CompNet: a GUI based tool for comparison of multiple biological interaction networks

BackgroundNetwork visualization and analysis tools aid in better understanding of complex biological systems. Furthermore, to understand the differences in behaviour of system(s) under various environmental conditions (e.g. stress, infection), comparing multiple networks becomes necessary. Such comparisons between multiple networks may help in asserting causation and in identifying key components of the studied biological system(s). Although many available network comparison methods exist, which employ techniques like network alignment and querying to compute pair-wise similarity between selected networks, most of them have limited features with respect to interactive visual comparison of multiple networks.ResultsIn this paper, we present CompNet - a graphical user interface based network comparison tool, which allows visual comparison of multiple networks based on various network metrics. CompNet allows interactive visualization of the union, intersection and/or complement regions of a selected set of networks. Different visualization features (e.g. pie-nodes, edge-pie matrix, etc.) aid in easy identification of the key nodes/interactions and their significance across the compared networks. The tool also allows one to perform network comparisons on the basis of neighbourhood architecture of constituent nodes and community compositions, a feature particularly useful while analyzing biological networks. To demonstrate the utility of CompNet, we have compared a (time-series) human gene-expression dataset, post-infection by two strains of Mycobacterium tuberculosis, overlaid on the human protein-protein interaction network. Using various functionalities of CompNet not only allowed us to comprehend changes in interaction patterns over the course of infection, but also helped in inferring the probable fates of the host cells upon infection by the two strains.ConclusionsCompNet is expected to be a valuable visual data mining tool and is freely available for academic use from http://metagenomics.atc.tcs.com/compnet/ or http://121.241.184.233/compnet/

[1]  Hongtao Yu,et al.  The Bub1–Plk1 kinase complex promotes spindle checkpoint signalling through Cdc20 phosphorylation , 2016, Nature Communications.

[2]  G. Kaplan,et al.  Mycobacterium tuberculosis H37Ra and H37Rv differential growth and cytokine/chemokine induction in murine macrophages in vitro. , 2006, Journal of interferon & cytokine research : the official journal of the International Society for Interferon and Cytokine Research.

[3]  Jignesh M. Patel,et al.  SAGA: a subgraph matching tool for biological graphs , 2007, Bioinform..

[4]  Amit Singh,et al.  Express Path Analysis Identifies a Tyrosine Kinase Src-centric Network Regulating Divergent Host Responses to Mycobacterium tuberculosis Infection* , 2011, The Journal of Biological Chemistry.

[5]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[6]  Meng Xu,et al.  NetAlign: a web-based tool for comparison of protein interaction networks , 2006, Bioinform..

[7]  Kapaettu Satyamoorthy,et al.  Population Specific Impact of Genetic Variants in KCNJ11 Gene to Type 2 Diabetes: A Case-Control and Meta-Analysis Study , 2014, PloS one.

[8]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[9]  J. Yuan,et al.  K+ Channels in Apoptosis , 2006, The Journal of Membrane Biology.

[10]  L. Bermudez,et al.  Mycobacterium tuberculosis infection causes different levels of apoptosis and necrosis in human macrophages and alveolar epithelial cells , 2003, Cellular microbiology.

[11]  Mario Baum Basic Statistical Analysis , 2016 .

[12]  Å. Lernmark,et al.  Autoimmunity against INS-IGF2 Protein Expressed in Human Pancreatic Islets*♦ , 2013, The Journal of Biological Chemistry.

[13]  Fidel Ramírez,et al.  Computing topological parameters of biological networks , 2008, Bioinform..

[14]  W. Jin,et al.  IL-17 cytokines in immunity and inflammation , 2013, Emerging Microbes & Infections.

[15]  Anirban Dutta,et al.  Understanding the sequential activation of Type III and Type VI Secretion Systems in Salmonella typhimurium using Boolean modeling , 2013, Gut Pathogens.

[16]  P Manimaran,et al.  Prediction of conditional gene essentiality through graph theoretical analysis of genome-wide functional linkages. , 2009, Molecular bioSystems.

[17]  A. Rivera,et al.  Cyclin A1 is a p53-induced gene that mediates apoptosis, G2/ M arrest, and mitotic catastrophe in renal, ovarian, and lung carcinoma cells , 2006, Cellular and Molecular Life Sciences CMLS.

[18]  Reinhard Schneider,et al.  Using graph theory to analyze biological networks , 2011, BioData Mining.

[19]  Reuven Agami,et al.  p53-Dependent Regulation of Cdc6 Protein Stability Controls Cellular Proliferation , 2005, Molecular and Cellular Biology.

[20]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[21]  Robert E. W. Hancock,et al.  NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration , 2014, Nucleic Acids Res..

[22]  Alain C. Mita,et al.  Survivin: Key Regulator of Mitosis and Apoptosis and Novel Target for Cancer Therapeutics , 2008, Clinical Cancer Research.

[23]  Guy Karlebach,et al.  Modelling and analysis of gene regulatory networks , 2008, Nature Reviews Molecular Cell Biology.

[24]  Ralf Hofestädt,et al.  PathAligner: metabolic pathway retrieval and alignment. , 2004, Applied bioinformatics.

[25]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[26]  Giovanni Micale,et al.  GASOLINE: a Cytoscape app for multiple local alignment of PPI networks , 2014, F1000Research.

[27]  I. Screpanti,et al.  Differential regulation of E2F1 apoptotic target genes in response to DNA damage , 2003, Nature Cell Biology.

[28]  M. Gerstein,et al.  TopNet: a tool for comparing biological sub-networks, correlating protein properties with topological statistics. , 2004, Nucleic acids research.

[29]  Shang-Hua Teng,et al.  Finding local communities in protein networks , 2009, BMC Bioinformatics.

[30]  Gang Wu,et al.  MIMO: an efficient tool for molecular interaction maps overlap , 2013, BMC Bioinformatics.

[31]  T. Fukushima,et al.  Antitumor activity and modified immunoregulation associated with IFN-gamma treatment of RG2 gliomas. , 1999, Anticancer research.

[32]  Leland Wilkinson,et al.  Exact and Approximate Area-Proportional Circular Venn and Euler Diagrams , 2012, IEEE Transactions on Visualization and Computer Graphics.

[33]  R. Gamelli,et al.  Foxa2 may modulate hepatic apoptosis through the cIAP1 pathway. , 2013, Cellular signalling.

[34]  M. Levandowsky,et al.  Distance between Sets , 1971, Nature.

[35]  M. Meuth,et al.  Chk1 and p21 cooperate to prevent apoptosis during DNA replication fork stress. , 2005, Molecular biology of the cell.

[36]  D. Blayney,et al.  Challenges and solutions. , 2007, Journal of oncology practice.

[37]  Nadezhda T. Doncheva,et al.  Topological analysis and interactive visualization of biological networks and protein structures , 2012, Nature Protocols.

[38]  H. Lehrach,et al.  A Human Protein-Protein Interaction Network: A Resource for Annotating the Proteome , 2005, Cell.

[39]  Chuanxing Li,et al.  The Dichotomy in Degree Correlation of Biological Networks , 2011, PloS one.

[40]  Barry Demchak,et al.  Porting and using PanGIA for Cytoscape 3: challenges and solutions , 2014, F1000Research.

[41]  Christian von Mering,et al.  STRING: a database of predicted functional associations between proteins , 2003, Nucleic Acids Res..

[42]  Andrei L. Turinsky,et al.  Bioinformatics Applications Note Systems Biology Orthonets: Simultaneous Visual Analysis of Orthologs and Their Interaction Neighborhoods across Different Organisms , 2022 .

[43]  Sharmila S. Mande,et al.  Gut Microbiomes of Indian Children of Varying Nutritional Status , 2014, PloS one.

[44]  L. Gan,et al.  SOCS3 promotes inflammation and apoptosis via inhibiting JAK2/STAT3 signaling pathway in 3T3-L1 adipocyte. , 2015, Immunobiology.

[45]  J. Raes,et al.  Microbial interactions: from networks to models , 2012, Nature Reviews Microbiology.

[46]  Brad T. Sherman,et al.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.

[47]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[48]  Sharmila S Mande,et al.  Community-analyzer: a platform for visualizing and comparing microbial community structure across microbiomes. , 2013, Genomics.

[49]  Sailu Yellaboina,et al.  Inferring genome-wide functional linkages in E. coli by combining improved genome context methods: comparison with high-throughput experimental data. , 2007, Genome research.

[50]  R. K. De,et al.  Comparing methods for metabolic network analysis and an application to metabolic engineering. , 2013, Gene.

[51]  Oliver Ebenhöh,et al.  A cross species comparison of metabolic network functions. , 2005, Genome informatics. International Conference on Genome Informatics.

[52]  Roded Sharan,et al.  QNet: A Tool for Querying Protein Interaction Networks , 2007, RECOMB.

[53]  Jerzy Tiuryn,et al.  MODEVO: exploring modularity and evolution of protein interaction networks , 2010, Bioinform..

[54]  Monzoorul Haque Mohammed,et al.  Microbial community profiling shows dysbiosis in the lesional skin of Vitiligo subjects , 2016, Scientific Reports.

[55]  R. Doms,et al.  Role of CCR5 in infection of primary macrophages and lymphocytes by macrophage-tropic strains of human immunodeficiency virus: resistance to patient-derived and prototype isolates resulting from the delta ccr5 mutation , 1997, Journal of virology.