@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 = {<p>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.</p>},
	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 = {<p>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.</p>
},
	doi = {10.3389/fgene.2019.00849},
	author = {Srivastava, Divyanshu and Baksi, Krishanu D. and Kuntal, Bhusan K. 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 {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.}
}
