactions observed experimentally and relevant for their bioactivities. This very effective tool was thus applied to identify the amino acids involved in the molecular enzymatic mechanisms ruling the activation of an immunomodulator drug candidate, P28 glutathione-S-transferase (P28GST). SINAPs is freely available at https://github.com/ParImmune/SINAPs.

Netgsa: Fast computation and interactive visualization for topology-based pathway enrichment analysis.

Existing software tools for topology-based pathway enrichment analysis are either computationally inefficient, have undesirable statistical power, or require expert knowledge to leverage the methods’ capabilities. To address these limitations, we have overhauled NetGSA, an existing topology-based method, to provide a computationally-efficient user-friendly tool that offers interactive visualization. Pathway enrichment analysis for thousands of genes can be performed in minutes on a personal computer without sacrificing statistical power. The new software also removes the need for expert knowledge by directly curating gene-gene interaction information from multiple external databases. Lastly, by utilizing the capabilities of Cytoscape, the new software also offers interactive and intuitive network visualization.