Jan 2026· TheScientificWorldJournal· Vol 2026· 0 citations· 116 references
Medicine
TL;DR
This work provides a critical evaluation of the functional gaps between genomic potential and in situ microbial activity and offers a novel synthesis of how multiomics integration and predictive modeling can move beyond species cataloging toward a more robust, evidence‐based framework for environmental sustainability.
Abstract
Environmental biotechnology increasingly relies on bioinformatics to address global challenges in pollution control or degradation, biodiversity conservation, and sustainable resource management. By integrating genomics, computational tools, and artificial intelligence, bioinformatics enables the analysis of complex biological datasets, such as metagenomes and environmental DNA (deoxyribonucleic acid), to uncover microbial diversity, pollutant degradation pathways, and ecological resilience. High‐throughput sequencing technologies and multiomics integration provide novel insights into microbial communities and their functional roles in bioremediation and ecosystem monitoring. Predictive modeling further enhances our ability to simulate microbial behavior in contaminated environments and assess the long‐term impacts of biotechnological interventions. Despite increased progress, challenges remain in managing large‐scale data, fostering interdisciplinary collaboration, and developing user‐friendly bioinformatics platforms. Future directions emphasize the application of machine learning, sustainable resource management, and collaborative frameworks to bridge bioinformatics and environmental sciences. Unlike traditional descriptive reviews, this work provides a critical evaluation of the functional gaps between genomic potential and in situ microbial activity. It offers a novel synthesis of how multiomics integration and predictive modeling can move beyond species cataloging toward a more robust, evidence‐based framework for environmental sustainability.
Agricultural systems are increasingly challenged by climate change, resource scarcity, environmental degradation, and the need to ensure food security for a growing global population. Addressing these multifaceted challenges requires innovative, science-driven approaches that enhance productivity while promoting ecological sustainability. This editorial examines the evolving role of plant biotechnology as a key driver of modern agricultural advancement, highlighting recent developments in genomics, multi-omics technologies, molecular breeding, genome editing, plant tissue culture, and digital agriculture. It discusses how these technologies contribute to the development of resilient crop varieties with improved tolerance to biotic and abiotic stresses, enhanced nutrient-use efficiency, and superior agronomic performance. The editorial further emphasizes the integration of artificial intelligence, high-throughput phenotyping, bioinformatics, and systems biology into precision crop improvement, enabling more efficient translation of molecular discoveries into field applications. The importance of interdisciplinary collaboration, responsible innovation, biosafety, and science-based regulatory frameworks is also considered in supporting the sustainable deployment of emerging biotechnologies. Looking ahead, the convergence of advanced molecular tools with computational and ecological sciences is expected to accelerate the development of resilient agricultural systems capable of addressing future environmental and food production challenges. Continued investment in research, technological innovation, and international collaboration will be essential to maximize the societal and environmental benefits of next-generation crop improvement strategies.
Md. Mosharraf Hossen· Journal of Agriculture and F...· 0 citations
Activities from several Industrial, agricultural, and urban settings have led to alarming environmental pollution, with about 2.3 billion tonnes of chemicals being produced annually. In this review, we discuss the use of microbial degradation as an eco-friendly and cost-effective option for the cleanup of the environment. We examine the core mechanisms of pollutant degradation, featuring principal microbes such as bacteria (Bacillus spp, Pseudomonas spp, Rhodococcus spp, Alcanivorax spp), fungi (Phanerochaete spp, Chrysosporium spp.), and their enzyme repertoire (oxygenases, dehalogenases, reductases). This review also considers how multi-omics technologies (proteomics, metagenomics, transcriptomics, and metabolomics) have led to a better understanding of microbial consortia interactions in polluted environments by allowing culture-free approaches. We also discuss the roles of biotechnological innovations such as CRISPR-based environmental engineering, synthetic biology, cell-free systems, engineered microbial consortia and artificial intelligence-driven predictive modelling in addressing the issues facing natural attenuation. However, in spite of the significant achievement that have been made using several clean up procedures to prevent or minimize environmental pollution there are still some challenges such as the effect of environmental complexity, microbial competition, and regulations for the use of genetically engineered organisms. This review discuss past prior and ongoing knowledge about microbial biodegradation using peer-reviewed literature from 2012–2026 to provide a framework for converting laboratory discoveries into field-level deployments to enable precision bioremediation approach for polluted ecosystem rehabilitation and public health safety.
Asenuga Ebunoluwa Racheal, Osemudiamen Anao Edene· Dutse Journal of Pure and Ap...· 0 citations
Conventional culture-dependent microbiological methods cannot characterize the vast majority of microbial diversity encountered in complex environmental and clinical samples. Metagenomics, the culture-independent, sequencing-based analysis of all genetic material recovered directly from a microbial community, has emerged as a transformative discipline that bridges this fundamental gap. By enabling simultaneous profiling of thousands of microbial genomes within a single assay, metagenomics provides unprecedented insights into microbial diversity, community structure, functional potential, and evolutionary relationships across diverse ecological niches. This narrative review discusses the conceptual foundations of metagenomics, its major methodological workflows, including total DNA extraction, metagenomic library construction, and screening strategies, and the evolution of sequencing platforms from first-generation Sanger sequencing through next-generation sequencing (NGS) to current long-read third-generation platforms such as Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio). The growing integration of artificial intelligence (AI) and machine learning (ML) into metagenomic bioinformatics pipelines is examined. Applications across healthcare, environmental science, agriculture, biotechnology, and industry are reviewed with particular emphasis on clinical metagenomic next-generation sequencing (mNGS) for infectious disease diagnostics, antimicrobial resistance (AMR) surveillance, gut microbiome research, and precision medicine. Key technical and translational challenges, including host DNA contamination, bioinformatic complexity, database incompleteness, absence of clinical standardization, and ethical considerations, are critically evaluated. The review concludes by identifying priority areas for future research and translational implementation.
Ahmed Alsharksi· Razi Medical Journal· 0 citations
Sustainable crop development aims to maintain or increase yields while reducing environmental impact and managing the challenges imposed by climate change. As the global population grows and arable land becomes scarcer, the integration of molecular breeding with bioinformatics has emerged as an effective strategy for long-term crop improvement. Bioinformatics enables researchers to analyze and interpret the vast quantities of genetic data generated by high-throughput sequencing, making it possible to identify molecular markers, candidate genes, and regulatory networks linked to specific agronomic traits, which breeders then translate into focused, ecologically sustainable breeding programs. This approach has enabled major progress across several fronts: the identification of genes conferring resistance to biotic stressors (pests, pathogens) and abiotic stressors (drought, salinity, heat); the development of nutrient-efficient, low-input crop varieties; the improvement of agronomic performance and nutritional quality through identification of yield- and quality-related genes; and the conservation and deployment of genetic diversity to safeguard long-term breeding sustainability. By combining genomic data with precision breeding techniques, researchers are developing crops that are better adapted to a growing population and a changing climate, positioning the integration of molecular breeding and bioinformatics as a central pillar of future global food security.
Muhammad Shahid Iqbal, Z. Sarfraz, Muhammad Mujahid et al.· Frontiers in Plant Science· 0 citations
Grasslands are among the largest terrestrial biomes and play essential roles in livestock production, carbon sequestration and global food security. The productivity and resilience of these ecosystems are driven by complex molecular interactions between plants and their associated microbiomes. Although recent advances in nucleic acid research and multi-omics approaches have provided new insights into these interactions, the molecular mechanisms underpinning plant-microbiome interactions in these ecosystems remain insufficiently explored. This review synthesizes the latest progress in nucleic-acid and multi-omics approaches to better understand plant-microbiome interactions. It integrates nucleic acid-based technologies with multi-omics frameworks to explain plant-microbiome interactions across molecular, ecological, and management scales. By linking microbial community structure, functional genes, gene expression, metabolite profiles, ecosystem multifunctionality and sustainable grassland management, this review provides a broader framework for translating molecular insights into practical strategies for grassland resilience, productivity, and food security. Advances in amplicon sequencing, shotgun and long-read metagenomics, environmental DNA (eDNA) monitoring, plant and microbiome genome-wide association studies (GWAS) and transcriptomics have provided valuable insights into plant-microbiome interaction. This review highlights how these techniques enable functional and mechanistic understanding by linking microbial diversity with gene expression, nutrient cycling and plant performance. Additionally, long-read sequencing technologies provide genome-resolved analysis, improving the detection of structural and epigenetic variations, which are essential for understanding these interactions. These approaches reveal the role of beneficial microbes in enhancing grassland fertility, ultimately improving grassland productivity. Integrating these findings with metabolomics and phenomics offers a novel approach for predictive modeling in sustainable grassland management. The review concludes by emphasizing the need for standardized protocols, longitudinal field studies and experimental validation through synthetic communities and genome editing to harness plant-microbiome interactions for enhanced productivity and food security.
Abdul Majeed, Muhammad Haseeb Javaid, Naima Mahreen et al.· International Journal of Bio...· 0 citations
Marine fishes are important contributors to biodiversity conservation and socioeconomic sustainability. While genomic and multi-omics datasets for marine fish species have expanded exponentially in recent years, their systematic integration remains underexplored, hindering comprehensive investigations into gene regulation and biological systems. To bridge this gap, we developed the Marine Fish Database (MFDB; http://marinefishdb.cn), the first integrative genomics platform specifically designed for marine teleosts. MFDB compiles fragmented multi-omics resources across 89 species, encompassing genome assemblies, phylogenomic reconstructions, collinearity maps, pan gene sets, gene architectures, functional annotations, expression profiles, and evolutionary gene family analyses. MFDB further delivers specialized analytical modules, including tissue-specific gene co-expression networks, lineage-defining core gene repertoires, and macrosynteny-driven chromosomal evolution models. The platform features an intuitive and programmable interface, enabling multiscale queries, cross-omics data mining, and dynamic visualization of multidimensional biological interactions. These functionalities collectively empower users to decipher how genomic elements orchestrate phenotypic outcomes through multi-layered regulatory cascades. By integrating rapidly expanding omics datasets with robust analytical pipelines, MFDB establishes a scalable framework to accelerate hypothesis-driven discoveries in marine fish biology, evolutionary adaptation, and ecological resilience research, and further provides an easy-to-operate information platform for the molecular breeding of marine fish species.
Chengbin Gao, Ming Li, Sheng Lu et al.· Nucleic Acids Research· 0 citations