01608nas a2200169 4500008004100000245010300041210006900144260000800213490000600221520097500227100001801202700002301220700001801243700002301261700002401284856013001308 2018 eng d00a Flim-Map: gene context based identification of functional modules in bacterial metabolic pathways0 aFlimMap gene context based identification of functional modules  cSEP0 v93 aPrediction of functional potential of bacteria can only be ascertained by the accurate annotation of its metabolic pathways. Homology based methods decipher metabolic gene content but ignore the fact that homologs of same protein can function in different pathways. Therefore, mere presence of all constituent genes in an organism is not sufficient to indicate a pathway. Contextual occurrence of genes belonging to a pathway on the bacterial genome can hence be exploited for an accurate estimation of functional potential of a bacterium. In this communication, we present a novel annotation resource to accurately identify pathway presence by using gene context. Our tool FLIM-MAP (Functionally Important Modules in bacterial Metabolic Pathways) predicts biologically relevant functional units called 'GCMs' (Gene Context based Modules) from a given metabolic reaction network. We benchmark the accuracy of our tool on amino acids and carbohydrate metabolism pathways.1 aBhatt, Vineet1 aMohapatra, Anwesha1 aAnand, Swadha1 aKuntal, Bhusan, K.1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/flim-map-gene-context-based-identification-functional-modules-bacterial-metabolic-pathways02685nas a2200157 4500008004100000022001300041245011100054210006900165260000800234490000600242520207700248100002402325700002302349700002302372856013202395 2018 eng d a1664302X00a'TIME': A web application for obtaining Insights into Microbial Ecology using longitudinal microbiome data0 aTIME A web application for obtaining Insights into Microbial Eco cJAN0 v93 a

Realization of the importance of microbiome studies, coupled with the decreasing sequencing cost, has led to the exponential growth of microbiome data. A number of these microbiome studies have focused on understanding changes in the microbial community over time. Such longitudinal microbiome studies have the potential to offer unique insights pertaining to the microbial social networks as well as their responses to perturbations. In this communication, we introduce a web based framework called ‘TIME’ (Temporal Insights into Microbial Ecology’), developed specifically to obtain meaningful insights from microbiome time series data. The TIME web-server is designed to accept a wide range of popular formats as input with options to preprocess and filter the data. Multiple samples, defined by a series of longitudinal time points along with their metadata information, can be compared in order to interactively visualize the temporal variations. In addition to standard microbiome data analytics, the web server implements popular time series analysis methods like Dynamic time warping, Granger causality and Dickey Fuller test to generate interactive layouts for facilitating easy biological inferences. Apart from this, a new metric for comparing metagenomic time series data has been introduced to effectively visualize the similarities/differences in the trends of the resident microbial groups. Augmenting the visualizations with the stationarity information pertaining to the microbial groups is utilized to predict the microbial competition as well as community structure. Additionally, the ‘causality graph analysis’ module incorporated in TIME allows predicting taxa that might have a higher influence on community structure in different conditions. TIME also allows users to easily identify potential taxonomic markers from a longitudinal microbiome analysis. We illustrate the utility of the web-server features on a few published time series microbiome data and demonstrate the ease with which it can be used to perform complex analysis.

1 aBaksi, Krishanu, D.1 aKuntal, Bhusan, K.1 aMande, Sharmila, S uhttps://library.ncl.res.in/content/time-web-application-obtaining-insights-microbial-ecology-using-longitudinal-microbiome-data02029nas a2200157 4500008004100000245008200041210006900123260000800192490000700200520145900207100002601666700002401692700002301716700002401739856010801763 2019 eng d00a"EviMass": A literature evidence-based miner for human microbial associations0 aEviMass A literature evidencebased miner for human microbial ass cSEP0 v103 a

The importance of understanding microbe-microbe as well as microbe-disease associations is one of the key thrust areas in human microbiome research. High-throughput metagenomic and transcriptomic projects have fueled discovery of a number of new microbial associations. Consequently, a plethora of information is being added routinely to biomedical literature, thereby contributing toward enhancing our knowledge on microbial associations. In this communication, we present a tool called "EviMass" (Evidence based mining of human Microbial Associations), which can assist biologists to validate their predicted hypotheses from new microbiome studies. Users can interactively query the processed back-end database for microbe-microbe and disease-microbe associations. The EviMass tool can also be used to upload microbial association networks generated from a human "disease-control" microbiome study and validate the associations from biomedical literature. Additionally, a list of differentially abundant microbes for the corresponding disease can be queried in the tool for reported evidences. The results are presented as graphical plots, tabulated summary, and other evidence statistics. EviMass is a comprehensive platform and is expected to enable microbiome researchers not only in mining microbial associations, but also enriching a new research hypothesis. The tool is available free for academic use at https://web.rniapps.net/evimass.

1 aSrivastava, Divyanshu1 aBaksi, Krishanu, D.1 aKuntal, Bhusan, K.1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/evimass-literature-evidence-based-miner-human-microbial-associations01322nas a2200121 4500008004100000245004200041210004200083490000700125520094700132100002301079700002401102856007401126 2019 eng d00aVisual exploration of microbiome data0 aVisual exploration of microbiome data0 v443 a

A dramatic increase in large-scale cross-sectional and temporal-level metagenomic experiments has led to an improved understanding of the microbiome and its role in human well-being. Consequently, a plethora of analytical methods has been developed to decipher microbial biomarkers for various diseases, cluster different ecosystems based on microbial content, and infer functional potential of the microbiome as well as analyze its temporal behavior. Development of user-friendly visualization methods and frameworks is necessary to analyze this data and infer taxonomic and functional patterns corresponding to a phenotype. Thus, new methods as well as application of pre-existing ones has gained importance in recent times pertaining to the huge volume of the generated microbiome data. In this reveiw, we present a brief overview of some useful visualization techniques that have significantly enriched microbiome data analytics.

1 aKuntal, Bhusan, K.1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/visual-exploration-microbiome-data01854nas a2200265 4500008004100000022001400041245010800055210006900163260000900232300000800241490000700249520099900256653001901255653002501274653001501299653001301314653002501327653001601352653001801368653001501386100002301401700001901424700002401443856012101467 2019 eng d a1664-302X00aWeb-gLV: a web based platform for lotka-volterra based modeling and simulation of microbial populations0 aWebgLV a web based platform for lotkavolterra based modeling and cFEB  a2880 v103 a

The affordability of high throughput DNA sequencing has allowed us to explore the dynamics of microbial populations in various ecosystems. Mathematical modeling and simulation of such microbiome time series data can help in getting better understanding of bacterial communities. In this paper; we present Web-gLV- a GUI based interactive platform for generalized Lotka-Volterra (gLV) based modeling and simulation of microbial populations. The tool can be used to generate the mathematical models with automatic estimation of parameters and use them to predict future trajectories using numerical simulations. We also demonstrate the utility of our tool on few publicly available datasets. The case studies demonstrate the ease with which the current tool can be used by biologists to model bacterial populations and simulate their dynamics to get biological insights. We expect Web-gLV to be a valuable contribution in the field of ecological modeling and metagenomic systems biology.

10alotka-volterra10amicrobial population10aMicrobiome10aModeling10anumerical-simulation10atime-series10avisualization10aweb-server1 aKuntal, Bhusan, K.1 aGadgil, Chetan1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/web-glv-web-based-platform-lotka-volterra-based-modeling-and-simulation-microbial00772nas a2200217 4500008004100000245010000041210006900141260000900210300001100219490000700230653001800237653002200255653002600277653001500303653001500318100002600333700002400359700002300383700002400406856012400430 2020 eng d00aEviMass: a literature evidence-based miner for human microbial associations (vol 10, 849, 2019)0 aEviMass a literature evidencebased miner for human microbial ass cNOV  a6140510 v1110aHuman Disease10aliterature mining10amicrobial association10aMicrobiome10aWeb server1 aSrivastava, Divyanshu1 aBaksi, Krishanu, D.1 aKuntal, Bhusan, K.1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/evimass-literature-evidence-based-miner-human-microbial-associations-vol-10-849-201900867nas a2200253 4500008004100000022001400041245012200055210006900177260000800246300001100254490000700265653001500272653001300287653002500300653001700325653001100342653002200353100001800375700002300393700001800416700002300434700002400457856013200481 2020 eng d a1664-302X00aFlim-MAP: gene context based identification of functional modules in bacterial metabolic pathways (vol 9, 2183, 2018)0 aFlimMAP gene context based identification of functional modules  cOCT a6054190 v1110aannotation10aBacteria10afunctional potential10agene context10agenome10ametabolic pathway1 aBhatt, Vineet1 aMohapatra, Anwesha1 aAnand, Swadha1 aKuntal, Bhusan, K.1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/flim-map-gene-context-based-identification-functional-modules-bacterial-metabolic-pathways-001572nas a2200193 4500008004100000022001400041245013700055210006900192260000900261300001400270490000700284520085300291100001801144700002301162700002301185700001801208700002401226856012801250 2020 eng d a1367-480300aFunGeCo: a web-based tool for estimation of functional potential of bacterial genomes and microbiomes using gene context information0 aFunGeCo a webbased tool for estimation of functional potential o cAPR  a2575-25770 v363 a

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.

1 aAnand, Swadha1 aKuntal, Bhusan, K.1 aMohapatra, Anwesha1 aBhatt, Vineet1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/fungeco-web-based-tool-estimation-functional-potential-bacterial-genomes-and-microbiomes00737nas a2200169 4500008004100000245010900041210006900150260000800219300000800227490000700235520010500242100001800347700002500365700002300390700002400413856013000437 2020 eng d00aNetConfer: a web application for comparative analysis of multiple biological networks (vol 18, 53, 2020)0 aNetConfer a web application for comparative analysis of multiple cOCT a1470 v183 a

An amendment to this paper has been published and can be accessed via the original article.

1 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-networks-vol-18-53-202002806nas 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

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.

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-networks00850nas a2200241 4500008004100000022001400041245012700055210006900182260000800251300001100259490000700270653001500277653002000292653003200312653001500344653001600359653001800375653001500393100002400408700002300432700002400455856012900479 2020 eng d a1664-302X00aTime: a web application for obtaining insights into microbial ecology using longitudinal microbiome data (vol 9, 36, 2018)0 aTime a web application for obtaining insights into microbial eco cNOV a6052950 v1110aclustering10acommunity state10aGranger causality algorithm10aMicrobiome10atime series10avisualization10aWeb server1 aBaksi, Krishanu, D.1 aKuntal, Bhusan, K.1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/time-web-application-obtaining-insights-microbial-ecology-using-longitudinal-microbiome-001084nas a2200157 4500008004100000022001400041245006200055210005800117260000800175300000800183490000700191520059100198100002300789700002400812856009000836 2020 eng d a0250-599100aVisual exploration of microbiome data (vol 44, 119, 2019)0 aVisual exploration of microbiome data vol 44 119 2019 cOCT a1340 v453 a

In the October 2019 Special Issue of theJournal of Bioscienceson Current Trends in Microbiome Research, in the Review article titled `'Visual exploration of microbiome data'' by Bhusan K. Kuntal and Sharmila S. Mande (DOI: 10.1007/s12038-019-9933-z; Vol. 44, Article No. 119), affiliation 3 for Bhusan K. Kuntal was incorrectly mentioned as `'Academy of Scientific and Innovative Research, CSIR-National Chemical Laboratory Campus, Pune 411008, India''. The correct affiliation should read as `'Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201 002, India''.

1 aKuntal, Bhusan, K.1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/visual-exploration-microbiome-data-vol-44-119-201900868nas a2200253 4500008004100000022001400041245012800055210006900183260000900252300001100261490000700272653001900279653002500298653001500323653001300338653002500351653001600376653001800392653001500410100002300425700001900448700002400467856012300491 2021 eng d a1664-302X00aWeb-gLV: a web based platform for lotka-volterra based modeling and simulation of microbial populations (vol 10, 288, 2019)0 aWebgLV a web based platform for lotkavolterra based modeling and cJAN  a6053080 v1110alotka-volterra10amicrobial population10aMicrobiome10aModeling10anumerical-simulation10atime-series10avisualization10aweb-server1 aKuntal, Bhusan, K.1 aGadgil, Chetan1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/web-glv-web-based-platform-lotka-volterra-based-modeling-and-simulation-microbial-002050nas a2200229 4500008004100000022001400041245008000055210006900135260000800204300001200212490000800224520132400232653001301556653001701569653001501586653001001601653003201611100002101643700002301664700002401687856010901711 2024 eng d a1364-507200aRole and significance of virus-bacteria interactions in disease progression0 aRole and significance of virusbacteria interactions in disease p cJUN alxae1300 v1353 a

Understanding disease pathogenesis caused by bacteria/virus, from the perspective of individual pathogen has provided meaningful insights. However, as viral and bacterial counterparts might inhabit the same infection site, it becomes crucial to consider their interactions and contributions in disease onset and progression. The objective of the review is to highlight the importance of considering both viral and bacterial agents during the course of coinfection. The review provides a unique perspective on the general theme of virus-bacteria interactions, which either lead to colocalized infections that are restricted to one anatomical niche, or systemic infections that have a systemic effect on the human host. The sequence, nature, and underlying mechanisms of certain virus-bacteria interactions have been elaborated with relevant examples from literature. It also attempts to address the various applied aspects, including diagnostic and therapeutic strategies for individual infections as well as virus-bacteria coinfections. The review aims to aid researchers in comprehending the intricate interplay between virus and bacteria in disease progression, thereby enhancing understanding of current methodologies and empowering the development of novel health care strategies to tackle coinfections.

10aBacteria10acoinfections10ainfections10avirus10avirus-bacteria interactions1 aPokhrel, Vatsala1 aKuntal, Bhusan, K.1 aMande, Sharmila, S. uhttps://library.ncl.res.in/content/role-and-significance-virus-bacteria-interactions-disease-progression02096nas 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

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.

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