02685nas 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<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 ‘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.</p>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<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>
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-associations00772nas 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-10-849-201900850nas 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-0