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 201–300 of 600

Accurate viral genome reconstruction and host assignment with proximity-ligation sequencingPreprint
Uritskiy, G., Press, M., Sun, C. L., Domínguez‐Huerta, G., Zayed, A. A., Wiser, A. H., Grove, J., Auch, B., Eacker, S. M., Sullivan, S. T., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.14.448389 · Cited by 27
“Additional benchmarking data was generated from the shortread WGS and Hi-C sequencing data from the cow rumen metagenome (64), the wastewater benchmark metagenome (48), and the human fecal metagenome (see human fecal microbiome processing).”
Method
Effect of Dietary and Age Changes on Ruminal Microbial Diversity in Holstein Calves
Wu, Y., Jiao, C., Diao, Q., & Tu, Y. (2023) · Microorganisms · doi:10.3390/microorganisms12010012 · Cited by 26
“Cellulosic materials in the feed are degraded into available nutrients (e.g., volatile fatty acids (VFAs)) through fermentation, thereby providing energy for the growth of the host and leading to the production of milk and meat products for human consumption [1,2].”
Exploration of genes encoding KEGG pathway enzymes in rhizospheric microbiome of the wild plant Abutilon fruticosum
Abulfaraj, A. A., Shami, A., Alotaibi, N. M., Alomran, M. M., Aloufi, A. S., Al‐Andal, A., AlHamdan, N. R., Alshehrei, F. M., Sefrji, F. O., Alsaadi, K. H., et al. (2024) · AMB Express · doi:10.1186/s13568-024-01678-4 · Cited by 29
“This pivotal truth has been recently substantiated by the pioneering efforts of several investigators (Dilthey et al. 2019, Quince et al. 2017, Raes et al. 2007, Scholz et al. 2016, Stewart et al. 2018, Tringe et al. 2005, Wilkins et al. 2019).”
Result
A Review of the Rumen Microbiota and the Different Molecular Techniques Used to Identify Microorganisms Found in the Rumen Fluid of Ruminants
da Silva, É. B. R., da Silva, J. A. R., da Silva, W. C., Belo, T. S., Sousa, C. E. L., dos Santos, M. R. P., Neves, K. A. L., Rodrigues, T. C. G. D. C., Júnior, R. N. C. C., & Júnior, J. D. B. L. (2024) · Animals · doi:10.3390/ani14101448 · Cited by 34
“The rumen microbiota is made up of different microorganisms depending on the region where the animal is reared and the diet used [11,12].”
Background
A Cautionary Report for Pathogen Identification Using Shotgun Metagenomics; A Comparison to Aerobic Culture and Polymerase Chain Reaction for Salmonella enterica Identification
Doster, E., Rovira, P., Noyes, N. R., Burgess, B. A., Yang, X., Weinroth, M. D., Linke, L., Magnuson, R. J., Boucher, C. A., Belk, K. E., et al. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.02499 · Cited by 26
“The study and detection of microbial organisms has long been reliant on cultivation and characterization of certain species, but advancements in sequencing technologies have revealed an underlying microbial diversity largely ignored by culturebased techniques (Staley and Konopka, 1985; Hugenholtz, 2002; Stewart et al.…”
Background
Recovery of human gut microbiota genomes with third-generation sequencing
Li, Y., Jin, Y., Zhang, J., Pan, H., Wu, L., Liu, D., Liu, J., Hu, J., & Shen, J. (2021) · Cell Death and Disease · doi:10.1038/s41419-021-03829-y · Cited by 26
Metagenome-Assembled Genomes Contribute to Unraveling of the Microbiome of Cocoa Fermentation
de Almeida, O. G. G., & Martinis, E. C. P. D. (2021) · Applied and Environmental Microbiology · doi:10.1128/aem.00584-21 · Cited by 26
Carbohydrate flow through agricultural ecosystems: Implications for synthesis and microbial conversion of carbohydrates
Low, K. E., Tingley, J. P., Klassen, L., King, M. L., Xing, X., Watt, C., Hoover, S. E., Gorzelak, M. A., & Abbott, D. W. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108245 · Cited by 26
Ecological connectivity of genomic markers of antimicrobial resistance in Escherichia coli in Hong Kong
Xu, X., Lin, Y., Deng, Y., Liu, L., Wang, D., Tang, Q., Wang, C., Chen, X., Che, Y., Wyrsch, E. R., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62455-w · Cited by 31
“IDEEL scores 66 were calculated to assess protein-coding gene integrity, de ﬁ ned as the proportion of predicted proteins ≥ 95%the length of their best BLASTPhit in a curated protein database.”
Genomic insights into ruminant evolution: from past to future prospects
Wang, B., Chen, L., & Wang, W. (2019) · 动物学研究 · doi:10.24272/j.issn.2095-8137.2019.061 · Cited by 27
“Most previous studies have focused on the interaction between microorganisms and the rumen (Bovine Genome Sequencing and Analysis Consortium et al., 2009; Seshadri et al., 2018; Stewart et al., 2018; Zhang et al., 2016).”
Background
Addressing Learning Needs on the Use of Metagenomics in Antimicrobial Resistance Surveillance
Duarte, A. S. R., Stärk, K. D. C., Munk, P., Leekitcharoenphon, P., Bossers, A., Luiken, R. E. C., Sarrazin, S., Lukjančenko, O., Pamp, S. J., Bortolaia, V., et al. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00038 · Cited by 25
“Metagenomic assembly and binning (31) help overcoming the first issue, and many recent developments have contributed to increase the number of genomes assembled from metagenomics datasets, including the methods of Hi-C Chromatin conformation capture (31), DNA methylation profiling (32) and co-assembly and cobinning (3…”
Background
In silico Prediction of Virus-Host Interactions for Marine Bacteroidetes With the Use of Metagenome-Assembled Genomes
Tominaga, K., Morimoto, D., Nishimura, Y., Ogata, H., & Yoshida, T. (2020) · Frontiers in Microbiology · doi:10.3389/fmicb.2020.00738 · Cited by 25
Taxonomic distribution and evolutionary analysis of the equol biosynthesis gene cluster
Dufault‐Thompson, K., Hall, B., & Jiang, X. (2022) · BMC Genomics · doi:10.1186/s12864-022-08426-7 · Cited by 25
“These included Senegalimassilia faecalis, a species isolated from healthy human faeces [32], RUG013 sp001486445, a species represented by a metagenomeassembled genome (MAG) from a cow rumen metagenome [33], and CAG-1427 sp000435475 and CAG-1427 sp900556585, represented by MAGs assembled from human fecal metagenomes co…”
Background
Metabolism in the Niche: a Large-Scale Genome-Based Survey Reveals Inositol Utilization To Be Widespread among Soil, Commensal, and Pathogenic Bacteria
Weber, M., & Fuchs, T. M. (2022) · Microbiology Spectrum · doi:10.1128/spectrum.02013-22 · Cited by 25
Highly accurate metagenome-assembled genomes from human gut microbiota using long-read assembly, binning, and consolidation methodsPreprint
Portik, D. M., Feng, X., Benoit, G., Nasko, D. J., Auch, B., Bryson, S., Cano, R. J., Carlin, M. R., Damerum, A., Farthing, B., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.10.593587 · Cited by 25
“Metagenome deconvolution was performed with ProxiMeta (Press et al. 2017; Stewart et al. 2018), resulting in the creation of putative genome and genome fragment clusters, as well as viral, plasmid, and AMR gene host annotations.”
Post-weaning shifts in microbiome composition and metabolism revealed by over 25 000 pig gut metagenome-assembled genomes
Gaio, D., DeMaere, M. Z., Anantanawat, K., Chapman, T. A., Djordjevic, S. P., & Darling, A. E. (2021) · Microbial Genomics · doi:10.1099/mgen.0.000501 · Cited by 27
MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data
Du, Y., & Sun, F. (2023) · Nature Communications · doi:10.1038/s41467-023-41209-6 · Cited by 24
“Plasmids present in multiple copies in genomes are often absent from MAGs retrieved by shotgun-based binning methods since such kind of methods rely on the coverage information to bin contigs 39 .”
Meta-omics based analyses of microbiome involved in biomethanation of rice straw in a thermophilic anaerobic bioreactor under optimized conditions
Pore, S. D., Engineer, A. S., Dagar, S. S., & Dhakephalkar, P. K. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2019.01.099 · Cited by 24
Expanding the rumen Prevotella collection: The description of Prevotella communis, sp. nov. of ovine origin
Grabner, E., Stare, E., Fanedl, L., Zorec, M., Jones, D. S., Johnston, C. D., Avguštin, G., & Accetto, T. (2023) · Systematic and Applied Microbiology · doi:10.1016/j.syapm.2023.126437 · Cited by 27
Disproportionate microbial responses to decadal drainage on a Siberian floodplain
Kwon, M. J., Tripathi, B. M., Goeckede, M., Shin, S. C., Myeong, N. R., Lee, Y. K., & Kim, M. (2021) · Global Change Biology · doi:10.1111/gcb.15785 · Cited by 24
“We combined two approaches for metagenome assembly as described previously (Stewart et al., 2018).”
Method
Enrofloxacin Alters Fecal Microbiota and Resistome Irrespective of Its Dose in Calves
Beyi, A. F., Goulart, D. B., Hawbecker, T., Ruddell, B., Hassall, A., Dewell, R. D., Dewell, G. A., Şahin, O., Zhang, Q., & Plummer, P. J. (2021) · Microorganisms · doi:10.3390/microorganisms9102162 · Cited by 23
“effective in identifying ARGs along with their reservoirs in high-biomass environmental samples, such as cattle feces [23,24].”
Background
Characterization of the Microbial Communities along the Gastrointestinal Tract in Crossbred Cattle
Wang, K., Zhang, H., Hu, L., Zhang, G., Lu, H., Luo, H., Zhao, S., Zhu, H., & Wang, Y. (2022) · Animals · doi:10.3390/ani12070825 · Cited by 26
“Animals 2022, 12, 825 7 of 12 At the family level, all differentially abundant taxa from Bacteroidetes except Rikenellaceae and Bacteroidaceae were biomarkers for the stomach, such as Prevotellaceae and Muribaculaceae.”
BackgroundInfluential
Functional characterization of prokaryotic dark matter: the road so far and what lies ahead
Escudeiro, P., Henry, C. S., & Dias, R. P. M. (2022) · Current Research in Microbial Sciences · doi:10.1016/j.crmicr.2022.100159 · Cited by 24
“These authors further highlight that their rumen metagenomic dataset not only offers a valuable resource for the discovery of biomass-degrading enzymes, but also that these novel enzymes might be potential candidates for application in the biofuels and biotechnology industries (Stewart et al., 2018).”
BackgroundInfluential
The hologenome ofDaphnia magnareveals possible DNA methylation and microbiome-mediated evolution of the host genome
Chaturvedi, A., Li, X., Dhandapani, V., Marshall, H., Kissane, S., Cambronero, M. C., Asole, G., Calvet, F., Ruiz‐Romero, M., Marangio, P., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad685 · Cited by 22
Effects of Age, Diet CP, NDF, EE, and Starch on the Rumen Bacteria Community and Function in Dairy Cattle
Hao, Y., Gong, Y., Huang, S., Ji, S., Wang, W., Wang, Y., Yang, H., Cao, Z., & Li, S. (2021) · Microorganisms · doi:10.3390/microorganisms9081788 · Cited by 23
“The rumen is a complex microbial ecosystem containing a great diversity of bacteria, archaea, viruses, protozoa, and fungi [1,2].”
Background
Recovery of high-qualitied Genomes from a deep-inland Salt Lake Using BASALTPreprint
Yu, K., Qiu, Z. G., Mu, R., Qiao, X., Zhang, L., Lian, C., Deng, C., Wu, Y., Xu, Z., Li, B. J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.05.434042 · Cited by 22
Design of an Epitope-Based Vaccine Ensemble for Animal Trypanosomiasis by Computational Methods
Todó, L. M., Bigey, P., Reche, P. A., Pinazo, M., Gascón, J., & Alonso-Padilla, J. (2020) · Vaccines · doi:10.3390/vaccines8010130 · Cited by 22
“In order to find out the epitopes identity (i.e., % identity over the queried sequence length) to proteins in cows, we blasted them against cow microbiome protein sequences obtained from the Cow Microbiome Project [20] and against the NCBI non redundant (nr-) collection of Bos taurus and Bos indicus proteins.”
Method
Microbiome analysis revealing microbial interactions and secondary bacterial infections in COVID‐19 patients comorbidly affected by Type 2 diabetes
Al‐Emran, H. M., Rahman, S., Hasan, M. S., Alam, R. U., Islam, O. K., Anwar, A., Jahid, I. K., & Hossain, M. A. (2022) · Journal of Medical Virology · doi:10.1002/jmv.28234 · Cited by 22
Functional Interpretation of Cross-Talking Pathways with Emphasis on Amino Acid Metabolism in Rhizosphere Microbiome of the Wild Plant Moringa oleifera
Tashkandi, M. A., Jalal, R. S., Baz, L., Refai, M. Y., Shami, A., Ashy, R. A., Abuauf, H. W., Alshehrei, F. M., Alshubaily, F. A., Barqawi, A. A., et al. (2022) · Agriculture · doi:10.3390/agriculture12111814 · Cited by 23
Biodegradation of synthetic organic pollutants: principles, progress, problems, and perspectives
Huang, Y. L., Deng, Y., Yu, K., Li, B. J., & Zhang, T. (2025) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuaf043 · Cited by 23
“Abundant studies have shown the application potential of Hi-C in enhancing the metagenome-assembled genome quality (Bickhart et al. 2022 ) and revealing the functional–phenotypic determinants (Stewart et al. 2018 ).”
Effects of exogenous α-amylases, glucoamylases, and proteases on ruminal in vitro dry matter and starch digestibility, gas production, and volatile fatty acids of mature dent corn grain
Amaro, F. X., Kim, D. H., Agarussi, M. C. N., da Silva, V. P., Fernandes, T., Arriola, K. G., Jiang, Y., Pech-Cervantes, A. A., Adesogan, A. T., Ferraretto, L. F., et al. (2020) · Translational Animal Science · doi:10.1093/tas/txaa222 · Cited by 22
Catch me if you can: capturing microbial community transformation by extracellular DNA using Hi-C sequencing
Calderón-Franco, D., van Loosdrecht, M. C. M., Abeel, T., & Weissbrodt, D. G. (2023) · Antonie van Leeuwenhoek · doi:10.1007/s10482-023-01834-z · Cited by 22
The potential for mitigation of methane emissions in ruminants through the application of metagenomics, metabolomics, and other -OMICS technologies
Asselstine, V., Lam, S., Miglior, F., Brito, L. F., Sweett, H., Guan, L. L., Waters, S. M., Plastow, G. S., & Cánovas, Á. (2021) · Journal of Animal Science · doi:10.1093/jas/skab193 · Cited by 23
Rumen and Fecal Microbiota Characteristics of Qinchuan Cattle with Divergent Residual Feed Intake
Zhou, X., Ma, Y., Yang, C., Zhao, Z., Ding, Y., Zhang, Y., Wang, P., Zhao, L., Li, C., Su, Z., et al. (2023) · Microorganisms · doi:10.3390/microorganisms11020358 · Cited by 22
“Analysis of rumen and fecal resistant gene sources revealed that resistant genes were mainly derived from Firmicutes and Bacteroidetes, genes encoding broad-spectrum carbohydrate-activating enzymes, particularly GHs and GTs, which were reported to be enriched in the Bacteroidetes genome [68], which is consistent with…”
Result
Rumen Biogeographical Regions and Microbiome Variation
Soltis, M. P., Moorey, S. E., Egert-McLean, A. M., Voy, B. H., Shepherd, E., & Myer, P. R. (2023) · Microorganisms · doi:10.3390/microorganisms11030747 · Cited by 22
“Cattle possess the capability to break down recalcitrant plant material into useable energy, which is largely due to the diverse degrading enzymes encoded by the commensal and symbiotic microbes [1,2] and the distinct structure of the forestomach [3].”
Background
Unraveling the core functional microbiota involved in metabolic network of characteristic flavor development during soy sauce fermentation
Zhang, L., Xiong, S., Du, T., Xu, Y., Zhao, X., Huang, G., Guan, Q., & Xiong, T. (2024) · Food Bioscience · doi:10.1016/j.fbio.2024.103697 · Cited by 20
Pd-promoting reduction of zinc salt to PdZn alloy catalyst for the hydrogenation of nitrothioanisole
Cheng, M. T., Zhang, X., Guo, Z., Lv, P., Xiong, R., Wang, Z., Zhou, Z. P., & Zhang, M. (2021) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2021.06.024 · Cited by 20
Recoding the metagenome: microbiome engineering in situ
Whitfill, T. M., & Oh, J. (2019) · Current Opinion in Microbiology · doi:10.1016/j.mib.2019.09.005 · Cited by 18
Disentangling the syntrophic electron transfer mechanisms of Candidatus geobacter eutrophica through electrochemical stimulation and machine learning
Yuan, H., Wang, X., Lin, T., Kim, J., & Liu, W. (2021) · Scientific Reports · doi:10.1038/s41598-021-94628-0 · Cited by 19
Dynamics of the ruminal microbial ecosystem, and inhibition of methanogenesis and propiogenesis in response to nitrate feeding to Holstein calves
Ortiz‐Chura, A., Gere, J. I., Marcoppido, G. A., Depetris, G. J., Cravero, S., Faverín, C., Pinares-Patiño, C. S., Cataldi, A. A., & Cerón‐Cucchi, M. E. (2021) · Animal nutrition · doi:10.1016/j.aninu.2021.07.005 · Cited by 21
“The genome of this bacterium was recently sequenced from the metagenome of beef cattle (Stewart et al., 2018).”
Background
A global phylogenomic and metabolic reconstruction of the large intestine bacterial community of domesticated cattle
Teseo, S., Otani, S., Brinch, C., Léroy, S., Ruiz, P., Desvaux, M., Forano, É., Aarestrup, F. M., & Sapountzis, P. (2022) · Microbiome · doi:10.1186/s40168-022-01357-1 · Cited by 19
Multi-omics revealed the effects of dietary energy levels on the rumen microbiota and metabolites in yaks under house-feeding conditions
Ma, X., La, Y., Yang, G., Dai, R., Zhang, J., Zhang, Y., Jin, J., Ma, X., Guo, X., Chu, M., et al. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1309535 · Cited by 19
Genome reduction and horizontal gene transfer in the evolution of Endomicrobia—rise and fall of an intracellular symbiosis with termite gut flagellates
Mies, U. S., Hervé, V., Kropp, T., Platt, K., Sillam‐Dussès, D., Šobotník, J., & Brune, A. (2024) · mBio · doi:10.1128/mbio.00826-24 · Cited by 19
Open prediction of polysaccharide utilisation loci (PUL) in 5414 public Bacteroidetes genomes using PULpy
Watson, M. (2018) · University of Edinburgh · doi:10.7488/ds/2438 · Cited by 21
Effect of supplementation with ruminal probiotics on growth performance, carcass characteristics, plasma metabolites, methane emissions, and the associated rumen microbiome changes in beef cattle
Pittaluga, A. M., Yang, F., Gaffney, J., Embree, M. M., & Relling, A. E. (2023) · Journal of Animal Science · doi:10.1093/jas/skac308 · Cited by 18
Exploring the microbial diversity and characterization of cellulase and hemicellulase genes in goat rumen: a metagenomic approach
Thapa, S., Zhou, S., O’Hair, J., Nasr, K. A., Ropelewski, A. J., & Li, H. (2023) · BMC Biotechnology · doi:10.1186/s12896-023-00821-6 · Cited by 18
“In 2018, Stewart et al. assembled 913 draft bacterial and archaeal Metagenome-Assembled Genomes (MAGs) from an extensive dataset of rumen metagenomic sequences obtained from 43 Scottish cattle [17].”
Background
Yeast culture enhances long-term fermentation of corn straw by ruminal microbes for volatile fatty acid production: Performance and mechanism
Liang, J., Liu, S., Zhang, R., Chang, J., Lv, L., Nabi, M., Zhang, P., & Zhang, P. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.122736 · Cited by 18
Shifts in microbial community, pathogenicity‐related genes and antibiotic resistance genes during dairy manure piled up
Zhang, X., Ma, C., Zhang, W., Li, W., Yu, J., Xue, D., Wu, X., & Deng, G. (2020) · Microbial Biotechnology · doi:10.1111/1751-7915.13551 · Cited by 18
“were assembled to construct a metagenome for each sample by SOAP denovo software with the De Bruijn graph and overlap-layout-consensus (OLC) methods (Ye and Tang, 2016; Stewart et al., 2018).”
Method
Reducing crude protein content in the diet of lactating dairy cows improved nitrogen-use-efficiency and reduced N excretion in urine, whilst having no obvious effects on the rumen microbiome
Zeleke, A. W., Dimonaco, N. J., Lawther, K., Lavery, A., Ferris, C. P., Moorby, J. M., & Huws, S. (2025) · Journal of Animal Science and Biotechnology/Journal of animal science and biotechnology · doi:10.1186/s40104-025-01240-7 · Cited by 23
“GHs represent one of the largest enzyme families that hydrolyse reactions related to the metabolism of various polysaccharides, including starch, cellulose, xylan, and chitin [97], which was consistent with our study.”
On the Role of Bioinformatics and Data Science in Industrial Microbiome Applications
van den Bogert, B., Boekhorst, J., Pirovano, W., & May, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00721 · Cited by 17
“In a recent study of cow rumen microbiome, a valuable environment for biomass-degrading enzyme discovery, Stewart et al. (2018) showed that 90% of the proteins predicted to be involved in the studied mechanism (carbohydrate metabolism) did not have a good match in public databases.”
Background
Microbial composition play the leading role in volatile fatty acid production in the fermentation of different scale of corn stover with rumen fluid
Zhang, H., Zhang, W., Cao, Q., Wang, S., Zhu, Z., & Dong, H. (2024) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1275454 · Cited by 18
“Rumen microorganisms are crucial in degradation of the lignocellulosic biomass, where different kinds of microbials synergistically participate in the hydrolysis (Stewart et al., 2018).”
Background
Genome reconstruction and haplotype phasing using chromosome conformation capture methodologies
Xu, Z., & Dixon, J. R. (2019) · Briefings in Functional Genomics · doi:10.1093/bfgp/elz026 · Cited by 18
“Subsequentstudieshaveappliedthis approach to metagenomics samples from a variety of sources, including cow rumen (93,94), mouse (12) and human (95) fecal samples and Belgian lambic style beer (96).”
Background
Pan-Genomic Insights into Rumen Microbiome-Mediated Short-Chain Fatty Acid Production and Regulation in Ruminants
Shi, J., Su, H., He, S., Dai, S., Mao, H., & Wu, D. (2025) · Microorganisms · doi:10.3390/microorganisms13061175 · Cited by 22
“High-quality data collection is the initial step, primarily relying on metagenomic sequencing data from rumen content samples and genome data from isolated and cultured microorganisms [16,24].”
Influential
Differential Effects of Natural Grazing and Feedlot Feeding on Yak Fecal Microbiota
Shah, T., Ding, L., Din, A. U., Hassan, F., Ahmad, A. A., Wei, H., Wang, X., Yan, Q., Ishaq, M., Ali, N., et al. (2022) · Frontiers in Veterinary Science · doi:10.3389/fvets.2022.791245 · Cited by 17
“Prevotellaceae is one of the dominant bacteria of the saccharolytic group in the rumen and is known for its protein binding ability and digestion of multiple carbohydrate substrates (45).”
Background
A Catalog of over 5,000 Metagenome-Assembled Microbial Genomes from the Caprinae Gut Microbiota
Zhang, X., Lv, Q., Yan, Q., Zhang, Y., Guo, R., Meng, J., Ma, H., Qin, S., Zhu, Q., Li, C.-Q., et al. (2022) · Microbiology Spectrum · doi:10.1128/spectrum.02211-22 · Cited by 16
Assembly of novel microbial genomes from gut metagenomes of rhesus macaque ( Macaca mulatta )
Yang, S., Fan, Z., Li, J., Wang, X., Yue, B., Yue, B., He, M., Zhang, A., & Li, J. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2188848 · Cited by 17
“To further compare the functional features, we predicted and annotated the genes of these MAGs.”
BackgroundInfluential
Age-related alterations in metabolome and microbiome provide insights in dietary transition in giant pandas
Liu, F., Li, R., Zhong, Y., Liu, X., Deng, W., Huang, X., Price, M., & Li, J. (2023) · mSystems · doi:10.1128/msystems.00252-23 · Cited by 17
“We found 237 different types of antibiotic resistance genes (ARGs) in all samples, with the highest ARG number (113) in the Old group and the lowest number (66) in the Cub group.”
Background
Reference gene catalog and metagenome-assembled genomes from the gut microbiome reveal the microbial composition, antibiotic resistome, and adaptability of a lignocellulose diet in the giant panda
Yang, S., Deng, W., Guo, L., Jin, L., Huang, Y., He, Y., Wu, D., Li, D., Zhang, A., Liu, C., et al. (2023) · Environmental Research · doi:10.1016/j.envres.2023.118090 · Cited by 16
Recovery of the fibrolytic microorganisms from rumen fluid by flocculation for simultaneous treatment of lignocellulosic biomass and volatile fatty acid production
Takizawa, S., Abe, K., Fukuda, Y., Feng, M., Baba, Y., Tada, C., & Nakai, Y. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.120626 · Cited by 17
CAMITAX: Taxon labels for microbial genomes
Bremges, A., Fritz, A., & McHardy, A. C. (2020) · GigaScience · doi:10.1093/gigascience/giz154 · Cited by 12
“complete genomes even from complex environments [10, 11], and easily scale to hundreds or even thousands of metagenome samples [12–16].”
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., Tan, Z., Zhu, W., & Cheng, Y. (2023) · BMC Microbiology · doi:10.1186/s12866-023-02814-z · Cited by 15
“Background Domesticated ruminants are crucial sources of animal meat and dairy products for humans, and their population would further increase with discretionary incomes and urbanization [1].”
Background
The claim of primacy of human gut Bacteroides ovatus in dietary cellobiose degradation
Li, M., Wang, Y., Guo, C., Wang, S., Zheng, L., Bu, Y., & Ding, K. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2227434 · Cited by 16
“BACOVA_02745 with lower catalytic activity was the only β-glucosidases from human gut microbiota reported so far.(45) In spite of this, we consider that the exocellulases are still believed to be necessary for microbes to effectively extract energy.”
Background
Gastrointestinal microbiome, resistance genes, and risk assessment of heavy metals in wild giant pandas
Jin, L., Wu, H., Guo, L., Yang, S., Wei, R., Huang, Y., Penttinen, P., Deng, W., Chen, J., Han, X., et al. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.165671 · Cited by 15
Red pandas with different diets and environments exhibit different gut microbial functional composition and capacity
Yunwei, L. U., Zhang, L., Liu, X., Lan, Y., Lixia, W. U., Wang, J., Wu, K., Chaojie, Y. A. N. G., Lv, R., Yi, D., et al. (2024) · Integrative Zoology · doi:10.1111/1749-4877.12813 · Cited by 15
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) · Genetics Selection Evolution · doi:10.1186/s12711-024-00887-6 · Cited by 15
“…were prepared from microbial genomic DNA and sequenced on an Illumina HiSeq Systems 4000 (samples from 283 animals from experimental years 2011, 2012, 2013, and 2014) [40, 41] or a NovaSeq (samples from 76 animals from experimental year 2017) by Edinburgh Genomics (Edinburgh, Scotland, UK).”
Method
Taxonomic and functional assessment using metatranscriptomics reveals the effect of Angus cattle on rumen microbial signatures
Neves, A. L. A., Chen, Y., Cao, K. L., Mandal, S., Sharpton, T. J., McAllister, T. A., & Guan, L. L. (2019) · animal · doi:10.1017/s1751731119002453 · Cited by 16
Species Identification in Plant-Associated Prokaryotes and Fungi Using DNA
Inderbitzin, P., Robbertse, B., & Schoch, C. L. (2020) · Phytobiomes Journal · doi:10.1094/pbiomes-12-19-0067-rvw · Cited by 15
“Prokaryote genomes are routinely reconstructed from environmental DNA (Parks et al. 2017; Stewart et al. 2018) and never before cultured microbes are isolated and grown in the lab (Cross et al. 2019; Epstein et al. 2013; Imachi et al. 2020).”
Background
From genomic signals to prediction tools: a critical feature analysis and rigorous benchmark for phage–host prediction
Shang, J., Peng, C., Guan, J., Cai, D., Wang, D., & Sun, Y. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf626 · Cited by 14
Dietary Supplementation with Sugar Beet Fructooligosaccharides and Garlic Residues Promotes Growth of Beneficial Bacteria and Increases Weight Gain in Neonatal Lambs
Quijada, N. M., Bodas, R., Lorenzo, J. M., Schmitz‐Esser, S., Rodrı́guez-Làzaro, D., & Hernández, M. (2020) · Biomolecules · doi:10.3390/biom10081179 · Cited by 14
“However, the new insights provided by this study coupled to the development of ongoing rumen-specific databases, such as the Hungate 1000 [79,80] and the metagenomic associated genomes (MAGs) extracted from rumen shotgun sequencing [81,82], may help to deepen our knowledge on ruminant-associated bacteria and to improv…”
Background
MetaPlatanus: a metagenome assembler that combines long-range sequence links and species-specific features
Kajitani, R., Noguchi, H., Gotoh, Y., Ogura, Y., Yoshimura, D., Okuno, M., Toyoda, A., Kuwahara, T., Hayashi, T., & Itoh, T. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab831 · Cited by 13
Translational multi-omics microbiome research for strategies to improve cattle production and health
Wang, Y., & Guan, L. L. (2022) · Emerging Topics in Life Sciences · doi:10.1042/etls20210257 · Cited by 16
An Age Effect of Rumen Microbiome in Dairy Buffaloes Revealed by Metagenomics
Li, L., Peng, K., Xue, M., Zhu, S., Liu, J., & Sun, H. (2022) · Microorganisms · doi:10.3390/microorganisms10081491 · Cited by 14
“Most of the identified GHs were polysaccharide-degrading enzymes such as cellulase, hemicellulose, and pectinase, all of which degrade the plant cell wall to produce oligosaccharides [78].”
Background
Metagenomics analysis reveals differences in rumen microbiota in cows with low and high milk protein percentage
Amin, A. B., Zhang, L., Zhang, J., & Mao, S. (2023) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-023-12620-2 · Cited by 16
Integrated large-scale metagenome assembly and multi-kingdom network analyses identify sex differences in the human nasal microbiome
Ju, Y., Zhang, Z., Liu, M., Lin, S., Sun, Q., Song, Z., Liang, W., Tong, X., Jie, Z., Lu, H., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03389-2 · Cited by 15
“Bins with ≥ 80% completeness and ≤ 10% contamination were retained for further analysis [121].”
MosAIC: An annotated collection of mosquito-associated bacteria with high-quality genome assemblies
Foo, A., Brettell, L. E., Nichols, H. L., Students, 2. U.-M. C. I. M., Muñoz, M. M., Lysne, J. A., Dhokiya, V., Hoque, A. F., Brackney, D. E., Caragata, E. P., et al. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002897 · Cited by 14
Dynamic changes in the gastrointestinal microbial communities of Gangba sheep and analysis of their functions in plant biomass degradation at high altitude
Liu, X., Ding, H., Zhang, X., Zhao, J., Zhao, J., Zhang, Q., Liu, H., Sun, M., & Zhang, X. (2025) · Microbiome · doi:10.1186/s40168-024-02022-5 · Cited by 15
“Ruminant gastrointestinal microbial MAGs were investigated in this study, including 519 assembled as described above, and 410 [47], 538 [48], 913 [49] and 4934 [50] MAGs previously reported were re-annotated via prokka v1.”
A homodimeric bacterial exo-β-1,3-glucanase derived from moose rumen microbiome shows a structural framework similar to yeast exo-β-1,3-glucanases
Kalyani, D. C., Reichenbach, T., Aspeborg, H., & Divne, C. (2020) · Enzyme and Microbial Technology · doi:10.1016/j.enzmictec.2020.109723 · Cited by 14
Tracking Reservoirs of Antimicrobial Resistance Genes in a Complex Microbial Community Using Metagenomic Hi-C: The Case of Bovine Digital Dermatitis
Beyi, A. F., Hassall, A., Phillips, G. J., & Plummer, P. J. (2021) · Antibiotics · doi:10.3390/antibiotics10020221 · Cited by 15
“Keywords: antimicrobial resistance genes; digital dermatitis; Hi-C ProxiMeta; resistomes; tetracycline resistance”
Influential
Unique pool of carbohydrate-degrading enzymes in novel bacteria assembled from cow and buffalo rumen metagenomes
Bohra, V., Tikariha, H., Purohit, H., & Dafale, N. A. (2022) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-022-12020-y · Cited by 11
Comparative Rumen Metagenome and CAZyme Profiles in Cattle and Buffaloes: Implications for Methane Yield and Rumen Fermentation on a Common Diet
Malik, P. K., Trivedi, S., Kolte, A. P., Mohapatra, A., Biswas, S., Bhattar, A. V. K., Bhatta, R., & Rahman, H. (2023) · Microorganisms · doi:10.3390/microorganisms12010047 · Cited by 14
“The GHs are a broad class of enzymes that are involved in the metabolism of xylan, chitin, starch, and cellulose [84].”
Multi-omics strategy reveals potential role of antimicrobial resistance and virulence factor genes responsible for Simmental diarrheic calves caused by Escherichia coli
Shi, Z., Lan, Y., Wang, Y., Yan, X., Ma, X., Hassan, F., Rushdi, H. E., Xu, Z., Wang, W., & Deng, T. (2024) · mSystems · doi:10.1128/msystems.01348-23 · Cited by 15
“Moreover, shotgun metagenomic sequencing has also been widely used for investigat ing microbial diversity in different farm animal species, including cattle (14), goats (15), sheep (16), and buffalo (17).”
Background
Noise reduction strategies in metagenomic chromosome confirmation capture to link antibiotic resistance genes to microbial hosts
McCallum, G. E., Rossiter, A. E., Quraishi, M. N., Iqbal, T. H., Kuehne, S. A., & van Schaik, W. (2023) · Microbial Genomics · doi:10.1099/mgen.0.001030 · Cited by 12
“3C/Hi- C approaches have been used to considerable effect in improving the assembly of MAGs in complex microbial ecosystems such as those present in the bovine rumen [17], the gut of dogs [18], sheep [19] and pigs [20].”
Background
Metagenomic approach to infer rumen microbiome derived traits of cattle
Manno, M. A. T., Gizzi, F. O., Martín, M., Espariz, M., Magni, C., & Blancato, V. S. (2023) · World Journal of Microbiology and Biotechnology · doi:10.1007/s11274-023-03694-1 · Cited by 15
Comparative analysis of functional diversity of rumen microbiome in bison and beef heifers
Nguyen, T. T. M., Badhan, A., Reid, I. D., Ribeiro, G. O., Gruninger, R. J., Tsang, A., Guan, L. L., & McAllister, T. A. (2023) · Applied and Environmental Microbiology · doi:10.1128/aem.01320-23 · Cited by 12
“…group in MycoCosm database (22); 410 rumen microbial genomes from Hungate1000 Project (21); 913 microbial genomes from cow rumen (24); 4,933 MAGs from 240 Scottish cattle rumen (23); 974 bacterial and 29 archaeal species from the Genomic Encyclopedia of Bacteria and Archaea…”
Background
Accessing Dietary Effects on the Rumen Microbiome: Different Sequencing Methods Tell Different Stories
Zhou, M., O’Hara, E., Tang, S., Chen, Y., Walpole, M. E., Górka, P., Penner, G. B., & Guan, L. L. (2021) · Veterinary Sciences · doi:10.3390/vetsci8070138 · Cited by 13
“Studies evaluating rumen microbial composition typically use one of two major sequencing approaches: (i) marker-based amplicon sequencing [5], or (ii) metagenomic shotgun sequencing [6,7].”
Method
Rumen multi-omics addressing diet–host–microbiome interplay in farm animals: a review
Dixit, S., Kumar, S., Sharma, R., Banakar, P. S., Singh, M., Keshri, A., & Tyagi, A. K. (2022) · Animal Biotechnology · doi:10.1080/10495398.2022.2078979 · Cited by 13
Comparative and functional analyses of fecal microbiome in Asian elephants
Li, G., Jiang, Y., Li, Q., An, D.-F., Bao, M., Lang, L., Han, L., Huang, X., & Jiang, C. (2022) · Antonie van Leeuwenhoek · doi:10.1007/s10482-022-01757-1 · Cited by 12
“They are mega-herbivores that spend 12–18 h per day feeding, and their diet is composed of highly fibrous plant material from more than 110 plant species depending on availability and season (Ishwaran 1983; Sukumar 2006).”
Background
New insights into the enteric methane production based on the archaeal genome atlas of ruminant gastrointestinal tract
Tang, Y., Liu, X., Zhu, S., Jia, M., Liu, J., & Sun, H. (2024) · Journal of Advanced Research · doi:10.1016/j.jare.2024.09.016 · Cited by 11
Understanding the transfer and persistence of antimicrobial resistance in aquaculture using a model teleost gut system
Barcan, A. S., Humble, J. L., Kasaragod, S., Sajib, M. S. I., Barcan, R. A., McGinnity, P., Welch, T. J., Robertson, B., Vamanu, E., Bacigalupo, A., et al. (2025) · Animal Microbiome · doi:10.1186/s42523-025-00377-0 · Cited by 11
“Metagenome deconvolution was performed with ProxiMeta [36, 37], resulting in the creation of putative genome and genome fragment clusters.”
Temporal changes in ruminal microbiota composition and diversity in dairy cows supplemented with a lactobacilli-based DFM
Campbell, B. E., Hassan, M. M., Moore, R. J., Olchowy, T. W. J., Soust, M., Jassim, R. A. M. A., & Alawneh, J. I. (2025) · Frontiers in Veterinary Science · doi:10.3389/fvets.2025.1584959 · Cited by 12
Discovery of novel carbohydrate degrading enzymes from soda lakes through functional metagenomics
Oumer, O. J., Simachew, A., Alexandersson, E., Johansson, E., & Gessesse, A. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.1059061 · Cited by 11
“GHs are the best-described families of carbohydrate-degrading enzymes due to their high prevalence and broad distribution across genomes (Berlemont and Martiny, 2015; Stewart et al., 2018; Wang et al., 2019).”
Background
Panel 3: Genomics, precision medicine and targeted therapies
Santos‐Cortez, R. L. P., Bhutta, M. F., Earl, J. P., Hafrén, L., Jennings, M. P., Mell, J. C., Pichichero, M. E., Ryan, A. F., Tateossian, H., & Ehrlich, G. D. (2019) · International Journal of Pediatric Otorhinolaryngology · doi:10.1016/j.ijporl.2019.109835 · Cited by 11
Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies
Muñoz‐Tamayo, R., Davoudkhani, M., Fakih, I., Robles-Rodríguez, C. E., Rubino, F., Creevey, C. J., & Forano, É. (2023) · animal · doi:10.1016/j.animal.2023.100984 · Cited by 17
Hi-C deconvolution of a textile dye–related microbiome reveals novel taxonomic landscapes and links phenotypic potential to individual genomes
Sánchez‐Reyes, A., Bretón‐Deval, L., Mangelson, H., Salinas-Peralta, I., & Sánchez‐Flores, A. (2021) · International Microbiology · doi:10.1007/s10123-021-00189-7 · Cited by 10
Recovery of metagenome-assembled genomes from the rumen and fecal microbiomes of Bos indicus beef cattle
Conteville, L. C., da Silva, J. V., Andrade, B. G. N., Coutinho, L. L., Palhares, J. C. P., & Regitano, L. C. D. A. (2024) · Scientific Data · doi:10.1038/s41597-024-04271-3 · Cited by 11
Eukfinder: a pipeline to retrieve microbial eukaryote genome sequences from metagenomic data
Zhao, D., Salas-Leiva, D. E., Williams, S. K., Dunn, K. A., Shao, J. D., & Roger, A. J. (2025) · mBio · doi:10.1128/mbio.00699-25 · Cited by 11
Effect of supplementation with L-Citrulline on rumen microbiota structure, plasma metabolites, reproductive hormones, and antioxidant capacity of Hu ewes
Liu, R., Li, P., Chen, F., Wang, S., Liu, Z., Zhao, G., & Yang, K. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1606437 · Cited by 10
“These microorganisms are essential for maintaining rumen homeostasis, promoting fiber degradation, and regulating nutrient metabolism through a dynamic balance of community structure and relative abundance (Stewart et al., 2018).”
Comparison of gastrointestinal microbiota in golden snub-nosed monkey (Rhinopithecus roxellanae), green monkey (Chlorocebus aethiops sabaeus), and ring-tailed lemur (Lemur catta) by high throughput sequencing
Zeng, Y., Pu, Y., Niu, L., Deng, J., Zeng, D., Amato, K. R., Li, Y., Zhou, Y., Lin, Y., Wang, J., et al. (2021) · Global Ecology and Conservation · doi:10.1016/j.gecco.2021.e01946 · Cited by 10
Current Progress in Optimising Sustainable Energy Recovery From Cattle Paunch Contents, a Slaughterhouse Waste Product
Dowd, B., McDonnell, D., & Tuohy, M. G. (2022) · Frontiers in Sustainable Food Systems · doi:10.3389/fsufs.2022.722424 · Cited by 10
New approach of bioprocessing towards lignin biodegradation
Liu, Y., Luo, G., Ngo, H. H., & Zhang, S. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.127730 · Cited by 10

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