Biomedical text can serve as a crucial source for obtaining a deeper understanding of the complexities pertaining to inter-bacterial associations. Existing methods are either based on bacterial co-occurrence observed in texts or rely on generic text mining methods, both of which may lead to extraction of limited amount of information. In this article, the efficiency of a multitude of generic text mining approaches for classifying text having bacterial associations have been first evaluated on a manually curated corpus. Further, a set of domain (or field of microbiology) specific features accompanied by three dictionaries relevant to bacterial associations were introduced. Incorporation of these domain-specific features in addition to generic text mining features from abstracts as well as sentences were shown to significantly improve the classification accuracy.

MOTIVATION: Venn diagrams are frequently used to compare composition of datasets (e.g. datasets containing list of proteins and genes). Network diagram constructed using such datasets are usually generated using 'list of edges', popularly known as edge-lists. An edge-list and the corresponding generated network are, however, composed of two elements, namely, edges (e.g. protein-protein interactions) and nodes (e.g. proteins). Researchers often use individual lists of edges and nodes to compare composition of biological networks using existing Venn diagram tools. However, specialized analysis workflows are required for comparison of nodes as well as edges. Apart from this, different tools or graph libraries are needed for visualizing any specific edges of interest (e.g. protein-protein interactions which are present across all networks or are shared between subset of networks or are exclusively present in a selected network). Further, these results are required to be exported in the form of publication worthy network diagram(s), particularly for small networks. RESULTS: We introduce a (server independent) JavaScript framework (called NetSets.js) that integrates popular Venn and network diagrams in a single application. A free to use intuitive web application (utilizing NetSets.js), specifically designed to perform both compositional comparisons (e.g. for identifying common/exclusive edges or nodes) and interactive user defined visualizations of network (for the identified common/exclusive interactions across multiple networks) using simple edge-lists is also presented. The tool also enables connection to Cytoscape desktop application using the Netsets-Cyapp. We demonstrate the utility of our tool using real world biological networks (microbiome, gene interaction, multiplex and protein-protein interaction networks). AVAILABILITYAND IMPLEMENTATION: http://web.rniapps.net/netsets (freely available for academic use). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Functional potential of genomes and metagenomes which are inferred using homology-based methods are often subjected to certain limitations, especially for proteins with homologs which function in multiple pathways. Augmenting the homology information with genomic location of the constituent genes can significantly improve the accuracy of estimated functions. This can help in distinguishing cognate homolog belonging to a candidate pathway from its other homologs functional in different pathways. RESULTS: In this article, we present a web-based analysis platform 'FunGeCo' to enable gene-context-based functional inference for microbial genomes and metagenomes. It is expected to be a valuable resource and complement the existing tools for understanding the functional potential of microbes which reside in an environment. AVAILABILITY AND IMPLEMENTATION: https://web.rniapps.net/fungeco [Freely available for academic use]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Following publication of the original article [1], the authors requested to update a link in the article. There was a server update and the hosted applications needed to move to a new web location.

Motivation: The majority of data generated routinely from various experiments are essentially multivariate, often categorized with multiple experimental metadata. Analyzing such results with interactive visualizations often yields interesting and intuitive results which otherwise remains undisclosed. Results: In this paper, we present Web-Igloo-a GUI based interactive 'feature decomposition independent' multivariate data visualization platform. Web-Igloo is likely to be a valuable contribution in the field of visual data mining, especially for researchers working with but not limited to multi-omics data. To demonstrate its utility, we have used a metagenomic dataset pertaining to the effect of multiple doses of antibiotic treatment on the human gut microbiome. Availability and Implementation: http://metagenomics.atc.tcs.com/webigloo and http://121.241.184.233/webigloo [Freely available for academic use]. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Network 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. RESULTS: In 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. CONCLUSIONS: CompNet 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/.