02806nas a2200229   4500008004100000245009000041210006900131260000800200300000700208490000700215520204000222653001902262653002402281653002502305653002302330653001802353100001802371700002502389700002302414700002402437856011502461       2020                        eng d00aNetConfer: a web application for comparative analysis of multiple biological networks0 aNetConfer a web application for comparative analysis of multiple  cMAY  a530 v183 a<p>Background Most biological experiments are inherently designed to compare changes or transitions of state between conditions of interest. The advancements in data intensive research have in particular elevated the need for resources and tools enabling comparative analysis of biological data. The complexity of biological systems and the interactions of their various components, such as genes, proteins, taxa, and metabolites, have been inferred, represented, and visualized via graph theory-based networks. Comparisons of multiple networks can help in identifying variations across different biological systems, thereby providing additional insights. However, while a number of online and stand-alone tools exist for generating, analyzing, and visualizing individual biological networks, the utility to batch process and comprehensively compare multiple networks is limited. Results Here, we present a graphical user interface (GUI)-based web application which implements multiple network comparison methodologies and presents them in the form of organized analysis workflows. Dedicated comparative visualization modules are provided to the end-users for obtaining easy to comprehend, insightful, and meaningful comparisons of various biological networks. We demonstrate the utility and power of our tool using publicly available microbial and gene expression data. Conclusion NetConfer tool is developed keeping in mind the requirements of researchers working in the field of biological data analysis with limited programming expertise. It is also expected to be useful for advanced users from biological as well as other domains (working with association networks), benefiting from provided ready-made workflows, as they allow to focus directly on the results without worrying about the implementation. While the web version allows using this application without installation and dependency requirements, a stand-alone version has also been supplemented to accommodate the offline requirement of processing large networks.</p>
10aBioinformatics10abiological networks10aInteraction networks10aNetwork comparison10avisualization1 aNagpal, Sunil1 aBaksi, Krishanu, Das1 aKuntal, Bhusan, K.1 aMande, Sharmila, S.  uhttps://library.ncl.res.in/content/netconfer-web-application-comparative-analysis-multiple-biological-networks02096nas a2200241   4500008004100000022001400041245008200055210006900137260000800206300000800214490000800222520129400230653003001524653001901554653002001573653003901593653001501632100002901647700002501676700002101701700002301722856010901745       2025                        eng d  a0302-893300aMicrobiome knowledge graph as a tool to understand bacteria-host associations0 aMicrobiome knowledge graph as a tool to understand bacteriahost   cAUG  a2220 v2073 a<p>
	Gut bacteria are well known to significantly influence human health and physiology. Knowledge Graph (KG) can effectively integrate the heterogenous factors modulating gut bacteria-host associations. Limited studies describe the construction and application of KGs capturing these associations for domain experts. This work outlines a methodology for constructing microbiome-centric KG and demonstrates how it enhances conventional microbiome data analysis workflows. Towards construction and deployment of this domain centric KG, methodologies involved in collection of data, selecting relevant entities and relationships, and preprocessing them are discussed. Key relevant entities include bacteria, host genetic and immune factors, chemicals and diseases. The KG construction in both RDF (Resource Description Framework) and LPG (Labeled Property Graph) models are demonstrated. Comparison of the querying techniques in both these models and applications of the KG using biologically relevant case studies are also presented. Overall, the work is intended to provide domain experts with a complete protocol for construction of a microbiome-centric KG starting from entity selection and schema design to utilizing the KG for microbiome data analysis and hypothesis generation.</p>
10aBacteria-host association10aBioinformatics10aKnowledge graph10aKnowledge graph question answering10aMicrobiome1 aPudavar, Anand, Eruvessi1 aBaksi, Krishanu, Das1 aPokhrel, Vatsala1 aKuntal, Bhusan, K.  uhttps://library.ncl.res.in/content/microbiome-knowledge-graph-tool-understand-bacteria-host-associations