Role and significance of virus-bacteria interactions in disease progression.

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.

NetSets.js: a JavaScript framework for compositional assessment and comparison of biological networks through Venn-integrated network diagrams.

MOTIVATION: Venn diagrams are frequently used to compare composition of datasets (e.g. datasets containing list of proteins and genes). Network diagram constructed using such datasets are usually generated using 'list of edges', popularly known as edge-lists. An edge-list and the corresponding generated network are, however, composed of two elements, namely, edges (e.g. protein-protein interactions) and nodes (e.g. proteins). Researchers often use individual lists of edges and nodes to compare composition of biological networks using existing Venn diagram tools. However, specialized analysis workflows are required for comparison of nodes as well as edges. Apart from this, different tools or graph libraries are needed for visualizing any specific edges of interest (e.g. protein-protein interactions which are present across all networks or are shared between subset of networks or are exclusively present in a selected network). Further, these results are required to be exported in the form of publication worthy network diagram(s), particularly for small networks. RESULTS: We introduce a (server independent) JavaScript framework (called NetSets.js) that integrates popular Venn and network diagrams in a single application. A free to use intuitive web application (utilizing NetSets.js), specifically designed to perform both compositional comparisons (e.g. for identifying common/exclusive edges or nodes) and interactive user defined visualizations of network (for the identified common/exclusive interactions across multiple networks) using simple edge-lists is also presented. The tool also enables connection to Cytoscape desktop application using the Netsets-Cyapp. We demonstrate the utility of our tool using real world biological networks (microbiome, gene interaction, multiplex and protein-protein interaction networks). AVAILABILITYAND IMPLEMENTATION: http://web.rniapps.net/netsets (freely available for academic use). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sensing Host Health: Insights from Sensory Protein Signature of the Metagenome.

The human microbiota, which comprises an ensemble of taxonomically and functionally diverse but often mutually cooperating microorganisms, benefits its host by shaping the host immunity, energy harvesting, and digestion of complex carbohydrates as well as production of essential nutrients. Dysbiosis in the human microbiota, especially the gut microbiota, has been reported to be linked to several diseases and metabolic disorders. Recent studies have further indicated that tracking these dysbiotic variations could potentially be exploited as biomarkers of disease states. However, the human microbiota is not geography agnostic, and hence a taxonomy-based (microbiome) biomarker for disease diagnostics has certain limitations. In comparison, (microbiome) function-based biomarkers are expected to have a wider applicability. Given that (i) the host physiology undergoes certain changes in the course of a disease and (ii) host-associated microbial communities need to adapt to this changing microenvironment of their host, we hypothesized that signatures emanating from the abundance of bacterial proteins associated with the signal transduction system (herein referred to as sensory proteins [SPs]) might be able to distinguish between healthy and diseased states. To test this hypothesis, publicly available metagenomic data sets corresponding to three diverse health conditions, namely, colorectal cancer, type 2 diabetes mellitus, and schizophrenia, were analyzed. Results demonstrated that SP signatures (derived from host-associated metagenomic samples) indeed differentiated among healthy individual and patients suffering from diseases of various severities. Our finding was suggestive of the prospect of using SP signatures as early biomarkers for diagnosing the onset and progression of multiple diseases and metabolic disorders. IMPORTANCE The composition of the human microbiota, a collection of host-associated microbes, has been shown to differ among healthy and diseased individuals. Recent studies have investigated whether tracking these variations could be exploited for disease diagnostics. It has been noted that compared to microbial taxonomies, the ensemble of functional proteins encoded by microbial genes are less likely to be affected by changes in ethnicity and dietary preferences. These functions are expected to help the microbe adapt to changing environmental conditions. Thus, healthy individuals might harbor a different set of genes than diseased individuals. To test this hypothesis, we analyzed metagenomes from healthy and diseased individuals for signatures of a particular group of proteins called sensory proteins (SP), which enable the bacteria to sense and react to changes in their microenvironment. Results demonstrated that SP signatures indeed differentiate among healthy individuals and those suffering from diseases of various severities.

A perspective on the benefits of consumption of parboiled rice over brown rice for glycaemic control.

PURPOSE: Rice is a staple food for over 3.5 billion people worldwide. The nutritional content of rice varies with different post-harvest processing techniques. Major varieties include brown rice (BR), white rice (WR) and parboiled rice (PBR). While consumption of BR is advocated due to its higher nutritional content compared to other varieties, some studies have indicated lower post-prandial blood glucose (PPBG) levels when PBR is consumed. This apparent benefit of PBR consumption is not well publicised and no commentaries on underlying mechanisms are available in literature. METHODS: In this review, we looked into differential nutrient content of PBR, as compared to BR and WR, and tried to understand how their consumption could be associated with glycaemic control. Various roles played by these nutrients in mechanisms of insulin secretion, insulin resistance, nutrient absorption and T2DM-associated inflammation were reviewed from literature-based evidence. RESULTS: We report differential nutritional factors in PBR, with respect to BR (and WR), such as higher calcium and selenium content, lower phytic acids, and enriched vitamin B6 which might aid PBR's ability to provide better glycaemic control than BR. CONCLUSION: Our interpretation of reviewed literature leads us to suggest the possible benefits of PBR consumption in glycaemic control and its inclusion as the preferred rice variant in diets of T2DM patients and at-risk individuals.

MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks.

Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group 'meta-data' and 'inter-omic' functional profiles that are often associated with microbiome studies. This not only necessitates the need for categorical networks, but also integrated and bi-partite networks. Multiple options of network inference algorithms further add to the efforts required for performing correlation-based microbiome interaction studies. We present MetagenoNets, a web-based application, which accepts multi-environment microbial abundance as well as functional profiles, intelligently segregates 'continuous and categorical' meta-data and allows inference as well as visualization of categorical, integrated (inter-omic) and bi-partite networks. Modular structure of MetagenoNets ensures logical flow of analysis (inference, integration, exploration and comparison) in an intuitive and interactive personalized dashboard driven framework. Dynamic choice of filtration, normalization, data transformation and correlation algorithms ensures, that end-users get a one-stop solution for microbial network analysis. MetagenoNets is freely available at https://web.rniapps.net/metagenonets.

FunGeCo: a web-based tool for estimation of functional potential of bacterial genomes and microbiomes using gene context information.

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. AVAILABILITY AND IMPLEMENTATION: https://web.rniapps.net/fungeco [Freely available for academic use]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Correction to: NetConfer: a web application for comparative analysis of multiple biological networks.

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

NetConfer: a web application for comparative analysis of multiple biological networks.

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.

Can Pathogenic and Nonpathogenic Bacteria Be Distinguished by Sensory Protein Abundance?

Signal transduction systems are essential for microorganisms to respond to their ever-changing environment. They can be distinguished into one-component systems, two-component systems, and extracytoplasmic-function σ factors. Abundances of a few signal-transducing proteins, termed herein as sensory proteins (SPs), have previously been reported to be correlated with the genome size and ecological niche of certain Gram-positive bacteria. No such reports are available for Gram-negative bacteria. The current study attempts to investigate the relationship of the abundances of SPs to genome size in Escherichia coli, and the bacterial pathotypes or phylotypes. While the relationship between SP abundance and genome size could not be established, the sensory protein index (SPI), a new metric defined herein, was found to be correlated with E. coli virulence. In addition, significant association was observed among the distribution of SPs and E. coli pathotypes. Results indicate that such associations might be due to genomic rearrangements to best utilize the resources available in a given ecological niche. Overall, the study provides an in-depth analysis of the occurrence of different SPs among pathogenic and nonpathogenic E. coli strains. Possibilities of using the SPI as a marker for identifying pathogenic strains from among an organism complex are also discussed.IMPORTANCE Sensory proteins (SPs) act as sensors and actuators for a cell and participate in important mechanisms pertaining to bacterial survival, adaptation, and virulence. Therefore, bacterial species residing in similar ecological niches or those sharing common pathotypes are expected to exhibit similar SP signatures. We have investigated profiles of SPs in different species of Escherichia coli and present in this article the sensory protein index (SPI), a metric for quantifying the abundance and/or distribution of SPs across bacterial genomes, which could indicate the virulence potency of a bacterium. The SPI could find use in characterizing uncultured strains and bacterial complexes, as a biomarker for disease diagnostics, evaluating the effect of therapeutic interventions, assessing effects of ecological alterations, etc. Grouping the studied strains of E. coli on the basis of the frequency of occurrence of SPs in their genomes could potentially replicate the stratification of these strains on the basis of their phylotypes. In addition, E. coli strains belonging to the same pathotypes were also seen to share similar SP signatures. Furthermore, the SPI was seen to be an indicator of pathogenic potency of E. coli strains. The SPI metric is expected to be useful in the (pathogenic) characterization of hereto uncultured strains which are routinely sequenced in host microbiome analysis projects, or from among an ensemble of microbial organisms constituting a biospecimen. Thus, the possibilities of using the SPI as a biomarker for diagnosis of a disease or the outcome of a therapeutic intervention cannot be ruled out. Further, SPIs obtained from longitudinal ecological samples have the potential to serve as key indicators of environmental changes. Such changes in the environment are often detrimental to the resident biome and methods for timely detection of environmental changes hold huge socioeconomic benefits.

Correction to: CompNet: a GUI based tool for comparison of multiple biological interaction networks.

Following publication of the original article [1], the authors requested to update a link in the article. There was a server update and the hosted applications needed to move to a new web location.

Investigating host-bacterial interactions among enteric pathogens.

BACKGROUND: In 2017, World Health Organization (WHO) published a catalogue of 12 families of antibiotic-resistant "priority pathogens" that are posing the greatest threats to human health. Six of these dreaded pathogens are known to infect the human gastrointestinal system. In addition to causing gastrointestinal and systemic infections, these pathogens can also affect the composition of other microbes constituting the healthy gut microbiome. Such aberrations in gut microbiome can significantly affect human physiology and immunity. Identifying the virulence mechanisms of these enteric pathogens are likely to help in developing newer therapeutic strategies to counter them. RESULTS: Using our previously published in silico approach, we have evaluated (and compared) Host-Pathogen Protein-Protein Interaction (HPI) profiles of four groups of enteric pathogens, namely, different species of Escherichia, Shigella, Salmonella and Vibrio. Results indicate that in spite of genus/ species specific variations, most enteric pathogens possess a common repertoire of HPIs. This core set of HPIs are probably responsible for the survival of these pathogen in the harsh nutrient-limiting environment within the gut. Certain genus/ species specific HPIs were also observed. CONSLUSIONS: The identified bacterial proteins involved in the core set of HPIs are expected to be helpful in understanding the pathogenesis of these dreaded gut pathogens in greater detail. Possible role of genus/ species specific variations in the HPI profiles in the virulence of these pathogens are also discussed. The obtained results are likely to provide an opportunity for development of novel therapeutic strategies against the most dreaded gut pathogens.

Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach.

BACKGROUND: Mycobacterium tuberculosis infection in humans is often associated with extended period of latency. To adapt to the hostile hypoxic environment inside a macrophage, M. tuberculosis cells undergo several physiological and metabolic changes. Previous studies have mostly focused on inspecting individual facets of this complex process. In order to gain deeper insights into the infection process and to understand the coordination among different regulatory/ metabolic pathways in the pathogen, the current in silico study investigates three aspects, namely, (i) host-pathogen interactions (HPIs) between human and M. tuberculosis proteins, (ii) gene regulatory network pertaining to adaptation of M. tuberculosis to hypoxia and (iii) alterations in M. tuberculosis metabolism under hypoxic condition. Subsequently, cross-talks between these components have been probed to evaluate possible gene-regulatory events as well as HPIs which are likely to drive metabolic changes during pathogen's adaptation to the intra-host hypoxic environment. RESULTS: The newly identified HPIs suggest the pathogen's ability to subvert host mediated reactive oxygen intermediates/ reactive nitrogen intermediates (ROI/ RNI) stress as well as their potential role in modulating host cell cycle and cytoskeleton structure. The results also indicate a significantly pronounced effect of HPIs on hypoxic metabolism of M. tuberculosis. Findings from the current study underscore the necessity of investigating the infection process from a systems-level perspective incorporating different facets of intra-cellular survival of the pathogen. CONCLUSIONS: The comprehensive host-pathogen interaction network, a Boolean model of M. tuberculosis H37Rv (Mtb) hypoxic gene-regulation, as well as a genome scale metabolic model of Mtb, built for this study are expected to be useful resources for future studies on tuberculosis infection.

Web-igloo: a web based platform for multivariate data visualization.

Motivation: The majority of data generated routinely from various experiments are essentially multivariate, often categorized with multiple experimental metadata. Analyzing such results with interactive visualizations often yields interesting and intuitive results which otherwise remains undisclosed. Results: In this paper, we present Web-Igloo-a GUI based interactive 'feature decomposition independent' multivariate data visualization platform. Web-Igloo is likely to be a valuable contribution in the field of visual data mining, especially for researchers working with but not limited to multi-omics data. To demonstrate its utility, we have used a metagenomic dataset pertaining to the effect of multiple doses of antibiotic treatment on the human gut microbiome. Availability and Implementation: http://metagenomics.atc.tcs.com/webigloo and http://121.241.184.233/webigloo [Freely available for academic use]. Contact: sharmila@atc.tcs.com. Supplementary information: Supplementary data are available at Bioinformatics online.

CompNet: a GUI based tool for comparison of multiple biological interaction networks.

BACKGROUND: Network visualization and analysis tools aid in better understanding of complex biological systems. Furthermore, to understand the differences in behaviour of system(s) under various environmental conditions (e.g. stress, infection), comparing multiple networks becomes necessary. Such comparisons between multiple networks may help in asserting causation and in identifying key components of the studied biological system(s). Although many available network comparison methods exist, which employ techniques like network alignment and querying to compute pair-wise similarity between selected networks, most of them have limited features with respect to interactive visual comparison of multiple networks. RESULTS: In this paper, we present CompNet - a graphical user interface based network comparison tool, which allows visual comparison of multiple networks based on various network metrics. CompNet allows interactive visualization of the union, intersection and/or complement regions of a selected set of networks. Different visualization features (e.g. pie-nodes, edge-pie matrix, etc.) aid in easy identification of the key nodes/interactions and their significance across the compared networks. The tool also allows one to perform network comparisons on the basis of neighbourhood architecture of constituent nodes and community compositions, a feature particularly useful while analyzing biological networks. To demonstrate the utility of CompNet, we have compared a (time-series) human gene-expression dataset, post-infection by two strains of Mycobacterium tuberculosis, overlaid on the human protein-protein interaction network. Using various functionalities of CompNet not only allowed us to comprehend changes in interaction patterns over the course of infection, but also helped in inferring the probable fates of the host cells upon infection by the two strains. CONCLUSIONS: CompNet is expected to be a valuable visual data mining tool and is freely available for academic use from http://metagenomics.atc.tcs.com/compnet/ or http://121.241.184.233/compnet/.

CS-SCORE: Rapid identification and removal of human genome contaminants from metagenomic datasets.

UNLABELLED: Metagenomic sequencing data, obtained from host-associated microbial communities, are usually contaminated with host genome sequence fragments. Prior to performing any downstream analyses, it is necessary to identify and remove such contaminating sequence fragments. The time and memory requirements of available host-contamination detection techniques are enormous. Thus, processing of large metagenomic datasets is a challenging task. This study presents CS-SCORE--a novel algorithm that can rapidly identify host sequences contaminating metagenomic datasets. Validation results indicate that CS-SCORE is 2-6 times faster than the current state-of-the-art methods. Furthermore, the memory footprint of CS-SCORE is in the range of 2-2.5GB, which is significantly lower than other available tools. CS-SCORE achieves this efficiency by incorporating (1) a heuristic pre-filtering mechanism and (2) a directed-mapping approach that utilizes a novel sequence composition metric (cs-score). CS-SCORE is expected to be a handy 'pre-processing' utility for researchers analyzing metagenomic datasets. AVAILABILITY: For academic users, an implementation of CS-SCORE is freely available at: http://metagenomics.atc.tcs.com/cs-score (or) https://metagenomics.atc.tcs.com/preprocessing/cs-score.

MetaCAA: A clustering-aided methodology for efficient assembly of metagenomic datasets.

A key challenge in analyzing metagenomics data pertains to assembly of sequenced DNA fragments (i.e. reads) originating from various microbes in a given environmental sample. Several existing methodologies can assemble reads originating from a single genome. However, these methodologies cannot be applied for efficient assembly of metagenomic sequence datasets. In this study, we present MetaCAA - a clustering-aided methodology which helps in improving the quality of metagenomic sequence assembly. MetaCAA initially groups sequences constituting a given metagenome into smaller clusters. Subsequently, sequences in each cluster are independently assembled using CAP3, an existing single genome assembly program. Contigs formed in each of the clusters along with the unassembled reads are then subjected to another round of assembly for generating the final set of contigs. Validation using simulated and real-world metagenomic datasets indicates that MetaCAA aids in improving the overall quality of assembly. A software implementation of MetaCAA is available at https://metagenomics.atc.tcs.com/MetaCAA.

Igloo-Plot: a tool for visualization of multidimensional datasets.

Advances in science and technology have resulted in an exponential growth of multivariate (or multi-dimensional) datasets which are being generated from various research areas especially in the domain of biological sciences. Visualization and analysis of such data (with the objective of uncovering the hidden patterns therein) is an important and challenging task. We present a tool, called Igloo-Plot, for efficient visualization of multidimensional datasets. The tool addresses some of the key limitations of contemporary multivariate visualization and analysis tools. The visualization layout, not only facilitates an easy identification of clusters of data-points having similar feature compositions, but also the 'marker features' specific to each of these clusters. The applicability of the various functionalities implemented herein is demonstrated using several well studied multi-dimensional datasets. Igloo-Plot is expected to be a valuable resource for researchers working in multivariate data mining studies. Igloo-Plot is available for download from: http://metagenomics.atc.tcs.com/IglooPlot/.

Classification of metagenomic sequences: methods and challenges.

Characterizing the taxonomic diversity of microbial communities is one of the primary objectives of metagenomic studies. Taxonomic analysis of microbial communities, a process referred to as binning, is challenging for the following reasons. Primarily, query sequences originating from the genomes of most microbes in an environmental sample lack taxonomically related sequences in existing reference databases. This absence of a taxonomic context makes binning a very challenging task. Limitations of current sequencing platforms, with respect to short read lengths and sequencing errors/artifacts, are also key factors that determine the overall binning efficiency. Furthermore, the sheer volume of metagenomic datasets also demands highly efficient algorithms that can operate within reasonable requirements of compute power. This review discusses the premise, methodologies, advantages, limitations and challenges of various methods available for binning of metagenomic datasets obtained using the shotgun sequencing approach. Various parameters as well as strategies used for evaluating binning efficiency are then reviewed.

Community-analyzer: a platform for visualizing and comparing microbial community structure across microbiomes.

A key goal in comparative metagenomics is to identify microbial group(s) which are responsible for conferring specific characteristics to a given environment. These characteristics are the result of the inter-microbial interactions between the resident microbial groups. We present a new GUI-based comparative metagenomic analysis application called Community-Analyzer which implements a correlation-based graph layout algorithm that not only facilitates a quick visualization of the differences in the analyzed microbial communities (in terms of their taxonomic composition), but also provides insights into the inherent inter-microbial interactions occurring therein. Notably, this layout algorithm also enables grouping of the metagenomes based on the probable inter-microbial interaction patterns rather than simply comparing abundance values of various taxonomic groups. In addition, the tool implements several interactive GUI-based functionalities that enable users to perform standard comparative analyses across microbiomes. For academic and non-profit users, the Community-Analyzer is currently available for download from: http://metagenomics.atc.tcs.com/Community_Analyzer/.

EnsembleSeq: a workflow towards real-time, rapid, and simultaneous multi-kingdom-amplicon sequencing for holistic and resource-effective microbiome research at scale.

Bacterial communities are often concomitantly present with numerous microorganisms in the human body and other natural environments. Amplicon-based microbiome studies have generally paid skewed attention, that too at a rather shallow genus level resolution, to the highly abundant bacteriome, with interest now forking toward the other microorganisms, particularly fungi. Given the generally sparse abundance of other microbes in the total microbiome, simultaneous sequencing of amplicons targeting multiple microbial kingdoms could be possible even with full multiplexing. Guiding studies are currently needed for performing and monitoring multi-kingdom-amplicon sequencing and data capture at scale. Aiming to address these gaps, amplification of full-length bacterial 16S rRNA gene and entire fungal internal-transcribed spacer (ITS) region was performed for human saliva samples (n = 96, including negative and positive controls). Combined amplicon DNA libraries were prepared for nanopore sequencing using a major fraction of 16S molecules and a minor fraction of ITS amplicons. Sequencing was performed in a single run of an R10.4.1 flow cell employing the latest V14 chemistry. An approach for real-time monitoring of the species saturation using dynamic rarefaction was designed as a guiding determinant of optimal run time. Real-time saturation monitoring for both bacterial and fungal species enabled the completion of sequencing within 30 hours, utilizing less than 60% of the total nanopores. Approximately 5 million high quality (HQ) taxonomically assigned reads were generated (~4.2 million bacterial and 0.7 million fungal), providing a wider (beyond bacteriome) snapshot of human oral microbiota at species-level resolution. Among the more than 400 bacterial and 240 fungal species identified in the studied samples, the species of Streptococcus (e.g., Streptococcus mitis and Streptococcus oralis) and Candida (e.g., Candida albicans and Candida tropicalis) were observed to be the dominating microbes in the oral cavity, respectively. This conformed well with the previous reports of the human oral microbiota. EnsembleSeq provides a proof-of-concept toward the identification of both fungal and bacterial species simultaneously in a single fully multiplexed nanopore sequencing run in a time- and resource-effective manner. Details of this workflow, along with the associated codebase, are provided to enable large-scale application for a holistic species-level microbiome study. IMPORTANCE: Human microbiome is a sum total of a variety of microbial genomes (including bacteria, fungi, protists, viruses, etc.) present in and on the human body. Yet, a majority of amplicon-based microbiome studies have largely remained skewed toward bacteriome as an assumed proxy of the total microbiome, primarily at a shallow genus level. Cost, time, effort, data quality/management, and importantly lack of guiding studies often limit progress in the direction of moving beyond bacteriome. Here, EnsembleSeq presents a proof-of-concept toward concomitantly capturing multiple-kingdoms of microorganisms (bacteriome and mycobiome) in a fully multiplexed (96-sample) single run of long-read amplicon sequencing. In addition, the workflow captures dynamic tracking of species-level saturation in a time- and resource-effective manner.