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 301–400 of 600

Microbiota and Serum Metabolic Profile Changes in Korean Native Hanwoo Steer in Response to Diet Feeding Systems
Jung, J. S., Soundharrajan, I., Kim, D., Baik, M., Ha, S., & Choi, K. C. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232012391 · Cited by 10
“They have a low protein binding ability and are capable of digesting a wide range of carbohydrate substrates [39].”
Background
Functional Diversity within Gut Microbiomes: Implications for Conserving Biodiversity
Dodd, C. S., & Grueber, C. E. (2021) · Conservation · doi:10.3390/conservation1040024 · Cited by 10
Metagenomic mining of Indian river confluence reveal functional microbial community with lignocelluloytic potential
Rajput, V., Samson, R., Yadav, R., Dastager, S. G., Khairnar, K., & Dharne, M. S. (2022) · 3 Biotech · doi:10.1007/s13205-022-03190-7 · Cited by 8
Ensiled diet improved the growth performance of Tibetan sheep by regulating the rumen microbial community and rumen epithelial morphology
Zhao, J., Zhao, X., Gao, J., Bai, B., Niu, J., Yang, Y., Zhao, G., Wang, Z., Xu, Z., Wang, J., et al. (2024) · Journal of Animal Science · doi:10.1093/jas/skae173 · Cited by 9
Comparative Analysis of Gut Bacterial Diversity in Wild and Domestic Yaks on the Qinghai–Tibetan Plateau
Shah, T., Guo, X., Song, Y., Fang, Y., & Ding, L. (2024) · Animals · doi:10.3390/ani14162380 · Cited by 9
The crosstalk between host and rumen microbiome in cattle: insights from multi-omics approaches and genome-wide association studies
Ye, X., Sahana, G., Lund, M. S., & Cai, Z. (2025) · World Journal of Microbiology and Biotechnology · doi:10.1007/s11274-025-04504-6 · Cited by 10
“Subsequent efforts have further enriched genomic resources, including 913 metagenome-assembled genomes (MAGs) (Stewart et al. 2018), 4941 MAGs (Stewart et al. 2019), over 10,000 MAGs from ruminants (Xie et al. 2021), and 1200 high-quality MAGs from African cattle (Wilkinson et al. 2020) (Fig.”
Review: Shifts of rumen microbiota by feeding non-fibrous carbohydrates to improve cattle performance
Souza, J. M., Ribeiro, P. H. C., & Millen, D. D. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2026.1735296 · Cited by 12
“…Prevotella within the Bacteroidetes phylum are highly adapted to the utilization of non-fiber carbohydrates, producing amy-lases and glycosidases that facilitate the rapid fermentation of starches and soluble polysaccharides (Purushe et al., 2010; Stewart et al., 2018; Accetto and Avguštin, 2019).”
Temporal colonization and metabolic regulation of the gut microbiome in neonatal oxen at single nucleotide resolution
Dong, Q., Hua, D., Wang, X., Jiao, Y., Liu, L., Deng, Q., Wu, T., Zou, H., Zhao, C., Wang, C., et al. (2024) · The ISME Journal · doi:10.1093/ismejo/wrad022 · Cited by 10
“We compared these representative genomes with existing microbial genomes obtained from cattle, including RUG [45], RUG2.”
BackgroundResult
Effects of Exogenous Glucoamylase Enzymes Alone or in Combination with a Neutral Protease on Apparent Total Tract Digestibility and Feces D-Lactate in Crossbred Angus Bulls Fed a Ration Rich in Rolled Corn
Devant, M., Yu, S., Genís, S., Larsen, T., & Wenting, L. (2020) · Animals · doi:10.3390/ani10061077 · Cited by 9
“Glucoamylases were selected in this study considering rumen microbiota metagenome data available in recent years showing more abundancy of AA genes than GA genes [5,6].”
MethodResult
Age-related compositional and functional changes in the adult and breastfed buffalo rumen microbiome
Xu, Y., Feng, T., Ding, Z., Li, L., Li, Z., Cui, K., Chen, W., Pan, H., Zhu, P., & Liu, Q. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1342804 · Cited by 8
Significant response of coral-associated bacteria and their carbohydrate-active enzymes diversity to coral bleaching
Sun, F., Yang, H., Zhang, X., Tan, F., Wang, G., & Shi, Q. (2024) · Marine Environmental Research · doi:10.1016/j.marenvres.2024.106694 · Cited by 8
Can meta-omics revolutionize our understanding of rumen methane emissions?
Zhao, H., Bai, S., Tan, J. F., Liu, M., Zhao, Y., & Jiang, L. (2024) · Animal Nutriomics · doi:10.1017/anr.2024.18 · Cited by 9
Metagenomic analysis reveals the community composition and functional characteristics of ruminal microbes in fermenting lignocellulosic biomass in vivo and in vitro
Liang, J., Lv, L., Sun, L., Chen, L., Zhang, G., & Zhang, P. (2025) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2025.115851 · Cited by 8
Potential role of key rumen microbes in regulating host health and growth performance in Hu sheep
Xie, X., Yang, H., Zhao, X., Li, T., Yang, Y., & Luo, H. (2025) · Animal Microbiome · doi:10.1186/s42523-025-00412-0 · Cited by 9
“…enzymes in the rumen and include a large class of enzymes involved in the metabolism of polysaccharides such as starch, cellulose, lignin, and chitin, responsible for catalyzing the hydrolysis of glycosidic bonds in complex carbohydrates [9, 73], and promoting the production of VFAs in the rumen.”
Prediction of functional metagenomic composition using archived 16S rDNA sequence data from the gut microbiota of livestock
Avila-Jaime, B., Kawas, J. R., & García-Mazcorro, J. F. (2018) · Livestock Science · doi:10.1016/j.livsci.2018.04.017 · Cited by 8
Effect of Dioscorea Opposite Waste Supplementation on Antioxidant Capacity, Immune Response and Rumen Microbiome in Weaned Lambs
Yang, R., Guo, Y., Zhang, S., Hao, Q., Duan, C., Wang, Y., Ji, S., Yan, H., Zhang, Y., & Liu, Y. (2023) · Fermentation · doi:10.3390/fermentation9030256 · Cited by 8
Metagenomic Insights Into Ecosystem Function in the Microbial Mats of a Large Hypersaline Coastal Lagoon System
Walter, J. M., de Oliveira, L. S., Tschoeke, D. A., Meirelles, P. M., Neves, M. H. C. B., Batista, D., Carvalho, A. P., Costa, R. D. S., Dobretsov, S., Coutinho, R., et al. (2021) · Frontiers in Marine Science · doi:10.3389/fmars.2021.715335 · Cited by 8
Intraintestinal Analysis of the Functional Activity of Microbiomes and Its Application to the Common Marmoset Intestine
Uehara, M., Inoue, T., Kominato, M., Hase, S., Sasaki, E., Toyoda, A., & Sakakibara, Y. (2022) · mSystems · doi:10.1128/msystems.00520-22 · Cited by 8
“these reconstructed genomes improved the metagenomic read classification 7-fold (15).”
Background
Functional profiling of abundant glycosyltransferases in the rhizospheric bacteriome of Abutilon fruticosum
Sonbol, H. S., & Jalal, R. S. (2024) · Rhizosphere · doi:10.1016/j.rhisph.2024.101001 · Cited by 7
The main representatives of the rumen microbiome (review)
Miroshnikova, M. S. (2020) · Животноводство и кормопроизводство · doi:10.33284/2658-3135-103-4-174 · Cited by 8
Strain-Level Dynamics Reveal Regulatory Roles in Atopic Eczema by Gut Bacterial Phages
Chu, Y., Meng, Q., Yu, J., Zhang, J., Chen, J., & Kang, Y. (2023) · Microbiology Spectrum · doi:10.1128/spectrum.04551-22 · Cited by 7
Analysis procedures for assessing recovery of high quality, complete, closed genomes from Nanopore long read metagenome sequencingPreprint
Arumugam, K., Bessarab, I. N., Haryono, M., Liu, X., Zuniga-Montanez, R. E., Roy, S., Qiu, G., Drautz‐Moses, D. I., Law, Y., Wuertz, S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.12.974238 · Cited by 7
Diversity of Gut Methanogens and Functional Enzymes Associated With Methane Metabolism in Smallholder Dairy CattlePreprint
Ng'etich, D. K., Bett, R. C., Gachuiri, C. K., & Kibegwa, F. M. (2022) · Research Square · doi:10.21203/rs.3.rs-1120235/v1 · Cited by 7
Multi-Omics Reveals Microbial Roles and Metabolic Functions at the Spatiotemporal Niche in Pit MudPreprint
Ren, D., Liu, S., Zhang, S., Qin, H., Han, X., & Mao, J. (2022) · Research Square · doi:10.21203/rs.3.rs-1251838/v1 · Cited by 7
Genomic profiling of Antarctic geothermal microbiomes using long-read, Hi-C, and single-cell techniques
Myeong, N. R., Choe, Y., Shin, S. C., Kim, J., Sul, W. J., & Kim, M. (2024) · Scientific Data · doi:10.1038/s41597-024-03875-z · Cited by 7
“Deeper sequencing enhances our understanding at the genome level by reconstructing metagenome-assembled genomes (MAGs), which is particularly useful in less complex environments 12,13 .”
Background
Genome-centric metagenomics reveals uncharacterised microbiomes in Angus cattle
Legrand, T. P. R. A., Alexandre, P. A., Wilson, A., Farr, R. J., Reverter, A., & Denman, S. E. (2025) · Scientific Data · doi:10.1038/s41597-025-04919-8 · Cited by 10
Proximity‐Ligation Metagenomic Sequence Analysis Reveals That the Antibiotic Resistome Makes Significant Transitions During Municipal Wastewater Treatment
McCorison, C. B., Kim, T., Donato, J. J., & LaPara, T. M. (2025) · Environmental Microbiology · doi:10.1111/1462-2920.70036 · Cited by 8
“(Press et al. 2017; Stewart et al. 2018).”
Patterns of genomic variation reveal a single evolutionary origin of the wild allotetraploid Mimulus sookensis
Whitener, M. R., Mangelson, H., & Sweigart, A. L. (2024) · Evolution · doi:10.1093/evolut/qpae079 · Cited by 4
Rodents consuming the same toxic diet harbor a unique functional core microbiome
Stapleton, T. E., Lindsey, L. M., Sundar, H., & Dearing, M. D. (2024) · Animal Microbiome · doi:10.1186/s42523-024-00330-7 · Cited by 7
“However, poorly studied hosts living in natural environments, much of the genetic information recovered cannot be classified with currently available databases, as was the case in this study and many others [38, 49, 97, 107, 117].”
Background
MT-MAG: Accurate and interpretable machine learning for complete or partial taxonomic assignments of metagenome-assembled genomes
Li, W., Lila Kari, A., Yu, Y., & Hug, L. A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0283536 · Cited by 6
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) · BMC Ecology and Evolution · doi:10.1186/s12862-023-02160-8 · Cited by 13
Transcriptional control of carbohydrate catabolism by the CcpA protein in the ruminal bacterium Streptococcus bovis
Zhao, X., Zhang, Y., He, B., Han, Y., Shen, B., Zang, Y., & Wang, H. (2023) · Applied and Environmental Microbiology · doi:10.1128/aem.00474-23 · Cited by 6
“On the other hand, due to the significant differences in diet between humans and ruminants, as well as variations in microbial community structure (19, 20), specific ecological niches have emerged, promoting the evolution of strains in different directions.”
Background
Manipulating the rumen microbiome to address challenges facing Australasian dairy farming
Stanton, C., Leahy, S. C., Kelly, B., Ross, R. P., & Attwood, G. T. (2019) · Animal Production Science · doi:10.1071/an18611 · Cited by 6
Characteristics of ruminal microbial community: Evolutionary and ecological perspectives
Patra, A. K. (2020) · Indian Journal of Animal Health · doi:10.36062/ijah.59.2spl.2020.114-127 · Cited by 6
Potentilla anserina L. developmental changes affect the rhizosphere prokaryotic community
Wang, Y., Liu, Y., Li, X., Han, X., Zhang, Z., Ma, X., & Li, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-82610-9 · Cited by 6
Peer Review #1 of "Yak rumen microbial diversity at different forage growth stages of an alpine meadow on the Qinghai-Tibet Plateau (v0.2)"
(2019) · PeerJ · doi:10.7287/peerj.7645v0.2/reviews/1 · Cited by 6
Thermogenic hydrocarbons fuel a redox stratified subseafloor microbiome in deep sea cold seep sedimentsPreprint
Dong, X., Rattray, J. E., Campbell, D. C., Webb, J., Chakraborty, A., Adebayo, O., Matthews, S., Li, C., Fowler, M. G., MacDonald, A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.02.928283 · Cited by 7
A Comprehensive Analysis of the Global Human Gut Archaeome from a Thousand Genome CataloguePreprint
Chibani, C. M., Mahnert, A., Borrel, G., Almeida, A., Werner, A., Brugère, J., Gribaldo, S., Finn, R. D., Schmitz, R. A., & Moissl‐Eichinger, C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3742253 · Cited by 6
Comprehensive mouse gut metagenome catalog reveals major difference to the human counterpartPreprint
Kieser, S., Zdobnov, E. M., & Trajkovski, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.18.435958 · Cited by 6
How marine currents and environment shape plankton genomic differentiation: a mosaic view from Tara Oceans metagenomic dataPreprint
Laso‐Jadart, R., O’Malley, M., Sykulski, A. M., Ambroise, C., & Madoui, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.29.441957 · Cited by 6
Prokaryotic cellulase gene clusters derived from 2,305 metagenomes
Song, B., Tria, F. D. K., & Skejo, J. (2025) · Scientific Data · doi:10.1038/s41597-025-04524-9 · Cited by 5
Exploration of mobile genetic elements in the ruminal microbiome of Nellore cattle
Faleiros, C. A., Nunes, A. T., Gonçalves, O. S., Alexandre, P. A., Poleti, M., Mattos, E. C., Perna, F., Rodrigues, P. H. M., & Fukumasu, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-63951-7 · Cited by 5
“To prepare Hi-C libraries, the Phase Genomics ProxiMeta Hi-C kit v4.”
MethodResultInfluential
Geographic patterns of antibiotic resistance in the human gut microbiome: insights from metagenome-assembled genomes across four Chinese provinces
Zhou, S., Liu, H., Bai, X., Li, D., Wang, T., Zhong, H., Gao, W., & Sun, J. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1652757 · Cited by 5
“…in humans (Almeida et al., 2021) and chickens (Huang et al., 2018), and thousands of MAGs have been generated from diverse hosts, including humans (Pasolli et al., 2019), ruminants (Stewart et al., 2018), chickens (Glendinning et al., 2020), pigs (Wang et al., 2019), and horses (Li et al., 2023).”
Cells function as a ternary logic gate to decide migration direction under integrated chemical and fluidic cues
Moon, H., Saha, S., Mugler, A., & Han, B. (2022) · Lab on a Chip · doi:10.1039/d2lc00807f · Cited by 5
Alterations in the gut microbial composition and diversity associated with diarrhea in neonatal Peruvian alpacas
Zapata, C., Estrada, R., Oros, O., Sánchez, D. C. S., Maicelo, J. L., Arbizu, C. I., & Añasco, P. U. C. (2024) · Small Ruminant Research · doi:10.1016/j.smallrumres.2024.107273 · Cited by 6
Comparing rumen fluid collection methods on fermentation profile and microbial population in lactating dairy cows
Pathak, N., Guan, H., Fan, P. F., Sultana, H., Arriola, K. G., Oyebade, A., NinoDeGuzman, C., Malekkhahi, M., Jeong, K. C., & Vyas, D. (2024) · JDS Communications · doi:10.3168/jdsc.2024-0566 · Cited by 5
Hydrogen-rich water 400ppb as a potential strategy for improving ruminant nutrition and mitigating methane emissions
Mao, K., Lu, G., Zang, Y., Qiu, Q., Zhao, X., Ouyang, K., Qu, M., & Li, Y. (2024) · BMC Microbiology · doi:10.1186/s12866-024-03638-1 · Cited by 5
Unravelling AMR dynamics in the rumenofaecobiome: Insights, challenges and implications for One Health
Wu, Z., Famous, M., Stoikidou, T., Bowden, F. E. S., Dominic, G., Huws, S., Godoy-Santos, F., & Oyama, L. B. (2025) · International Journal of Antimicrobial Agents · doi:10.1016/j.ijantimicag.2025.107494 · Cited by 6
Metagenomics Insights into the Role of Microbial Communities in Mycotoxin Accumulation During Maize Ripening and Storage
Nie, X., Chen, X., Lu, X., Yang, S., Wang, X., Liu, F., Yang, J., Guo, Y., Shi, H., Xu, H.-L., et al. (2025) · Foods · doi:10.3390/foods14081378 · Cited by 5
“Overall, the findings indicate that this maize is associated with a high prevalence of toxic fungi, the accumulation of mycotox-ins, and potential hazards to human and animal health.”
Evolution of Holobionts: The Hologenome Concept
ROSENBERG, E. B. (2021) · The microbiomes of humans, animals, plants, and the environment · doi:10.1007/978-3-030-65317-0_9 · Cited by 6
Metagenomes Binning Using Proximity-Ligation Data
Marbouty, M., & Koszul, R. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1390-0_8 · Cited by 5
Rumen microbial genomics: from cells to genes (and back to cells)
Popova, M., Fakih, I., Forano, É., Siegel, A., Muñoz‐Tamayo, R., & Morgavi, D. (2022) · CABI Reviews · doi:10.1079/cabireviews202217025 · Cited by 6
Recovery of metagenomic data from the Aedes aegypti microbiome using a reproducible snakemake pipeline: MINUUR
Foo, A., Cerdeira, L., Hughes, G. L., & Heinz, E. (2023) · Wellcome Open Research · doi:10.12688/wellcomeopenres.19155.1 · Cited by 5
Development of a field-deployable qPCR assay for real-time pest monitoring in algal cultivation systems
Sanchez, M. R., Denning, E., Biondi, T. C., Hovde, B. T., Eacker, S. M., Getto, S., Kaur, H., Jebali, A., Echenique‐Subiabre, I., Green, M., et al. (2023) · Algal Research · doi:10.1016/j.algal.2023.103194 · Cited by 5
Deduplication Improves Cost-Efficiency and Yields of De Novo Assembly and Binning of Shotgun Metagenomes in Microbiome Research
Zhang, Z., Zhang, L., Zhang, G., Zhao, Z., Wang, H., & Ju, F. (2023) · Microbiology Spectrum · doi:10.1128/spectrum.04282-22 · Cited by 5
“the recovery of metagenome-assembled genomes (MAGs) from various complex environments, including the global ocean (19), the cow rumen (22), the human gut (23, 24),”
Background
Metagenomics-Metabolomics Exploration of Three-Way-Crossbreeding Effects on Rumen to Provide Basis for Crossbreeding Improvement of Sheep Microbiome and Metabolome of Sheep
Wang, H., Zhan, J., Jiang, H., Jia, H., Yue, P., Zhong, X., Huo, J., & Zhao, S. (2024) · Animals · doi:10.3390/ani14152256 · Cited by 5
“Macrogenomic analysis revealed that the GH and GT families were enriched in rumen species, which is similar to previous findings [83,84].”
Result
A catalog of microbial genes from the bovine rumen unveils a specialized and diverse biomass-degrading environmentPreprint
Li, J., Zhong, H., Ramayo‐Caldas, Y., Terrapon, N., Lombard, V., Potocki-Véronèse, G., Estellé, J., Popova, M., Yang, Z., Zhang, H., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/272690 · Cited by 6
Alternative hydrogen uptake pathways suppress methane production in ruminantsPreprint
Greening, C., Geier, R. R., Wang, C., Woods, L. C., Morales, S. E., McDonald, M. J., Rushton-Green, R., Morgan, X. C., Koike, S., Leahy, S. C., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/486894 · Cited by 5
Metage2Metabo: metabolic complementarity applied to genomes of large-scale microbiotas for the identification of keystone speciesPreprint
Belcour, A., Frioux, C., Aïte, M., Bretaudeau, A., & Siegel, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/803056 · Cited by 5
Shotgun and Hi-C Sequencing Datasets for Binning Wheat Rhizosphere Microbiome
Regmi, R., Anderson, J. P., Burgess, L., Mangelson, H., Liachko, I., & Gupta, V. V. S. R. (2025) · Scientific Data · doi:10.1038/s41597-025-04651-3 · Cited by 5
Opening doors to physical sample tracking and attribution in Earth and environmental sciences
Damerow, J., Raia, N. H., Stanley, V., Choe, S., Borton, M., Byers, N., Cassidy, E. R., Cholia, S., Edmunds, R., Forbes, B., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-05295-z · Cited by 6
“Similarly, the reuse of genomics datasets has contributed to research with diverse applications, for example in the industrial biotechnology 100 and biomedical 101 sectors, and has enabled researchers to better understand the biological effects of ecosystem disturbance 102 .”
Biocontrol Effect of Bacillus velezensis D7-8 on Potato Common Scab and Its Complete Genome Sequence Analysis
Jiang, Y. Y., He, P., Kong, H., He, P., Wu, Y., Tang, G., Tang, P., Di, Y., Li, X., Liu, L., et al. (2025) · Microorganisms · doi:10.3390/microorganisms13040770 · Cited by 4
“These findings lay a solid foundation for the development of B. velezensis D7-8 as a biocontrol agent against potato common scab in practical agricultural applications.”
16S rRNA gene amplicon sequencing for microbiota analysis of rumen fluid, feces, and milk of Sarda sheep fed different contents of alfalfa hay (Medicago sativa)
Vanzin, A., Giannuzzi, D., Zardinoni, G., Cecchinato, A., Correddu, F., Macciotta, N. P. P., Atzori, A. S., Carta, S., Ledda, A., Schiavon, S., et al. (2025) · Journal of Dairy Science · doi:10.3168/jds.2024-26172 · Cited by 4
Biochanin A improves nitrogen utilization efficiency by regulating ruminal microbial community in dairy goats
Zhang, X., Li, Y., Xiong, Z., Zheng, N., Wang, J., & Zhao, S. (2025) · Microbiome · doi:10.1186/s40168-025-02275-8 · Cited by 4
“Metagenomic binning, an effective technique for recovering microbial genomes, was employed to analyze the rumen microbial genomes [32].”
Recovery of 52 bacterial genomes from the fecal microbiome of the domestic cat ( Felis catus ) using Hi-C proximity ligation and shotgun metagenomics
Rojas, C. A., Gardy, J. L., Eisen, J. A., & Ganz, H. H. (2023) · Microbiology Resource Announcements · doi:10.1128/mra.00601-23 · Cited by 4
“Our data is limited to two individual cats, but we add to the growing catalog of studies using Hi-C with shotgun sequencing to retrieve quality genomes from mammals, including dogs (20), sheep (21), cows (16), and humans (22).”
BackgroundMethodInfluential
Metagenomic and metabolomic analyses reveal differences in rumen microbiota between grass- and grain-fed Sanhe heifers
Zhang, X., Wang, W., Wang, Y., Cao, Z., Yang, H., & Li, S. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1336278 · Cited by 6
“Regarding the carbohydrate-active enzymes in the rumen microbiome, the GH-encoding genes had the widest distribution in the genomes (Stewart et al., 2018), mainly comprising enzymes involved in polysaccharide metabolism, including cellulose and starch.”
Background
MFTP-Tool: A Wide & Deep Learning Framework for Multi-Functional Therapeutic Peptides Prediction
Lv, Y., Liu, T., Ma, Y., Lyu, H., & Liu, Z. (2024) · Current Bioinformatics · doi:10.2174/0115748936299646240625092734 · Cited by 4
Oat Brewery Waste Decreased Methane Production and Alters Rumen Fermentation, Microbiota Composition, and CAZymes Profiles
Malik, P. K., Trivedi, S., Mohapatra, A., Kolte, A. P., Mech, A., Victor, T., Ahasic, E., & Bhatta, R. (2024) · Microorganisms · doi:10.3390/microorganisms12071475 · Cited by 4
“GH is a broad class consisting of a large number of families (111 identified in this study) and is involved in the degradation of cellulose, chitin, and starch [96,97].”
Background
Metagenomics: A paradigm shift in microbiology
Mardanov, A. V., Kadnikov, V. V., & Ravin, N. V. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-91631-8.00006-8 · Cited by 5
Rumen-Targeted Mining of Enzymes for Bioenergy Production
Cann, I., Cheng, Y., Alhawsawi, M. A. B., Moran, M., Li, Y., Gong, T., Zhu, W., & Mackie, R. I. (2024) · Annual Review of Animal Biosciences · doi:10.1146/annurev-animal-021022-030040 · Cited by 4
Cultivation and sequencing of microbiota members unveil the functional potential of yak gut microbiota
Dai, M., Zhao, F., Shi, X., Tian, C., Lin, Y., Bai, L., Li, T., Jin, X., Xiao, L., Kristiansen, K., et al. (2025) · mSystems · doi:10.1128/msystems.00367-25 · Cited by 4
Improving recovery of member genomes from enrichment reactor microbial communities using MinION–based long read metagenomicsPreprint
Arumugam, K., Bessarab, I. N., Liu, X., Natarajan, G. K., Drautz‐Moses, D. I., Wuertz, S., Lauro, F. M., Law, Y., Huson, D. H., & Williams, R. B. H. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/465328 · Cited by 4
Tracking Double-Stranded DNA Bacteriophages and Their Hosts in a Deep Freshwater Lake by Integrating Metagenomics and The Hi-C TechniquePreprint
Ye, T., Gao, P., Li, B., Xing, P., & Wu, Q. L. (2020) · Research Square · doi:10.21203/rs.3.rs-129104/v1 · Cited by 4
“More broadly, this technique was able to reconstruct strainand species-level microbial genomes (metagenome-assembled genomes, MAGs) from meta-communities [43].”
Method
Novel microbial syntrophies identified by longitudinal metagenomicsPreprint
Raguideau, S., Trego, A., Farrell, F. F., Collins, G., Quince, C., & Soyer, O. S. (2021) · bioRxiv · doi:10.1101/2021.07.05.451125 · Cited by 1
“Thus, several novel MAGs, including some CPRs, were shown to harbour genes 70 associated with key processes including organic degradation (2,8,9) and nitrogen, and sulfur, 71 cycling (10-13).”
Background
Dietary n-3 polyunsaturated fatty acids enhance dry matter intake in transition cows by regulating taste transduction gene expression in the liver associated with rumen microbial alteration
Sun, X., Guo, C., Wang, Q., Zhang, Y., Wang, Z., Cao, Z., Wang, W., & Li, S. (2025) · Animal nutrition · doi:10.1016/j.aninu.2025.01.015 · Cited by 3
Persistent auxiliary microbiome of early novel colonizers in the developing rumen with lasting functional significance
Furman, O., Sorek, G., Moraïs, S., Levin, L., Tovar-Herrera, O. E., Winkler, S., & Mizrahi, I. (2025) · The ISME Journal · doi:10.1093/ismejo/wraf252 · Cited by 3
“[39], respectively.”
Influential
Bioaerosols in Indoor Environments: characteristic, health risk, sampling, analysis methodologies and challenges – a review
Naik, A. K., & Zabłocka-Godlewska, E. (2026) · Atmospheric Environment · doi:10.1016/j.atmosenv.2026.121934 · Cited by 3
Metagenomic Approaches for the Discovery of Pollutant-Remediating Enzymes: Recent Trends and Challenges
Mukherjee, A., & Cotter, P. D. (2022) · Springer eBooks · doi:10.1007/978-981-19-4320-1_24 · Cited by 3
The Alpine ibex (Capra ibex) gut microbiome, seasonal dynamics, and potential application in lignocellulose bioconversion
Nanetti, E., Scicchitano, D., Palladino, G., Interino, N., Corlatti, L., Pedrotti, L., Zanetti, F., Pagani, E., Esposito, E., Brambilla, A., et al. (2024) · iScience · doi:10.1016/j.isci.2024.110194 · Cited by 4
“…using MinHash sketches implemented in the mash tool (v. 2.3), with 8,217 genomes from three of the largest ruminant gut metagenomic datasets, 10,57,58 with the Genomic Encyclopedia of Bacteria and Archaea (GEBA) collection 56 and with 4,930 SGBs previously identiﬁed in a study describing the…”
Influential
Integrated Multi-Omics Reveals New Ruminal Microbial Features Associated with Peanut Vine Efficiency in Dairy Cattle
Teng, Z., Zhang, N., Zhang, L., Zhang, L., Liu, S., Fu, T., Wang, Q., & Gao, T. (2024) · Life · doi:10.3390/life14070802 · Cited by 3
“Prevotella are major rumen microbes that can produce succinate and acetate from starch and protein [44].”
Background
Sequential co-assembly reduces computational resources and errors in metagenome-assembled genomes
Lynn, H. M., & Gordon, J. I. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101005 · Cited by 3
Comparative Analysis of Microbial–Short-Chain Fatty Acids–Epithelial Transport Axis in the Rumen Ecosystem Between Tarim Wapiti (Cervus elaphus yarkandensis) and Karakul Sheep (Ovis aries)
Huang, J., Sheng, Y., Jia, X., Qian, W., & Li, Z. (2025) · Microorganisms · doi:10.3390/microorganisms13051111 · Cited by 3
“GHs may hydrolyze glycosidic linkages in complex carbohydrates, degrade cellulose, hemicellulose, and starch effectively [70], and degrade lignocellulosic materials synergistically with CEs and PLs [71,72].”
Building Predictive Understanding of the Activated Sludge Microbiome by Bridging Microbial Growth Kinetics and Microbial Population Dynamics
Cheng, Z., Xia, W., McKelvey, S., He, Q., Chen, Y., & Yuan, H. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c05925 · Cited by 3
Rôle de l'environnement précoce dans la variabilité des phénotypes et l’adaptation des animaux d’élevage à leur milieu
Frédérique Pitel, A., Calenge, F., Aigueperse, N., Estellé, J., Coustham, V., Calandreau, L., Morisson, M., Chavatte‐Palmer, P., & Ginane, C. (2019) · INRAE Productions Animales · doi:10.20870/productions-animales.2019.32.2.2467 · Cited by 3
Genome Analysis of a Member of the Uncultured Phylum Riflebacteria Revealed Pathways of Organotrophic Metabolism and Dissimilatory Iron Reduction
Kadnikov, V. V., Mardanov, A. V., Beletsky, A. V., Karnachuk, O. V., & Ravin, N. V. (2020) · Microbiology · doi:10.1134/s0026261720030078 · Cited by 3
“…of further Riflebacteria members from subsurface ecosystems were described: Candidatus Riflebacteria isolate UBA8953 (Parks et al., 2018) and Candidatus Ozemobacter sibiricus BY5 (Kadnikov et al., 2018), as well as Rumen uncultured genome RUG334 from a bovine rumen community (Stewart et al., 2018).”
Background
RUMEN METHANE PRODUCTION AND ITS REDUCTION USING NUTRITIONAL FACTORS (review)
Bogolyubova, N. V. (2023) · Sel skokhozyaistvennaya Biologiya · doi:10.15389/agrobiology.2022.6.1025eng · Cited by 3
A Comparison between Hi-C and 10X Genomics Linked Read Sequencing for Whole Genome Phasing in Hanwoo Cattle
Srikanth, K., Park, J., Lim, D., Cha, J., Cho, S., Cho, I., & Park, W. (2020) · Genes · doi:10.3390/genes11030332 · Cited by 3
“Approximately 100 µ L of blood was cross-linked for Hi-C using previously described protocols [24].”
Metagenomics of the Viral Community in Three Cattle Slurry Samples
Sazinas, P., Michniewski, S., Rihtman, B., Redgwell, T. A., Grigonyte, A., Brett, A., Guck, B., Smith, R. H., Hooton, S. P. T., Hobman, J. L., et al. (2019) · Microbiology Resource Announcements · doi:10.1128/mra.01442-18 · Cited by 3
“Although the bacterial fraction of the cow rumen has been characterized in some detail (1, 2) and to a lesser extent the viral fraction (3), relatively little is known about the bacterial or viral composition of slurry, the main constituent of which will be cow feces but will also include other farm wastes.”
Background
The genomic and proteomic landscape of the rumen microbiome revealed by comprehensive genome-resolved metagenomics
Watson, M. (2018) · University of Edinburgh · doi:10.7488/ds/2470 · Cited by 5
Association of milk microbiome in bovine clinical mastitis and their functional implications in cows in BangladeshPreprint
Hoque, M. N., Istiaq, A., Clement, R. A., Sultana, M., Crandall, K. A., Siddiki, A. Z., & Hossain, M. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/591982 · Cited by 3
A Genomic Blueprint of the Chicken Gut MicrobiomePreprint
Gilroy, R., Ravi, A., Getino, M., Pursley, I., Horton, D., Alikhan, N.-F., Baker, D., Gharbi, K., Hall, N., Watson, M., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-56027/v1 · Cited by 3
“The fact that novel metagenomic species are still being recovered from human gut datasets that include tens of thousands of metagenomes [12]—twinned with the promise of novel long-read and proximity-capture approaches to metagenome analyses [96]—make it clear that our attempts here to analyse all currently available c…”
Background
Genome sequence of thermophilic cellulolytic actinobacterium Thermobifida fusca strain UPMC 901 producing thermostable cellulases.
Zainudin, M. H. M., Gan, H. M., & Tokura, M. (2024) · Gene Reports · doi:10.1016/j.genrep.2024.101900 · Cited by 2
Molecular detection of ruminal micro-flora and micro-fauna in Saudi Arabian camels: Effects of season and region
Abdullah, H. M., Mohammed, O. B., Sheikh, A., Almathen, F. S., KHALID, A. M., Bakhiet, A. O., & Abdelrahman, M. M. (2024) · Saudi Journal of Biological Sciences · doi:10.1016/j.sjbs.2024.103982 · Cited by 2
“…microbiome interactions with the cow (cattle) nutrition, production and even the neonatal core of the microbiome have been studied and explored by some researchers (AlZahal et al., 2014; Li et al., 2019; Malmuthuge et al., 2019; Matthews et al., 2019; Mizrahi et al., 2021; Stewart et al., 2018).”
Background
Removal of sequencing adapter contamination improves microbial genome databases
Moeller, A. H., Dillard, B. A., Goldman, S. L., Real, M. V. F., & Sprockett, D. D. (2024) · BMC Genomics · doi:10.1186/s12864-024-10956-1 · Cited by 2
“Databases of thousands of microbial-genome assemblies from iso-lates and metagenomes (i.e., metagenome-assembled genomes—MAGs) are now available from humans [4], mice [5], cows [6], pigs [7], chicken [8], fish [3], and hon-eybees [9] as well as marine environments [3].”
Background
Exploring the rumen microbial function in Angus bulls with divergent residual feed intake
Sidney, T. S., Treon, E., Taiwo, G. A., Felton, E. E. D., Fan, P. F., & Ogunade, I. M. (2024) · Frontiers in Animal Science · doi:10.3389/fanim.2024.1485447 · Cited by 2
Rumen microbiome profiles of dairy cattle are affected by the presence of, and vaccination against, the abomasal parasitic nematode Ostertagia ostertagi
Lima, J., McNeilly, T. N., Auffret, M. D., Steele, P. J., Frew, D., Martínez-Álvaro, M., Dewhurst, R. J., Watson, M., & Roehe, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-30604-2 · Cited by 2
“For taxonomic classification, the sequence reads of the samples were aligned to a database including genomes from the Hungate 1000 Collection 33 and metagenome-assembled genomes (MAGs) from beef rumen samples 34 using Kraken 35 .”
Gut Microbiome and Metabolome Signatures Associated with Heat Tolerance in Dairy Cows
Li, M., Chen, P., Liu, C., Wang, S., Zhang, H., Li, J., Karrow, N. A., Mao, Y., & Yang, Z. (2025) · Microorganisms · doi:10.3390/microorganisms13122829 · Cited by 2
“…for complex polysaccharide utilization and immunomodulatory LPS biosynthesis [38,39]; given that these lineages derive from metagenome-assembled genomes without cultured representatives [40], their enrichment may indicate a carbohydrate-driven, potentially pro-inflammatory shift under heat stress.”
Effects of dietary supplementation with astragalus polysaccharides on growth performance, serum parameters, and rumen microbial function of yaks
Yang, G., He, W., Ma, B., La, Y., Ma, X., Wu, X., Chu, M., Zhao, M., Liu, X., Zhao, Y., et al. (2025) · BMC Microbiology · doi:10.1186/s12866-025-04581-5 · Cited by 2
“Additionally, GTs and PLs regulate extracellular polysaccharide synthesis [46, 47].”
Integrated multi-omics analysis reveals rumen and rectal microbiota–metabolite interaction features in polytocous fine-wool sheep with divergent residual feed intake
Kong, M., Pan, Z., Wang, X., Huang, J., Tulafu, H., Xu, Y., Sulaiman, Y., & Wu, W. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1712307 · Cited by 2
Comparative metagenomic and metatranscriptomic analyses reveal the role of the gayal rumen and hindgut microbiome in high-efficiency lignocellulose degradation
Li, S., Zhang, J., Han, L., Yu, Y., Mousa, A. A., Zhu, W.-Y., Leng, J., Xie, F., & Mao, S. (2026) · Journal of Animal Science and Biotechnology · doi:10.1186/s40104-025-01335-1 · Cited by 2
“Over the past decades, microbial communities of gastrointestinal fermenters, particularly domesticated ruminants such as dairy cattle, water buffalo, sheep, and goats [5–8], have been extensively studied.”

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