Cited by 600Genomics and Phylogenetic StudiesRuminant Nutrition and Digestive PhysiologyAnaerobic Digestion and Biogas Production

The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.

Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen

Open access

Stewart, Robert D., Auffret, Marc D., Warr, Amanda, Wiser, Andrew H., Press, Maximilian O., Langford, Kyle W., Liachko, Ivan, Snelling, Timothy J., Dewhurst, Richard J., Walker, Alan W., Roehe, Rainer, Watson, Mick·2018·Nature Communications

DOI 10.1038/s41467-018-03317-6PMC PMC5830445

Open this paper in CoChat — free account Open PDF

Abstract

The cow rumen is adapted for the breakdown of plant material into energy and nutrients, a task largely performed by enzymes encoded by the rumen microbiome. Here we present 913 draft bacterial and archaeal genomes assembled from over 800 Gb of rumen metagenomic sequence data derived from 43 Scottish cattle, using both metagenomic binning and Hi-C-based proximity-guided assembly. Most of these genomes represent previously unsequenced strains and species. The draft genomes contain over 69,000 proteins predicted to be involved in carbohydrate metabolism, over 90% of which do not have a good match in public databases. Inclusion of the 913 genomes presented here improves metagenomic read classification by sevenfold against our own data, and by fivefold against other publicly available rumen datasets. Thus, our dataset substantially improves the coverage of rumen microbial genomes in the public databases and represents a valuable resource for biomass-degrading enzyme discovery and studies of the rumen microbiome.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/s41467-018-03317-6

Cites

83 of 83 references verified0 citation errors · 0 not checkable

Statistics

Fig. 1 Phylogenetic tree of 913 draft genomes from the cow rumen, and closely related public genomes including 15 binned genomes from Hess et al1. Coloured circles represent the RUGs. White circles represent public genomes and have corresponding labelsCC BY · PubMed Central

Fig. 2 Distribution of carbohydrate-active enzyme classes across the 913 RUGs. GH glycoside hydrolase, GT glycosyl transferase, PL polysaccharide lyases, CE carbohydrate esterases, AA auxiliary activities, CB carbohydrate binding. White = absent, dark red = abundantCC BY · PubMed Central

Fig. 3 Distribution of the maximum percentage identity of the RUG proteins against five public databases for six classes of carbohydrate-active enzymes. GH glycoside hydrolase, GT glycosyl transferase, PL polysaccharide lyases, CE carbohydrate esterases, AA auxiliary activities, CB carbohydrate bindingCC BY · PubMed Central

Fig. 4 Classification rate for three datasets against various Kraken databases. BFAP bacterial, archaeal, fungal and protozoan genomes from RefSeq, BGEB BFAP + 1003 GEBA genomes, BHIC BFAP + 63 hRUG genomes, BHUN BFAP + 410 genomes from the Hungate 1000 project, BRUG BFAP + 850 RUG MAGs, BRHI BFAP + all 913 genomes from this study, BRHH BFAP + 913 RUGs + 410 Hungate 1000 genomes. Addition of rumen-specific RUGs or Hungate 1000 genomes has the most dramatic effectCC BY · PubMed Central

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
BLAST+: architecture and applications
Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-421 · Cited by 22,074
Fast and sensitive protein alignment using DIAMOND
Buchfink, B., Xie, C., & Huson, D. H. (2015) · Nature Methods · doi:10.1038/nmeth.3176 · Cited by 14,201
Aligning sequence reads, clone sequences and assembly contigs with BWA-MEMPreprint
Li, H. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1303.3997 · Cited by 10,846
MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph
Li, D., Liu, C.-M., Luo, R., Sadakane, K., & Lam, T. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv033 · Cited by 10,133
UniProt: the Universal Protein knowledgebase
Apweiler, R. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh131 · Cited by 7,593
The carbohydrate-active enzymes database (CAZy) in 2013
Lombard, V., Ramulu, H. G., Drula, É., Coutinho, P. M., & Henrissat, B. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1178 · Cited by 6,452
Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation
O'Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1189 · Cited by 6,357

Show all 83 references

Sorted by citations

Items 501–600 of 600

Additional file 19 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610707
Additional file 2 of Metagenomic insights into the microbe-mediated B and K2 vitamin biosynthesis in the gastrointestinal microbiome of ruminants
Jiang, Q., Lin, L., Xie, F., Jin, W., Zhu, W., Wang, M., Qiu, Q., Li, Z., Liu, J., & Mao, S. (2022) · Figshare · doi:10.6084/m9.figshare.20356416
Additional file 3 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610656
Additional file 20 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610710
Additional file 12 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610683
Additional file 5 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610662
Additional file 2 of High-quality bacterial genomes of a partial-nitritation/anammox system by an iterative hybrid assembly method
Liu, L., Wang, Y., Che, Y., Chen, Y., Xia, Y., Luo, R., Cheng, S. H., Zheng, C., & Zhang, T. (2020) · Figshare · doi:10.6084/m9.figshare.13203415
Additional file 8 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610671
Additional file 6 of An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants
Xie, F., Jin, W., Si, H., Yuan, Y., Tao, Y., Liu, J., Wang, X., Yang, C., Li, Q., Yan, X., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14775438
Additional file 17 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610698
Additional file 4 of Metagenomic insights into the microbe-mediated B and K2 vitamin biosynthesis in the gastrointestinal microbiome of ruminants
Jiang, Q., Lin, L., Xie, F., Jin, W., Zhu, W., Wang, M., Qiu, Q., Li, Z., Liu, J., & Mao, S. (2022) · Figshare · doi:10.6084/m9.figshare.20356422
Biochemical characterization of a SusD-like protein involved in β-1,3-glucan utilization by an uncultured cow rumen Bacteroides
Li, X., Lippens, G. M., Parrou, J., Cioci, G., Esque, J., Wang, Z., Laville, É., Potocki-Véronèse, G., & Labourel, A. (2024) · mSphere · doi:10.1128/msphere.00278-24 · Cited by 1
“Because bacteria are active players in plant biomass degradation, many recent stud ies have investigated their carbohydrate-active enzyme (CAZyme) content (9, 14–18).”
Multi-omics illuminates the functional significance of previously unknown species in a full-scale landfill leachate treatment plant
Chen, T., Deng, C., Li, S., Li, B. J., Liang, Y., Zhang, Y., Li, J., Xu, N., & Yu, K. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.135669
Application of Amplicon Metagenomics to Identify Fungal Pathogens in Formalin-Fixed Paraffin-Embedded Samples: Proof of Concept in Animals with Fungal Pathologies
Needle, D. B., Reboul, G., Mitchell, P. K., Rothenheber, D., Marra, N. J., Cronk, B. D., Patel, N. G., & Goodman, L. B. (2025) · Microorganisms · doi:10.3390/microorganisms13030533
“The metagenomics and bioinformatics pipelines applied to fresh or frozen samples have been widely demonstrated to powerfully identify a broad array of viruses, bacteria, fungi, and protozoa, including evidence of 913 distinct genomes in one sample of cow rumen [21].”
Pipelines and Databases—Microbiome Analysis
Sanders, J., Huang, L., Zhu, Q., & Morton, J. T. (2025) · Wiley eBooks · doi:10.1002/9781683674597.ch22
Deciphering the microbial landscape of lower respiratory tract in health and respiratory diseasesPreprint
Cheng, C., Li, Y., Wang, S., Wang, H., Liu, D., Wang, Q., Che, Y., Xue, L., Chao, N., He, X., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.11.659233
“It was reported that co-assembly is an effective approach for reconstructing low-abundance MAGs [33].”
Unraveling the role of bacteria with heritable versus non-heritable relative abundance in the gut on boar semen quality
Guo, L., Pei, X., Tan, J., Sun, H., Jiang, S., Wei, H., & Peng, J. (2025) · Genetics Selection Evolution · doi:10.1186/s12711-025-00990-2
“This may be due to the fact that Firmicutes bacteria themselves make up a much higher proportion of the porcine intestinal tract than Bacteroidetes, in addition to the fact that Bacteroidetes bacteria usually feed on carbohydrates and are, therefore, susceptible to environmental factors [36].”
Decomposition potential and population structure among active microbial communities in deep salt marsh sedimentsPreprint
Vineis, J. H., Bulseco, A., Rafie, S., Cardon, Z. G., & Bowen, J. L. (2025) · eLife · doi:10.7554/elife.108843
Comparative pangenome analysis of methanogenic archaea from diverse ecosystems reveals potential targets for methane mitigation in rumen microbiome
Park, J., Jang, K. B., Kanga, M.-G., Kyung, J., Yoon, J., Ryu, S., & Kim, Y. (2025) · Journal of Animal Science and Technology · doi:10.5187/jast.2500412
“Advancing high-quality genome references and refining functional annotations will connect genotype to mechanism in rumen systems, supporting the design of targeted, microbiome-informed strategies to reduce methane emissions in livestock production [8].”
Additional file 1 of Microbial dark matter filling the niche in hypersaline microbial mats
Wong, H. L., MacLeod, F. I., White, R. A., III, Visscher, P. T., & Burns, B. P. (2020) · Figshare · doi:10.6084/m9.figshare.12965735
Additional file 3 of Altitude influences microbial diversity and herbage fermentation in the rumen of yaks
Fan, Q., Wanapat, M., Yan, T., & Hou, F. (2020) · Figshare · doi:10.6084/m9.figshare.13336453
Additional file 6 of Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits
Yan, M., & Yu, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26718120
Additional file 2 of Characterization of metagenome-assembled genomes from the International Space Station
Singh, N. K., Wood, J. M., Patane, J., Moura, L. M. S., Lombardino, J., Setubal, J. C., & Venkateswaran, K. (2024) · Figshare · doi:10.6084/m9.figshare.26593271
Additional file 12 of Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits
Yan, M., & Yu, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26718138
Additional file 1 of Characterization of efficient xylanases from industrial-scale pulp and paper wastewater treatment microbiota
Wang, J., Liang, J., Li, Y., Tian, L., & Wei, Y. (2021) · Figshare · doi:10.6084/m9.figshare.13612424
Additional file 1 of PandaGUT provides new insights into bacterial diversity, function, and resistome landscapes with implications for conservation
Huang, G., Shi, W., Wang, L., Qu, Q., Zuo, Z., Wang, J., Zhao, F., & Wei, F. (2023) · Figshare · doi:10.6084/m9.figshare.24266048
Additional file 1 of The unique gut microbiome of giant pandas involved in protein metabolism contributes to the host’s dietary adaption to bamboo
DENG, F., Wang, C., Li, D., Peng, Y., Deng, L., Zhao, Y., Zhang, Z., Wei, M., Wu, K., Zhao, J., et al. (2023) · Figshare · doi:10.6084/m9.figshare.23949802
Additional file 8 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694066
Additional file 15 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610692
Additional file 21 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610713
Additional file 2 of Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits
Yan, M., & Yu, Z. (2024) · Figshare · doi:10.6084/m9.figshare.25792838
Additional file 2 of Holistic view of the seascape dynamics and environment impact on macro-scale genetic connectivity of marine plankton populations
Laso‐Jadart, R., O’Malley, M., Sykulski, A. M., Ambroise, C., & Madoui, M. (2023) · Figshare · doi:10.6084/m9.figshare.24076958
Additional file 11 of Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits
Yan, M., & Yu, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26718135
Additional file 5 of Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits
Yan, M., & Yu, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26718117
Additional file 7 of Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits
Yan, M., & Yu, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26718123
Additional file 13 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610686
Additional file 1 of Metagenomic insights into the microbe-mediated B and K2 vitamin biosynthesis in the gastrointestinal microbiome of ruminants
Jiang, Q., Lin, L., Xie, F., Jin, W., Zhu, W., Wang, M., Qiu, Q., Li, Z., Liu, J., & Mao, S. (2022) · Figshare · doi:10.6084/m9.figshare.20356413
Additional file 6 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694060
Additional file 4 of Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits
Yan, M., & Yu, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26718114
Additional file 3 of Quantifying fluorescent glycan uptake to elucidate strain-level variability in foraging behaviors of rumen bacteria
Klassen, L., Reintjes, G., Tingley, J. P., Jones, D. R., Hehemann, J., Smith, A. D., Schwinghamer, T. D., Arnosti, C., Jin, L., Alexander, T. W., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13632746
Additional file 9 of Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits
Yan, M., & Yu, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26718129
Additional file 3 of Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits
Yan, M., & Yu, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26718111
Additional file 5 of Altitude influences microbial diversity and herbage fermentation in the rumen of yaks
Fan, Q., Wanapat, M., Yan, T., & Hou, F. (2020) · Figshare · doi:10.6084/m9.figshare.13336465
Additional file 1 of Nutrient availability of roughages in isocaloric and isonitrogenous diets alters the bacterial networks in the whole gastrointestinal tract of Hu sheep
Li, Y., Gao, J., Xue, Y., Sun, R., Sun, X., Sun, Z., Liu, S., Zhankun, T., Zhu, W., & Cheng, Y. (2024) · Figshare · doi:10.6084/m9.figshare.26575388
Additional file 2 of Quantifying fluorescent glycan uptake to elucidate strain-level variability in foraging behaviors of rumen bacteria
Klassen, L., Reintjes, G., Tingley, J. P., Jones, D. R., Hehemann, J., Smith, A. D., Schwinghamer, T. D., Arnosti, C., Jin, L., Alexander, T. W., et al. (2021) · Figshare · doi:10.6084/m9.figshare.13632743
Additional file 1 of High-quality bacterial genomes of a partial-nitritation/anammox system by an iterative hybrid assembly method
Liu, L., Wang, Y., Che, Y., Chen, Y., Xia, Y., Luo, R., Cheng, S. H., Zheng, C., & Zhang, T. (2020) · Figshare · doi:10.6084/m9.figshare.13203412
Additional file 4 of Altitude influences microbial diversity and herbage fermentation in the rumen of yaks
Fan, Q., Wanapat, M., Yan, T., & Hou, F. (2020) · Figshare · doi:10.6084/m9.figshare.13336459
Additional file 10 of Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits
Yan, M., & Yu, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26718132
Additional file 1 of Characterization of metagenome-assembled genomes from the International Space Station
Singh, N. K., Wood, J. M., Patané, J. S. L., Moura, L. M. S., Lombardino, J., Setubal, J. C., & Venkateswaran, K. (2024) · Figshare · doi:10.6084/m9.figshare.26593268
Additional file 1 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25422374
Additional file 2 of PandaGUT provides new insights into bacterial diversity, function, and resistome landscapes with implications for conservation
Huang, G., Shi, W., Wang, L., Qu, Q., Zuo, Z., Wang, J., Zhao, F., & Wei, F. (2024) · Figshare · doi:10.6084/m9.figshare.24266072
Additional file 1 of Effect of castration timing and weaning strategy on the taxonomic and functional profile of ruminal bacteria and archaea of beef calves
Diaz, G. R., Gaire, T. N., Ferm, P., Case, L., Caixeta, L. S., Goldsmith, T., Armstrong, J., & Noyes, N. R. (2023) · Figshare · doi:10.6084/m9.figshare.24715490
Additional file 5 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694057
Additional file 4 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610659
Additional file 8 of Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits
Yan, M., & Yu, Z. (2024) · Figshare · doi:10.6084/m9.figshare.26718126
Additional file 4 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694054
Additional file 1 of Altitude influences microbial diversity and herbage fermentation in the rumen of yaks
Fan, Q., Wanapat, M., Yan, T., & Hou, F. (2020) · Figshare · doi:10.6084/m9.figshare.13336441
Additional file 2 of Altitude influences microbial diversity and herbage fermentation in the rumen of yaks
Fan, Q., Wanapat, M., Yan, T., & Hou, F. (2020) · Figshare · doi:10.6084/m9.figshare.13336447
Additional file 2 of An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants
Xie, F., Jin, W., Si, H., Yuan, Y., Tao, Y., Liu, J., Wang, X., Yang, C., Li, Q., Yan, X., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14775420
Additional file 1 of Viruses contribute to microbial diversification in the rumen ecosystem and are associated with certain animal production traits
Yan, M., & Yu, Z. (2024) · Figshare · doi:10.6084/m9.figshare.25792734
Additional file 6 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610665
Additional file 7 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694063
Additional file 1 of Microbiome-driven breeding strategy potentially improves beef fatty acid profile benefiting human health and reduces methane emissions
Martínez-Álvaro, M., Mattock, J., Auffret, M. D., Weng, Z., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., & Roehe, R. (2022) · Figshare · doi:10.6084/m9.figshare.21285704
Additional file 1 of Exploring the microbial diversity and characterization of cellulase and hemicellulase genes in goat rumen: a metagenomic approach
Thapa, S., Zhou, S., O’Hair, J., Al Nasr, K., Ropelewski, A. J., & Li, H. (2023) · Figshare · doi:10.6084/m9.figshare.24741841
Additional file 24 of Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse
Li, C., Li, X., Guo, R., Ni, W., Liu, K., Liu, Z., Dai, J., Xu, Y., Abduriyim, S., Wu, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22610725
Additional file 3 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694051
Additional file 2 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25422422
Additional file 3 of PandaGUT provides new insights into bacterial diversity, function, and resistome landscapes with implications for conservation
Huang, G., Shi, W., Wang, L., Qu, Q., Zuo, Z., Wang, J., Zhao, F., & Wei, F. (2024) · Figshare · doi:10.6084/m9.figshare.26624359
A molecular inventory of the faecal microbiomes of 23 marsupial species
Bowerman, K. L., Soo, R. M., Chaumeil, P.-A., Blyton, M. D. J., Sørensen, M., Gunbilig, D., Malig, M., Islam, M., Zaugg, J., Wood, D., et al. (2026) · Microbial Genomics · doi:10.1099/mgen.0.001601
“2.9) [48] and binning performed using the ProxiMeta platform [49, 50], incorporating both standard shotgun and Hi-C data.”
Influential
Effects of Glycerol Fatty Acid Esters on Growth Performance, Methane Emissions, and Rumen Microbial Flora of Dabieshan Beef Cattle
Nie, J., Li, X., Luo, Y., Li, H., Zhu, Y., Chen, C., & Hua, J. (2026) · Veterinary Sciences · doi:10.3390/vetsci13010092
“…sequencing using either the Illumina HiSeq or MiSeq platform, with the platform selection determined by the fragment size, all sample handling and sequencing procedures were performed in strict accordance with the detailed operational protocols outlined in Supplementary Method S1 [6,30].”
MetaMAG Explorer: A Database-Augmenting Pipeline for Genome-Resolved Metagenomics and Enhanced Microbial ClassificationPreprint
Satti, M. A., & Cai, Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.27.721001
“Accordingly, a significant proportion of metagenomic data, sometimes over 50%, remains unclassifiable [6, 7], particularly in environments such as the bovine rumen [5], plant rhizosphere [8], or non-Western gut microbiota [2, 3].”
A Cophylogenetic Approach for Virus-Host Interaction PredictionPreprint
Chowdhury, M. Z. U. S., Murali, T. M., & Sashittal, P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.26.708038
“The benchmark comprises of 251 virus-host interactions collected from three distinct environments: 84 from human gut samples [67], 66 from cow fecal samples [68] and 101 from wastewater samples [69].”
Neurosarcoidosis
Terpigorev, S. A., Bogdanov, R. R., & Dimertsev, A. V. (2026) · Clinical Medicine (Russian Journal) · doi:10.30629/0023-2149-2025-103-12-865-872
Metabolic capacities and potential microbial interactions in red and green snow of the Antarctic Peninsula
Chen, X., Ji, M., Yan, D., Liu, Y., Chen, Y., Dong, R., Shen, L., Takeuchi, N., & Kong, W. (2026) · New Phytologist · doi:10.1111/nph.71089
“2a), indicating widespread genetic capacity for both the synthesis and degradation of carbohydrates (Ortiz-Soto & Sei-bel, 2025), consistent with the diverse carbon catabolic and metabolic potentials reported in snow microbiomes (Lairson et al ., 2008; Stewart et al ., 2018).”
Analysis of the characteristics of rumen microorganisms and their metabolites and plasma metabolites in crossbred beef cattle at different stages
Tian, X., Feng, Y., Wang, C., Zhao, W., Xue, L., Zhu, L., Ji, X., Wang, H., Gu, Y., Jiang, Q., et al. (2026) · Veterinary Research Communications · doi:10.1007/s11259-026-11151-1
An overview of the livestock microbiome: sheep, horses, cattle, camels, and chickens
Ibadullayeva, A., Хамзина, А., Бакытбекович, С. Д., & Хамзин, К. (2026) · Brazilian Journal of Biology · doi:10.1590/1519-6984.299936
Investigating chromosome dynamics through Hi-C assembly
Baudry, L. (2019) · theses.fr (ABES) · doi:10.70675/a91a798czcccbz4ccfzb7f8zc2f5d6e00d20
Caractérisation de l'holobionte des isopodes par des approches omiques : exemple du processus de dégradation de la lignocellulose
Bredon, M. (2019) · doi:10.70675/fe7a7f41z710bz471cz9b7fz8337110de97e
Genome-wide effects of T4 phage Ndd protein expression on the Escherichia coli nucleoid
Foutel-Rodier, T. (2021) · theses.fr (ABES) · doi:10.70675/a5e4bd2az13bez477cz92aez8c90736cad60
Étude du rôle fonctionnel du microbiote intestinal dans l'adaptation à un régime sous optimal et dans l'efficience alimentaire de la poule pondeuse en utilisant une approche multi-omique.
Bernard, M. (2024) · theses.fr (ABES) · doi:10.70675/566f36f5z4361z4a72z83f4z6abd401a4af9
Microbiome and plasmidome shifts drive carbon, nitrogen, and greenhouse gas dynamics within transitioning permafrost
Leroy, M., Cyriaque, V., Rattei, T., Isabelle Laurion, E., & Comte, J. (2026) · Environmental Microbiome · doi:10.1186/s40793-026-00892-w
“Due to low sequencing depth, a de novo hybrid co-assembly technique was used to recover low-abundance microbes [34].”
Precision of Hi-C-Based Metagenome-Assembled Genome Reconstruction across Binning Pipelines
Lukaszewicz, M., Ridenhour, B. J., Top, E. M., & Stalder, T. (2026) · Computational and Structural Biotechnology Reports · doi:10.34133/csbr.0003
Host genetics shape productive lifespan in dairy cattle by sculpting rumen microbial metabolic functionsPreprint
Yang, P., Wu, Z., Cao, Z., Li, Y., Yan, L., Yan, Y., Zhou, F., Gu, S., Liu, C., Ren, X., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9101224/v1
Dietary L-Citrulline Supplementation Promotes Rumen Development and Modulates the Microbiota–Metabolome Axis in Suckling Hu Lambs
Tang, Z., Zhang, S., Xu, P., Tang, H., Gao, W., Cao, Y., Zhai, R., & Chen, K. (2026) · Animals · doi:10.3390/ani16111728
Lignocellulose-degrading enzymes/microbes: from gut to soil
Chen, P., Liu, T., Dong, Y., Qiao, S., Zhang, S., & Yan, L. (2026) · Materials Today Sustainability · doi:10.1016/j.mtsust.2026.101400
Metagenome-assembled genome sequence of an uncultured Roseburia sp. generated from mouse fecal DNA from the International Space Station
Chaudhary, A., Lin, X., Vitaterna, M., Auch, B., Liachko, I., & Green, S. (2026) · Microbiology Resource Announcements · doi:10.1128/mra.01047-25
BileActome reveals community-assembled bile acid metabolism in the rumen microbiome
Zhang, B., Jiang, X., Zhao, H., & Wang, B. (2026) · ISME Communications · doi:10.1093/ismeco/ycag205
L-carvone supplementation reduces methane production and modulates rumen fermentation, digestibility and microbial communities in vitro
Jalal, H., Pompei, L., Giammarco, M., Malik, M. I., Akram, S., Akram, M. Z., Domenico, M. D., Ghiaccio, F., Colleluori, R., Pezzi, P., et al. (2026) · Frontiers in Veterinary Science · doi:10.3389/fvets.2026.1900394
“Firmicutes, Bacteroidetes, and Proteobacteria were the dominant bacteria in this study, consistent with the earlier findings (35, 41, 42).”
Modulation of the rumen microbiome and metabolism in dairy cows by altering the concentrate feeding pattern and the inclusion of Saccharomyces cerevisiae yeast
Snelling, T. J., Johnson, C. A., Warren, H. E., Taylor-Pickard, J. A., Huntington, J. A., & Sinclair, L. A. (2026) · Frontiers in Microbiomes · doi:10.3389/frmbi.2026.1884444
Beyond ARG abundance: Genome-resolved metagenomics redefines Resistome dynamics in manure composting
Ahmad, H., Rauf, F., Ullah, I., Khan, S., & Korai, S. K. (2026) · Gene Reports · doi:10.1016/j.genrep.2026.102602
The ingestion of silymarin as an additive selectively modulates the ruminal and intestinal microbiota in lactating Jersey cows
Novakoski, P. V., Nora, L., Deolindo, G. L., Bajay, M. M., & da Silva, A. S. (2026) · Microbial Pathogenesis · doi:10.1016/j.micpath.2026.108812
Isolation, identification, characterization, regulation, and application of lactic acid bacteria
França, F., & Pìmentel-Filho, N. D. J. (2026) · The Microbe · doi:10.1016/j.microb.2026.100794
Predicting Enteric Methane Emissions from Crossbred Growing Bulls (Montbéliarde × Borgou) Under Semi-Intensive Production Systems
Alimi, N., Séidou, A. A., Houngbedji, M. J., Naitchede, H., Worogo, H. S. S., Idrissou, Y., Toukourou, Y., Attakpa, E. Y., & Traoré, I. A. (2026) · Methane · doi:10.3390/methane5030029
“According to [23], absolute feed intake regulates the structural volume of fermentable organic matter—predominantly structural carbohydrate matrices like neutral detergent fiber (NDF)—deposited directly into the reticulorumen and the microbial consortium degrades these fibrous particles; hydrolytic…”
Lyophilized rumen fluid from goats fed different roughage-based diets: fermentation characteristics, microbial communities, and post-reconstitution in vitro fermentation performance
Na, M., Guo, W., Zhang, R., Du, H., Li, K., Zhang, J., & Na, R. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2026.1905439
Resolving Host-Associated Antimicrobial Resistance in Municipal Wastewater Using Hi-C Metagenomics
Senok, A. C., Verma, S., Shouqair, D., Alghafri, R., Moradigaravand, D., Nassar, R., Everett, D., Goering, R., & Khan, M. A. (2026) · Microorganisms · doi:10.3390/microorganisms14102153
“Metagenome deconvolution was performed with the ProxiMeta (Phase Genomics) platform [17,18], which clusters assembled contigs into metagenome-assembled genomes (MAGs) based on Hi-C proximity-ligation linkage.”
Decomposition potential and population structure among active microbial communities in deep salt marsh sedimentsPreprint
Vineis, J. H., Bulseco, A., Rafie, S., Cardon, Z. G., & Bowen, J. L. (2026) · eLife · doi:10.7554/elife.108843.2
Comparative Study on Growth Performance and Rumen Digestive Metabolism Between Cull Beef Cows and Fattening Bulls
Bao, J., Li, H., Jiang, K., Xu, J., Wu, Y., Ji, W., Li, S., Qi, B., & Niu, H. (2026) · Animals · doi:10.3390/ani16193052
“Large-scale rumen metagenomic studies have shown that the cow rumen microbiome contains abundant microbial genomes and enzyme-coding genes involved in plant material degradation and carbohydrate utilization [9,10].”
Beyond the Rumen: Current Evidence and Knowledge Gaps in Microbial Diversity and Function Across the Bovine Gastrointestinal Tract
Akanmu, A. M. (2026) · Current Microbiology · doi:10.1007/s00284-026-05159-8
“Genome reconstruction has greatly expanded the recognised metabolic repertoire and carbohydrate-active enzyme (CAZyme) diversity [ 16 ].”
Influential
Taxonomic and Functional Profiles of the Gastrointestinal Microbiome Across Digestive Tract Segments in Growing Water Buffaloes
Wen, J., Huang, J., Liu, G., Liang, S., Zeng, L., Huang, R., Nongqi, Y., Qin, G., Chen, Q., & Yan, S. (2026) · Microorganisms · doi:10.3390/microorganisms14102206
“High-resolution metagenomic resources for ruminants initially focused mainly on the rumen, with public catalogs expanding from 913 rumen microbial genomes to 4941 MAGs [6,7].”
Metabolic network restoration, not donor microbial engraftment, associates with resolution of recurrent Clostridioides difficile infection
Whitman, B. T., Karimianghadim, R., Auch, B., Liachko, I., Monaghan, T., Pakpour, S., & Kao, D. (2026) · iScience · doi:10.1016/j.isci.2026.117907

Authors: Stewart, R. D., Auffret, M. D., Warr, A., Wiser, A. H., Press, M. O., Langford, K. W., Liachko, I., Snelling, T. J., Dewhurst, R. J., Walker, A. W., Roehe, R., & Watson, M.
Affiliations: Roslin Institute (GB)
Scotland's Rural College (GB)
University of Aberdeen (GB)
Corresponding author: Watson, Mick
ORCID: Auffret, Marc D.
Warr, Amanda
Press, Maximilian O.
Langford, Kyle W.
Liachko, Ivan
Snelling, Timothy J.
Dewhurst, Richard J.
Walker, Alan W.
Roehe, Rainer
Watson, Mick
Venue: Nature Communications
Publisher: Springer Science and Business Media LLC
Published: 2018-02-28
Volume: 9
Issue: 1
Pages: 870-870
ISSN: 2041-1723 (ISSN-L)
DOI: 10.1038/s41467-018-03317-6
PMC: PMC5830445
OpenAlex: W2786358949
Cited by: 559
References: 83
Citations by year: 2026: 39 · 2025: 48 · 2024: 77 · 2023: 69 · 2022: 64 · 2021: 73 · 2020: 89 · 2019: 73 · 2018: 27
FWCI: 24.09
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Nature Communications (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Edinburgh Research Explorer (University of Edinburgh) (publishedVersion, cc-by)
RePEc: Research Papers in Economics (submittedVersion)
Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Rural and Environment Science and Analytical Services Division
Scottish Government
Scotland’s Rural College
Directorate for Biological Sciences
Biotechnology and Biological Sciences Research Council (BBS/E/D/30002276, BB/M01844X/1, BB/J004235/1, BB/R015023/1, BB/P013732/1, BB/J004235, BB/N016742/1, BB/ J004243/1, BB/N01720X/1, P013759, BBP013759/1, BBS/E/D/20310000)
National Institute of Allergy and Infectious Diseases (R43AI122654, R44AI122654)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Agricultural and Biological Sciences › Animal Science and Zoology › Ruminant Nutrition and Digestive Physiology
Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Anaerobic Digestion and Biogas Production
Keywords: rumen microbiome, draft genome sequence, metagenome binning, Hi-C, cattle, rumen bacteria, rumen archaea, carbohydrate metabolism, biomass-degrading enzymes, metagenome-assembled genomes, read classification
MeSH terms: Animals, Bacteria, Bacterial Proteins, Cattle, Phylogeny, Rumen, Genome, Bacterial, Metagenome, Metagenomics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks