FLIM-MAP: Gene Context Based Identification of Functional Modules in Bacterial Metabolic Pathways

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 ‘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

EasyModeller: A graphical interface to MODELLER

<p>Abstract</p> <p>Background</p> <p>MODELLER is a program for automated protein Homology Modeling. It is one of the most widely used tool for homology or comparative modeling of protein three-dimensional structures, but most users find it a bit difficult to start with MODELLER as it is command line based and requires knowledge of basic Python scripting to use it efficiently.</p> <p>Findings</p> <p>The study was designed with an aim to develop of "EasyModeller" tool as a frontend graphical interface to MODELLER using Perl/Tk, which can be used as a standalone tool in windows platform with MODELLER and Python preinstalled. It helps inexperienced users to perform modeling, assessment, visualization, and optimization of protein models in a simple and straightforward way.</p> <p>Conclusion</p> <p>EasyModeller provides a graphical straight forward interface and functions as a stand-alone tool which can be used in a standard personal computer with Microsoft Windows as the operating system.</p

‘TIME’: A Web Application for Obtaining Insights into Microbial Ecology Using Longitudinal Microbiome Data

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

Functional Potential of Bacterial Communities using Gene Context Information

Estimation of the functional potential of a bacterial genome can be determined by accurate annotation of its metabolic pathways. Existing homology based methods for pathway annotation fail to account for homologous genes that participate in multiple pathways, causing overestimation of gene copy number. Mere presence of constituent genes of a candidate pathway which are dispersed on a genome often results in incorrect annotation, thereby leading to erroneous gene abundance and pathway estimation. Clusters of evolutionarily conserved coregulated genes are characteristic features in bacterial genomes and their spatial arrangement in the genome is constrained by the pathway encoded by them. Thus, in order to improve the accuracy of pathway prediction, it is important to augment homology based annotation with gene organization information. In this communication, we present a methodology considering prioritization of gene context for improved pathway annotation. Extensive literature mining was performed to confirm conserved juxtaposed arrangement of gene components of various pathways. Our method was utilized to identify and analyse the functional potential of all available completely sequenced bacterial genomes. The accuracy of the predicted gene clusters and their importance in metabolic pathways will be demonstrated using a few case studies. One of such case study corresponds to butyrate production pathways in gut bacteria where it was observed that gut pathogens and commensals possess a distinct set of pathway components. In another example, we will demonstrate how our methodology improves the prediction accuracy of carbohydrate metabolic potential in human microbial communities. Applicability of our method for estimation of functional potential in bacterial communities present in diverse environments will also be illustrated