@article {45381, title = { Flim-Map: gene context based identification of functional modules in bacterial metabolic pathways}, journal = {Frontiers in microbiology}, volume = {9}, year = {2018}, month = {SEP}, type = {Article}, abstract = {Prediction 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 {\textquoteright}GCMs{\textquoteright} (Gene Context based Modules) from a given metabolic reaction network. We benchmark the accuracy of our tool on amino acids and carbohydrate metabolism pathways.}, doi = {10.3389/fmicb.2018.02183}, author = {Bhatt, Vineet and Mohapatra, Anwesha and Anand, Swadha and Kuntal, Bhusan K. and Mande, Sharmila S.} } @article {44475, title = {{\textquoteright}TIME{\textquoteright}: A web application for obtaining Insights into Microbial Ecology using longitudinal microbiome data}, journal = {Frontiers in Microbiology}, volume = {9}, year = {2018}, month = {JAN}, abstract = {

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 {\textquoteleft}TIME{\textquoteright} (Temporal Insights into Microbial Ecology{\textquoteright}), 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 {\textquoteleft}causality graph analysis{\textquoteright} 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.

}, issn = {1664302X}, doi = {10.3389/fmicb.2018.00036}, author = {Baksi, Krishanu D.}, editor = {Kuntal, Bhusan K.} } @article {46279, title = {"EviMass": A literature evidence-based miner for human microbial associations}, journal = {Frontiers in Genetics}, volume = {10}, year = {2019}, month = {SEP}, type = {Article}, abstract = {

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.

}, doi = {10.3389/fgene.2019.00849}, author = {Srivastava, Divyanshu and Baksi, Krishanu D. and Kuntal, Bhusan K. and Mande, Sharmila S.} } @article {46265, title = {Visual exploration of microbiome data}, journal = {Journal of Biosciences}, volume = {44}, year = {2019}, type = {Review}, abstract = {

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.

}, doi = {10.1007/s12038-019-9933-z}, author = {Kuntal, Bhusan K. and Mande, Sharmila S.} } @article {46188, title = {Web-gLV: a web based platform for lotka-volterra based modeling and simulation of microbial populations}, journal = {Frontiers in Microbiology}, volume = {10}, year = {2019}, month = {FEB }, pages = {288}, type = {Article}, abstract = {

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.

}, keywords = {lotka-volterra, microbial population, Microbiome, Modeling, numerical-simulation, time-series, visualization, web-server}, issn = {1664-302X}, doi = {10.3389/fmicb.2019.00288}, author = {Kuntal, Bhusan K. and Gadgil, Chetan and Mande, Sharmila S.} } @article {46654, title = {EviMass: a literature evidence-based miner for human microbial associations (vol 10, 849, 2019)}, journal = {Frontiers in Genetics}, volume = {11}, year = {2020}, month = {NOV }, pages = {614051}, type = {Correction}, keywords = {Human Disease, literature mining, microbial association, Microbiome, Web server}, doi = {10.3389/fgene.2020.614051}, author = {Srivastava, Divyanshu and Baksi, Krishanu D. and Kuntal, Bhusan K. and Mande, Sharmila S.} } @article {46663, title = {Flim-MAP: gene context based identification of functional modules in bacterial metabolic pathways (vol 9, 2183, 2018)}, journal = {Frontiers in Microbiology}, volume = {11}, year = {2020}, month = {OCT}, pages = {605419}, type = {Correction}, keywords = {annotation, Bacteria, functional potential, gene context, genome, metabolic pathway}, issn = {1664-302X}, doi = {10.3389/fmicb.2020.605419}, author = {Bhatt, Vineet and Mohapatra, Anwesha and Anand, Swadha and Kuntal, Bhusan K. and Mande, Sharmila S.} } @article {46890, title = {FunGeCo: a web-based tool for estimation of functional potential of bacterial genomes and microbiomes using gene context information}, volume = {36}, year = {2020}, month = {APR }, pages = {2575-2577}, type = {Article}, abstract = {

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 {\textquoteleft}FunGeCo{\textquoteright} 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.

}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btz957}, author = {Anand, Swadha and Kuntal, Bhusan K. and Mohapatra, Anwesha and Bhatt, Vineet and Mande, Sharmila S.} } @article {46668, title = {NetConfer: a web application for comparative analysis of multiple biological networks (vol 18, 53, 2020)}, journal = {BMC Biology}, volume = {18}, year = {2020}, month = {OCT}, pages = {147}, type = {Correction}, abstract = {

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

}, doi = {10.1186/s12915-020-00898-x}, author = {Nagpal, Sunil and Das Baksi, Krishanu and Kuntal, Bhusan K. and Mande, Sharmila S.} } @article {46852, title = {NetConfer: a web application for comparative analysis of multiple biological networks}, journal = {BMC Biology}, volume = {18}, year = {2020}, month = {MAY}, pages = {53}, type = {Article}, abstract = {

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.

}, keywords = {Bioinformatics, biological networks, Interaction networks, Network comparison, visualization}, doi = {10.1186/s12915-020-00781-9}, author = {Nagpal, Sunil and Baksi, Krishanu Das and Kuntal, Bhusan K. and Mande, Sharmila S.} } @article {47336, title = {Time: a web application for obtaining insights into microbial ecology using longitudinal microbiome data (vol 9, 36, 2018)}, journal = {Frontiers in Microbiology}, volume = {11}, year = {2020}, month = {NOV}, pages = {605295}, type = {Correction}, keywords = {clustering, community state, Granger causality algorithm, Microbiome, time series, visualization, Web server}, issn = {1664-302X}, doi = {10.3389/fmicb.2020.605295}, author = {Baksi, Krishanu D. and Kuntal, Bhusan K. and Mande, Sharmila S.} } @article {46674, title = {Visual exploration of microbiome data (vol 44, 119, 2019)}, journal = {Journal of Biosciences}, volume = {45}, year = {2020}, month = {OCT}, pages = {134}, type = {Correction}, abstract = {

In the October 2019 Special Issue of theJournal of Bioscienceson Current Trends in Microbiome Research, in the Review article titled {\textquoteleft}{\textquoteright}Visual exploration of microbiome data{\textquoteright}{\textquoteright} 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 {\textquoteleft}{\textquoteright}Academy of Scientific and Innovative Research, CSIR-National Chemical Laboratory Campus, Pune 411008, India{\textquoteright}{\textquoteright}. The correct affiliation should read as {\textquoteleft}{\textquoteright}Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201 002, India{\textquoteright}{\textquoteright}.

}, issn = {0250-5991}, doi = {10.1007/s12038-020-00103-9}, author = {Kuntal, Bhusan K. and Mande, Sharmila S.} } @article {47112, title = {Web-gLV: a web based platform for lotka-volterra based modeling and simulation of microbial populations (vol 10, 288, 2019)}, journal = {Frontiers in Microbiology}, volume = {11}, year = {2021}, month = {JAN }, pages = {605308}, type = {Correction}, keywords = {lotka-volterra, microbial population, Microbiome, Modeling, numerical-simulation, time-series, visualization, web-server}, issn = {1664-302X}, doi = {10.3389/fmicb.2020.605308}, author = {Kuntal, Bhusan K. and Gadgil, Chetan and Mande, Sharmila S.} }