Metagenomic and Computational Perspectives on the Rumen Microbiome: A Mini Review
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Authors
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Dr. Vishakha Uttam
ICAR- National Bureau of Animal Genetic Resources, Karnal, Haryana
Author
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Anil Kumar Mishra
ICAR-National Bureau of Animal Genetic Resources, Karnal - 132001, Haryana
Author
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Raja Kolandanoor Nachiappan
ICAR-National Bureau of Animal Genetic Resources, Karnal - 132001, Haryana
Author
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Aditya D. Deshpande
ICAR-Central Institute of Fisheries Education, Mumbai
Author
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Sayed Nabil Abedin
ICAR-Indian Veterinary Research Institute, Mukteswar
Author
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Sonam Dwivedi
ICAR-Central Institute for Research on Cattle, Meerut
Author
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Garima Chaudhary
ICAR-Central Sheep & Wool Research Institute, Avikanagar
Author
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Harinder Deep Singh
ICAR-National Bureau of Animal Genetic Resources, Karnal - 132001, Haryana
Author
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Prasoon Nayak
ICAR-National Bureau of Animal Genetic Resources, Karnal - 132001, Haryana
Author
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Tamanna
ICAR-National Bureau of Animal Genetic Resources, Karnal - 132001, Haryana
Author
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Rafiul
ICAR-National Bureau of Animal Genetic Resources, Karnal - 132001, Haryana
Author
Abstract
The rumen microbiome comprises a highly varied and functionally complex community of microorganisms that plays a pivotal role in feed digestion, nutrient metabolism, animal productivity, and environmental sustainability. Conventional culture-based approaches have provided valuable understandings into rumen microbial diversity but remain limited by the incapability to cultivate the majority of rumen microorganisms. Advances in metagenomic sequencing have transformed rumen microbiome research by allowing culture-independent characterization of microbial communities, revealing wide taxonomic diversity, functional genes, metabolic pathways, and novel microbial resources with significant biotechnological and agricultural potential. Parallel developments in computational metagenomics, including progressive bioinformatics pipelines, long-read sequencing technologies, artificial intelligence (AI), and machine learning (ML), have considerably improved genome reconstruction, functional annotation, and the prediction of microbial functions from gradually complex sequencing datasets. These methods have prolonged applications in enzyme discovery, identification of fiber-degrading microorganisms, feed efficiency, methane mitigation, and microbiome-informed livestock management. Despite these advances, challenges such as large-scale data processing, incomplete reference databases, computational complexity, and the lack of standardized analytical workflows continue to limit the full exploitation of metagenomic data. This review summarizes recent advances in metagenomic and computational approaches for rumen microbiome research, highlighting sequencing technologies, bioinformatics tools, AI and ML based analytical methods, major applications, current challenges, and future perspectives. Continued combination of genome-centric metagenomics with multi-omics, AI, and advanced computational frameworks is expected to accelerate microbial discovery and support sustainable, climate-resilient, and precision ruminant production.
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References
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