Methods Mol. Biol. [JOURNAL]
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200 papers
Differential Scanning Calorimetry of Protein-Lipid Interactions.
Cañadas O, Casals C
Methods Mol Biol · 2026 · PMID 42156674 · Publisher ↗
Differential scanning calorimetry (DSC) is a powerful and highly sensitive nonperturbing technique used for studying the thermodynamic properties of thermally induced transitions. The ability of DSC to measure the effect...
Differential scanning calorimetry (DSC) is a powerful and highly sensitive nonperturbing technique used for studying the thermodynamic properties of thermally induced transitions. The ability of DSC to measure the effect of ligand binding on phase transitions of lipid membranes makes this technique particularly suitable for understanding interactions between proteins and lipid membranes. This chapter provides the newcomer and experienced practitioner with a comprehensive overview of basic DSC theory and an understanding of the capabilities of DSC instrumentation. Emphasis is placed on detailed analysis of DSC data to assess the effects of proteins on biomimetic membranes.
Isothermal Titration Calorimetry for Investigating Thermodynamics of Protein-Lipid Interactions.
Swamy MJ, Pawar SS
Methods Mol Biol · 2026 · PMID 42156673 · Publisher ↗
Isothermal titration calorimetry (ITC) is a remarkably versatile and powerful technique for studying the interactions between molecules. In ITC, intermolecular interactions are investigated by measuring heat changes (hea...
Isothermal titration calorimetry (ITC) is a remarkably versatile and powerful technique for studying the interactions between molecules. In ITC, intermolecular interactions are investigated by measuring heat changes (heat released/absorbed) occurring during the binding process. A very important feature of ITC is that it is a label-free technique and does not need any probe to be incorporated into the system. In view of this, it has become the method of choice for investigating the interaction of proteins with a wide range of species, including small ligands, other proteins, nucleic acids, drugs, nanoparticles, and metal ions. In recent years, ITC has gained popularity for studying protein interactions with lipids and lipid membranes as well. In this chapter, we provide a comprehensive overview of the ITC instrument and its practical application to determining the thermodynamic parameters characterizing the interaction between proteins and lipids. Employing ITC one can determine a variety of thermodynamic parameters, including enthalpy of binding (ΔH), binding stoichiometry (n), association constant (K), entropy of binding (ΔS), free energy of binding (ΔG), and change in heat capacity at constant pressure (ΔC) from a single calorimetric titration. Thus, ITC provides a complete thermodynamic description of the interaction, enhancing our understanding of the system. The ITC experimental system described in this chapter includes a well-known protein from bovine seminal plasma, PDC-109. This protein is chosen for its high affinity for choline-containing lipids, such as phosphatidylcholine, a major phospholipid component of the sperm cell membrane. Using this as a representative example, researchers can establish a standard protocol for investigating the thermodynamic parameters associated with the interaction of other soluble proteins with lipid membranes using ITC.
Surface Plasmon Resonance for Measuring Interactions of Proteins with Lipids and Lipid Membranes.
Šakanović A, Hodnik V, Zavec AB … +2 more , Filipič KE, Anderluh G
Methods Mol Biol · 2026 · PMID 42156672 · Publisher ↗
Surface plasmon resonance (SPR) is an established method for studying molecular interactions in real time. It allows obtaining qualitative and quantitative data on interactions of proteins with lipids or lipid membranes....
Surface plasmon resonance (SPR) is an established method for studying molecular interactions in real time. It allows obtaining qualitative and quantitative data on interactions of proteins with lipids or lipid membranes. In most of the approaches, a lipid membrane or a membrane-mimetic surface is prepared on the surface of Biacore (Cytiva) sensor chips, HPA or L1, and the studied protein is then injected across the surface. Here, we provide an overview of SPR in protein-lipid and protein-membrane interactions, different approaches described in the literature and a general protocol for conducting an SPR experiment, including lipid membranes, together with some experimental considerations.
Quartz Crystal Microbalances as Tools for Probing Protein-Membrane Interactions.
Nielsen SB, Otzen DE
Methods Mol Biol · 2026 · PMID 42156671 · Publisher ↗
Extensive studies on the spontaneous collapse of phospholipid vesicles into supported lipid bilayers (SLBs) have led to procedures that allow SLB formation on a wealth of substrates and lipid compositions. SLBs provide a...
Extensive studies on the spontaneous collapse of phospholipid vesicles into supported lipid bilayers (SLBs) have led to procedures that allow SLB formation on a wealth of substrates and lipid compositions. SLBs provide a widely accepted and versatile model system that mimics the natural cell membrane, separating the extracellular and intracellular fluids of the living cell. The quartz crystal microbalance with dissipation monitoring (QCM-D) has been central in both the understanding of vesicle collapse into SLBs on various substrates, but also in probing the kinetics and mechanisms of biomolecular interactions with SLBs in real time. We describe a robust procedure to form SLBs of zwitterionic and charged lipids on SiO sensor crystals, which subsequently can be exploited to probe the interaction between proteins and peptides with the SLB.
Characterization of Membrane Binding and Protein-Lipid Interactions at the Atomic Level with an Accelerated HMMM Model.
Hasdemir HS, Li Y, Kelich P … +2 more , Wen PC, Tajkhorshid E
Methods Mol Biol · 2026 · PMID 42156670 · Full text
Computational approaches, particularly molecular dynamics (MD) simulations, offer powerful tools for studying the membrane binding of peripheral membrane proteins (PMPs) and their interactions with lipids. However, the s...
Computational approaches, particularly molecular dynamics (MD) simulations, offer powerful tools for studying the membrane binding of peripheral membrane proteins (PMPs) and their interactions with lipids. However, the simulation of membranes at an atomic level is constrained by timescale limitations, further compounded by the slow diffusion of lipids in the membrane. The highly mobile membrane mimetic (HMMM) model was developed as one of the alternative membrane representations to overcome some of these limitations, particularly for investigating membrane binding of PMPs. By using short-tailed lipids, the HMMM model significantly enhances the lateral diffusion of lipids, thereby substantially accelerating membrane-associated phenomena. In this chapter, we outline best practices for setting up protein-HMMM systems and provide examples of relevant analyses. Ideally, one begins by thoroughly researching and understanding the protein of interest, including its cellular localization, the type of membrane it interacts with, and any biochemical evidence for structural changes upon membrane binding. Additional considerations include the quality of the available structures, as well as relevant posttranslational modifications, and involvement of ligands or cofactors. After constructing and equilibrating a representative full-length (FL) membrane with the desired lipid composition, it can be converted into an HMMM representation by using an in-house script provided in the SI materials or resources such as CHARMM-GUI. The PMP is then positioned on top of the HMMM membrane at a long enough distance from it and in different orientations to avoid biasing the membrane-binding process across multiple replicas with shuffled lipid arrangements. Following a careful equilibration protocol, one can capture spontaneous membrane-binding events for PMPs during production MD simulations. To quantify the membrane-binding events, one would first identify stable membrane-bound segments of the HMMM trajectories, which can be done by monitoring protein-membrane contacts. Analyzing these membrane-bound frames can then provide insights into membrane interaction hotspots, potential membrane-anchoring residues, the protein's selectivity for particular lipid types, and distinct membrane-bound poses that may be relevant to function or activation. Finally, it is crucial to verify the results from HMMM simulations by analyzing the findings after converting the protein-membrane system back to FL membranes, which can be done using an in-house script provided in the SI materials, and conducting additional simulations.
LambdaPy and LambdaR: Thermodynamics-Based Biogeochemical Reaction Modeling Packages for Integrating High-Resolution Mass Spectrometry Data.
Veeramani M, Kharel S, McCullough HC … +5 more , Chen X, Zheng J, Stegen JC, Scheibe TD, Song HS
Methods Mol Biol · 2026 · PMID 42156662 · Publisher ↗
Microorganisms are key drivers of biogeochemical cycles in natural environments. Microbially mediated biogeochemical reactions are influenced by both biotic and abiotic factors, including microbe-microbe interactions, en...
Microorganisms are key drivers of biogeochemical cycles in natural environments. Microbially mediated biogeochemical reactions are influenced by both biotic and abiotic factors, including microbe-microbe interactions, enzyme kinetics, and chemical traits. To address the impact of chemical substrates on biogeochemical reactions, this chapter provides guidance on substrate-explicit thermodynamic modeling (SXTM) (also known as lambda modeling). SXTM enables automatic formulation of stoichiometric and kinetic models of biogeochemistry from the chemical formulas of organic matter (OM). This approach is particularly useful for formulating biogeochemical reaction models from ultra-high-resolution mass spectrometry data that identify thousands of compounds with distinct molecular formulas. Regardless of the complexity of OM data, SXTM requires only two parameters, maximum growth rate (μ) and harvest volume (V), thereby avoiding the issue of overparameterization caused when one attempts to include a large set of chemical compounds. While the original formulation has been demonstrated with a focus on aerobic respiration, recent work has extended its scope to various other forms of electron acceptors. Here, we provide a tutorial on formulating biogeochemical reaction models using SXTM, with river corridor OM data collected by the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium as an example. The software packages for implementing SXTM are available in two languages, Python and R, and are referred to as LambdaPy and LambdaR, respectively. These tools will significantly facilitate modeling of complex OM pools in microbially driven biogeochemical cycling and will help improve our understanding of the interplay among microbes, enzymes, and OM when integrated with complementary approaches, including microbial- and enzyme-explicit modeling.
Dynamic Simulation of Growth and Cross-Feeding in Microbiomes with μbialSim.
Nawaz A, Schaefer JL, Centler F
Methods Mol Biol · 2026 · PMID 42156661 · Publisher ↗
Microbial cells in natural environments are typically embedded in microbial communities consisting of few to many different species. Close proximity and high diversity of neighboring cells facilitate manifold interaction...
Microbial cells in natural environments are typically embedded in microbial communities consisting of few to many different species. Close proximity and high diversity of neighboring cells facilitate manifold interactions on several layers, from substrate competition, exchange of genetic material, to metabolic cross-feeding. The complexity of these ecological interaction networks makes microbiomes notoriously difficult to study. While microbiome dynamics can routinely be elucidated by meta-omics technologies, pinpointing mechanisms driving these observed dynamics remains a challenge. Mechanistic mathematical modeling with its ability to focus on individual interactions and exploring their isolated impact on overall dynamics has emerged as a suitable tool in this context. Here, we use μbialSim, an open-source simulator that extends the Flux Balance Analysis approach to microbial communities, considering substrate competition and metabolic cross-feeding but neglecting any other microbial interactions. Assuming a well-mixed bioreactor environment, simulated trajectories enable the analysis of growth behavior of individual microbiome members, dynamics of intracellular enzymatic fluxes across all species, as well as the analysis of cross-feeding behavior and how it changes over time. The MATLAB implementation of μbialSim is available from https://github.com/fcentler/microbialSim .
Describing and Designing Microbial Community Metabolic Models In Silico: A Comprehensive Protocol Utilizing FLYCOP.
Del Ramo A, Granado DSL, Nogales J
Methods Mol Biol · 2026 · PMID 42156660 · Publisher ↗
Microbial communities play crucial roles in multiple natural and engineered environments, contributing to biogeochemical cycling, waste treatment, and biotechnological processes. Much like for single organisms, genome-sc...
Microbial communities play crucial roles in multiple natural and engineered environments, contributing to biogeochemical cycling, waste treatment, and biotechnological processes. Much like for single organisms, genome-scale metabolic models (GEMs) have become essential in contextualizing, designing and optimizing microbiomes. Computational modeling of microbial community metabolism provides valuable insights into community dynamics, interactions, and metabolic capabilities. Here, we present a comprehensive protocol for describing and engineering microbial community metabolic models in silico, leveraging FLYCOP (FLexible sYnthetic Consortium Optimization). Starting with available individual GEMs, this protocol covers the construction of condition-specific GEMs for community members, the generation of community-based metabolic models, and the analysis of community-wide metabolic capabilities and interactions. Furthermore, we showcase the utility of FLYCOP by illustrating its application in: (i) describing a community-driven complex biological process (e.g., denitrification) and (ii) designing a synthetic community for biotechnological purposes (e.g., production of violacein).
Predicting Interspecies Metabolic Dependencies in Microbial Communities by Integrating Flux Coupling Analysis with SteadyCom.
Zhang S, McCullough HC, Song HS
Methods Mol Biol · 2026 · PMID 42156659 · Publisher ↗
Microbial communities play a pivotal role in a wide range of ecological processes and engineered systems. The level of complexity in analyzing microbial communities necessitates the use of predictive mathematical models...
Microbial communities play a pivotal role in a wide range of ecological processes and engineered systems. The level of complexity in analyzing microbial communities necessitates the use of predictive mathematical models such as genome-scale metabolic networks. To identify metabolic interdependence among member species within microbial communities, we present a recently developed computational tool combining two existing approaches: SteadyCom and Flux Coupling Analysis (FCA). Both approaches leverage genome-scale metabolic networks as their primary inputs, albeit for distinct objectives. SteadyCom is a community modeling tool used to estimate flux distributions and metabolite exchanges within and across species, respectively, by constraining individual specific growth rates to be equal. In contrast, FCA has been used to identify the causal relationships among reactions with a primary focus on the analysis of individual metabolic networks, i.e., what reactions must be active for a target reaction to be active. The combination of these two approaches allows us to find how metabolic reactions in individual species are coordinated as an interacting community. Without any additional computations, the implementation of this algorithm also provides information on what reactions are blocked in the network. We provide all the information needed to implement these coupled computational tools in a Python Jupyter notebook.
Personalized Constraint-Based Modeling of Microbial Communities from Metagenomic Data.
Roma Pi J, Heinken A
Methods Mol Biol · 2026 · PMID 42156658 · Publisher ↗
High-throughput metagenomic sequencing techniques such as 16S rRNA and shotgun sequencing have enabled an unprecedented understanding of the structure and function of microbiome communities such as the human gut microbio...
High-throughput metagenomic sequencing techniques such as 16S rRNA and shotgun sequencing have enabled an unprecedented understanding of the structure and function of microbiome communities such as the human gut microbiome. Tailored dietary or therapeutic interventions targeting the microbiome could advance personalized medicine; however, predicting such interventions requires predictive systems biology methods. Constraint-Based Reconstruction and Analysis (COBRA) is a mechanistic systems biology approach that relies on detailed genome-scale reconstructions of a target organism's metabolism. A resource of genome-scale reconstructions of human microbes, AGORA, and its expansion in size and scope, AGORA2, have been developed through a semi-automated refinement pipeline, DEMETER. A user-friendly analysis pipeline, mgPipe, allows building and interrogating personalized models of microbiome communities from AGORA and AGORA2. Through sample-specific simulations, mgPipe can stratify patients and controls by the distinct metabolic capabilities of their microbiomes, starting from the processed metagenomic sequencing data. Building on this functionality, the protocol provides a comprehensive workflow for the contextualization of metagenomics data through personalized, mechanistic modeling. Comprehensive tutorials for the DEMETER and mgPipe workflows are presented, which will enable both systems biologists and microbiome scientists to contextualize metagenomic data and perform mechanistic simulations of diet-microbiome-host interactions.
Constraint-Based Modeling of Microbial Communities for Metabolite Production.
Ibrahim M, Raman K
Methods Mol Biol · 2026 · PMID 42156657 · Publisher ↗
In this chapter, we describe an in silico approach called CAMP (Co-Culture/Community Analyses for Metabolite Production) to predict two-species microbial communities best suited for producing a desired metabolite. Here,...
In this chapter, we describe an in silico approach called CAMP (Co-Culture/Community Analyses for Metabolite Production) to predict two-species microbial communities best suited for producing a desired metabolite. Here, we use genome-scale metabolic models (GEMs) to build microbial communities and constraint-based modeling methods such as flux balance analysis (FBA) to assess and identify suitable communities. Flux variability analysis (FVA) detects the maximum product flux in the communities. The interaction behavior between community members, i.e., mutualism, commensalism, parasitism, and competition, can be deduced based on the variations in the predicted growth rates of the species as monocultures and in co-cultures. In silico community optimization strategies to predict reaction knockouts that improve product flux have also been implemented. CAMP source codes are available from https://github.com/RamanLab/CAMP/tree/master/Protocol .
Constraint-Based Metabolic Modeling Approach for Microbial Communities.
Beura S, Roy SS, Das AK … +1 more , Ghosh A
Methods Mol Biol · 2026 · PMID 42156656 · Publisher ↗
Microorganisms grow in complex communities by fostering symbiotic relationships to uphold the integrity and functionality of the consortium. Deciphering the metabolic interactions within microbial communities and their i...
Microorganisms grow in complex communities by fostering symbiotic relationships to uphold the integrity and functionality of the consortium. Deciphering the metabolic interactions within microbial communities and their impact on host environments is essential due to their association with major domains, including human health, bioremediation, and bioenergy production. However, unraveling their metabolic activity in laboratory conditions is challenging, as many microbes resist cultivation, and recreating their complex natural ecosystem with all its biological parameters presents additional hurdles. Therefore, modeling the microbial communities has become crucial for comprehending the intricate interactions within diverse microbial populations. In this chapter, we elucidate an in silico methodology for reconstructing a genome-scale metabolic model of a microbial consortium. This community modeling approach encompasses the reconstruction of microbial models, the integration of individual models into a community, and the optimization of the community model under different environmental conditions. Furthermore, a wide range of flux analysis techniques, like Flux Balance Analysis (FBA), Flux Variability Analysis (FVA), and Flux Sampling (FS), were described to investigate both the community-wide flux profile and intermicrobial interactions.
Evaluating Metabolic Support in Pairwise Microbial Communities Using MetQuest.
Sengupta P, Vasudevan S, Raman K
Methods Mol Biol · 2026 · PMID 42156655 · Publisher ↗
This chapter delves into MetQuest, a computational tool that enumerates all possible reaction pathways that are feasible in genome-scale metabolic networks given a set of available metabolites. MetQuest accomplishes this...
This chapter delves into MetQuest, a computational tool that enumerates all possible reaction pathways that are feasible in genome-scale metabolic networks given a set of available metabolites. MetQuest accomplishes this by leveraging a guided breadth-first search framework, followed by dynamic programming. It enables us to perform interesting microbial community analyses, such as identifying metabolic exchanges between community members. Here, we specifically demonstrate how MetQuest can be employed to determine the Metabolic Support Index (MSI) for a pairwise microbial community. MSI is a metric that reflects an organism's benefit from the other member in co-culture, shedding light on the dynamics governing community interactions. Here, we present a pipeline to compute the MSI for two-membered communities from their genome-scale metabolic models. We illustrate this approach using the case of Acinetobacter baumannii and Klebsiella pneumoniae, two bacteria known to cross-feed metabolites. The MetQuest Python protocol used here is available from https://github.com/RamanLab/metquest/tree/master/MSI_Protocol .
Estimating Effective Pairwise Interactions to Predict the Structures of Microbial Communities (EPICS).
Sambamoorthy G, Ansari AF, Dixit NM
Methods Mol Biol · 2026 · PMID 42156654 · Publisher ↗
The engineering of multispecies microbial communities is important to applications in healthcare, biotechnology, and environmental sustainability. Predicting the structures of such communities requires knowledge of the i...
The engineering of multispecies microbial communities is important to applications in healthcare, biotechnology, and environmental sustainability. Predicting the structures of such communities requires knowledge of the interactions between the species involved. When high-order interactions are present, bottom-up approaches, which rely on the assembly of all possible subcommunities, become prohibitive because the number of such subcommunities scales exponentially with the number of species. Here, we present an alternative, top-down approach, EPICS, which requires the assembly of subcommunities whose number scales linearly with the number of species, hugely reducing experimental effort. EPICS estimates effective pairwise interactions between species, which subsume high-order interactions, using data from monocultures and leave-one-out subcommunities and predicts community structures. The method is efficient and scalable to large communities.
Identifying Differential Network Properties and Driver Microbes in Microbial Association Networks Using CompNet and NetShift.
Bhusan KK, Bose T, Dutta A
Methods Mol Biol · 2026 · PMID 42156653 · Publisher ↗
This chapter describes CompNet and NetShift, two network analysis tools, which can help in comparison and analysis of microbial association networks. Biological interaction networks are basic representations of entities...
This chapter describes CompNet and NetShift, two network analysis tools, which can help in comparison and analysis of microbial association networks. Biological interaction networks are basic representations of entities present in a biological system and their inter-relationships, which lies at the foundation of any modeling approaches. Inter-microbial associations define the community structure of a given microbiome and understanding these interaction networks are pivotal to modeling the microbiome. CompNet helps in the comparison of various network properties across multiple different microbial association networks that may represent communities inhabiting different/contrasting environments. NetShift helps in the identification of key microbes (driver organisms) in the network that can drive changes in community composition and interactions (network topology) characteristic of communities residing in different environments.
Computational Microbial and Viral Ecology Analysis.
Kosmopoulos JC, Anantharaman K
Methods Mol Biol · 2026 · PMID 42156652 · Publisher ↗
The explosion in known microbial diversity in the last two decades has made it abundantly clear that microbes in the environment do not exist in isolation; they are members of communities. Accordingly, omics approaches s...
The explosion in known microbial diversity in the last two decades has made it abundantly clear that microbes in the environment do not exist in isolation; they are members of communities. Accordingly, omics approaches such as metagenomics have revealed that interactions between diverse groups of community members such as archaea, bacteria, and viruses (bacteriophages) are common and have significant impacts on entire microbiomes. Thus, to have a well-developed understanding of microbes as they naturally exist in the environment, biological entities of all kinds must be studied together. While numerous protocols for metagenome analysis exist, comprehensive published protocols for the simultaneous analysis of viruses and prokaryotes together are scarce. Further, as bioinformatic methods for microbiology rapidly advance, existing metagenomic tools and pipelines require frequent re-evaluation. This ensures the adherence to best practices for microbiome and metagenomic data analysis. Here, we offer an expansive approach for the joint analysis of bulk sequence data from a mixed microbial community (metagenomes) and viral-sized fraction communities (viromes). This chapter serves as a beginner's-level guide for researchers with limited bioinformatics expertise who wish to engage in multiscale metagenome and virome analyses. We cover steps from initial study design to sequence read processing, metagenome assembly, quality control, virus identification, microbial and viral genome binning, taxonomic characterization, species-level clustering, and host-virus predictions. We also provide the bioinformatic scripts used in our workflow for reuse in one's own computational methods. Lastly, we discuss additional approaches a researcher can take after processing data with this workflow.
Dilution-to-Stimulation: A Method for Selecting Polymer-Transforming Microbial Consortia.
Jiménez DJ, Díaz-García L, Aldakheel L … +1 more , Rosado AS
Methods Mol Biol · 2026 · PMID 42156651 · Publisher ↗
The utilization of liquid enrichment cultures for selecting microbial communities has been employed to recover and increase the abundance of desired microbes capable of thriving on specific carbon sources. This strategy...
The utilization of liquid enrichment cultures for selecting microbial communities has been employed to recover and increase the abundance of desired microbes capable of thriving on specific carbon sources. This strategy facilitates the development of microbial consortia designed to serve as models for studying the eco-enzymology of particular metabolic processes. In this context, we introduce a top-down method referred to as "dilution-to-stimulation" to artificially select polymer-transforming microbial consortia from soil samples.
Shotgun Metagenomic Analysis of Microbial Community Dynamics in Wastewater Treatment Through Constructed Wetlands.
Miliotis G, Tumeo A
Methods Mol Biol · 2026 · PMID 42156650 · Publisher ↗
Constructed wetlands (CWs) offer a sustainable, nature-based solution to wastewater treatment, supporting diverse and dynamic microbial communities that drive nutrient cycling, pollutant degradation, and pathogen removal...
Constructed wetlands (CWs) offer a sustainable, nature-based solution to wastewater treatment, supporting diverse and dynamic microbial communities that drive nutrient cycling, pollutant degradation, and pathogen removal. This chapter presents an end-to-end methodology for performing shotgun metagenomic analyses on microbial populations from CW influent and effluent. We detail approaches for site selection, sample collection, filtration, DNA extraction, and the incorporation of positive and negative controls to ensure reproducibility and data quality. Two modular bioinformatic workflows encompassing quality control, assembly, taxonomic/functional annotation, and metagenome-assembled genome recovery are described alongside options for detecting antimicrobial resistance genes, pathogens, toxins, and plasmids. In addition, an example workflow for the calculation of alpha and beta diversity is provided. Guidelines for data standardization, replication, and compliance with community-driven reporting standards (MIMS, MIMAG) are also included. Incorporating this protocol will facilitate standardized, reproducible insights into CW microbial dynamics, thereby informing ecological understanding and guiding practical interventions that enhance wastewater treatment efficacy and improve public health outcomes.
Exploring the Ocean's Microbial World: Techniques and Protocols for Microbiome Research.
Rangamaran VR, Sushmitha TJ, Tamilmani KK … +2 more , Murugesan H, Gopal D
Methods Mol Biol · 2026 · PMID 42156649 · Publisher ↗
Marine microbiomes play a crucial role in oceanic ecosystems, influencing biogeochemical cycles, climate regulation, and marine biodiversity. Accurate characterization of these microbial communities requires standardized...
Marine microbiomes play a crucial role in oceanic ecosystems, influencing biogeochemical cycles, climate regulation, and marine biodiversity. Accurate characterization of these microbial communities requires standardized protocols for sample collection, processing, sequencing and data analysis. This chapter provides a comprehensive guide to essential methodologies for marine microbiome research including field sampling strategies, DNA and RNA extraction techniques, high-throughput sequencing approaches (such as 16S rRNA amplicon sequencing and metagenomics) and bioinformatics pipelines for data interpretation. Additionally, we discuss quality control measures, best practices for reproducibility, and challenges associated with marine microbiome profiling. By adopting standardized methodologies, researchers can generate reliable, comparable datasets that enhance our understanding of marine microbial ecology and its broader environmental implications.
Targeted Metagenomics Using Next-Generation Sequencing Methods.
Yugandhar Reddy BS, Sripradha S, Kumar A
Methods Mol Biol · 2026 · PMID 42156648 · Publisher ↗
Metagenomics allows the discovery of the full diversity of all microbes present in a given niche. The technique is very powerful and has allowed very significant advances delineating the role of the microbiome in several...
Metagenomics allows the discovery of the full diversity of all microbes present in a given niche. The technique is very powerful and has allowed very significant advances delineating the role of the microbiome in several disciplines including health, agriculture, ecology, industry, etc. Here, we describe the method required for processing of samples for metagenomic analysis using Next-Gen sequencing.
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