FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Mol Ecol Resour / Shallow shotgun sequencing of the microbiome recapitulates 1...
Molecular ecology resources2022; 23(3); 549-564; doi: 10.1111/1755-0998.13713

Shallow shotgun sequencing of the microbiome recapitulates 16S amplicon results and provides functional insights.

Abstract: Prevailing 16S rRNA gene-amplicon methods for characterizing the bacterial microbiome of wildlife are economical, but result in coarse taxonomic classifications, are subject to primer and 16S copy number biases, and do not allow for direct estimation of microbiome functional potential. While deep shotgun metagenomic sequencing can overcome many of these limitations, it is prohibitively expensive for large sample sets. Here we evaluated the ability of shallow shotgun metagenomic sequencing to characterize taxonomic and functional patterns in the faecal microbiome of a model population of feral horses (Sable Island, Canada). Since 2007, this unmanaged population has been the subject of an individual-based, long-term ecological study. Using deep shotgun metagenomic sequencing, we determined the sequencing depth required to accurately characterize the horse microbiome. In comparing conventional vs. high-throughput shotgun metagenomic library preparation techniques, we validate the use of more cost-effective laboratory methods. Finally, we characterize similarities between 16S amplicon and shallow shotgun characterization of the microbiome, and demonstrate that the latter recapitulates biological patterns first described in a published amplicon data set. Unlike for amplicon data, we further demonstrate how shallow shotgun metagenomic data provide useful insights regarding microbiome functional potential which support previously hypothesized diet effects in this study system.
© 2022 John Wiley & Sons Ltd.
Get Full Text
Publication Date: 2022-09-26 PubMed ID: 36112078DOI: 10.1111/1755-0998.13713Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research explores the benefits and efficiency of shallow shotgun metagenomic sequencing for assessing both the taxonomic and functional aspects of the microbiome in wildlife, with feral horses from Sable Island, Canada, serving as the study model. The study shows that this method can provide accurate characterizations that mirror those of 16S amplicon, while also offering valuable insights into microbiome functional potential through affordable lab techniques.

Research Methodology and Objectives

  • The researchers aim to test the efficiency and cost-effectiveness of shallow shotgun metagenomic sequencing in characterizing taxonomic and functional patterns in the faecal microbiome of feral horses.
  • The study uses a population of feral horses on Sable Island, Canada, as the study model. These horses have been part of an individual-based, long-term ecological study since 2007.
  • By comparing conventional versus high-throughput shotgun metagenomic library preparation techniques, the researchers intend to validate the use of more affordable laboratory methods.

Study Findings

  • The study finds that shallow shotgun metagenomic sequencing can accurately characterize the horse microbiome. It also identifies the adequate sequencing depth required to achieve this characterization.
  • The cost-effective laboratory methods used in this study were proven to effectively characterize the microbiome, positioning this technique as beneficial for large sample sets for which deep shotgun metagenomic sequencing may be too costly.
  • The research discovers that the shallow shotgun characterization of the microbiome, similar to 16S amplicon, can significantly recapitulate biological patterns. This finding validates the comparability between these two methods.
  • One distinctive advantage of shallow shotgun metagenomic sequencing, as demonstrated by this study, is its ability to provide insights regarding the microbiome’s functional potential. Such information was found to verify previously hypothesized diet effects on the studied population.

Implications of the Study

  • This research adds to the current understanding of microbiome profiling techniques, suggesting that shallow shotgun metagenomic sequencing could be a viable, cost-efficient alternative for large sample sets.
  • The study exposes a new means of getting broader taxonomic and functional coverage of the microbiome through affordable laboratory methods.
  • The resultant insights into functional microbiome potential could pave the way for new research directions and applications in ecological studies.

Cite This Article

APA
Stothart MR, McLoughlin PD, Poissant J. (2022). Shallow shotgun sequencing of the microbiome recapitulates 16S amplicon results and provides functional insights. Mol Ecol Resour, 23(3), 549-564. https://doi.org/10.1111/1755-0998.13713

Publication

Molecular ecology resources
ISSN: 1755-0998
NlmUniqueID: 101465604
Country: England
Language: English
Volume: 23
Issue: 3
Pages: 549-564

Researcher Affiliations

Stothart, Mason R
  • Department of Ecosystem and Public Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada.
McLoughlin, Philip D
  • Department of Biology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
Poissant, Jocelyn
  • Department of Ecosystem and Public Health, Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada.

MeSH Terms

  • Animals
  • Horses / genetics
  • RNA, Ribosomal, 16S / genetics
  • High-Throughput Nucleotide Sequencing / methods
  • Microbiota / genetics
  • Metagenome
  • Bacteria
  • Metagenomics / methods

Grant Funding

  • D20EQ-051 / Morris Animal Foundation
  • 2016-06459 / Natural Sciences and Engineering Research Council of Canada
  • 2019-04388 / Natural Sciences and Engineering Research Council of Canada

References

This article includes 61 references
  1. Armour CR, Topçuoğlu BD, Garretto A, Schloss PD. A goldilocks principle for the gut microbiome: Taxonomic resolution matters for microbiome-based classification of colorectal cancer. MBio 13,e0316121.
    doi: 10.1128/mbio.03161-21google scholar: lookup
  2. Beghini F, McIver LJ, Blanco-Míguez A, Dubois L, Asnicar F, Maharjan S, Mailyan A, Manghi P, Scholz M, Thomas AM, Valles-Colomer M, Weingart G, Zhang Y, Zolfo M, Huttenhower C, Franzosa EA, Segata N. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with biobakery 3. eLife 10,e65088.
    doi: 10.7554/elife.65088google scholar: lookup
  3. Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30(15),2114-2120.
    doi: 10.1093/bioinformatics/btu170google scholar: lookup
  4. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. dada2: High-resolution sample inference from illumina amplicon data. Nature Methods 13(7),581-587.
    doi: 10.1038/nmeth.3869google scholar: lookup
  5. Caspi R, Billington R, Keseler IM, Kothari A, Krummenacker M, Midford PE, Ong WK, Paley S, Subhraveti P, Karp PD. The MetaCyc database of metabolic pathways and enzymes-a 2019 update. Nucleic Acids Research 48,445-453.
    doi: 10.1093/nar/gkz862google scholar: lookup
  6. Chen C, Zhou Y, Fu H, Xiong X, Fang S, Jiang H, Wu J, Yang H, Gao J, Huang L. Expanded catalog of microbial genes and metagenome-assembled genomes from the pig gut microbiome. Nature Communications 12(1),1-13.
    doi: 10.1038/s41467-021-21295-0google scholar: lookup
  7. Chiou KL, Bergey CM. Methylation-based enrichment facilitates low-cost, noninvasive genomic scale sequencing of populations from feces. Scientific Reports 8(1),1975.
    doi: 10.1038/s41598-018-20427-9google scholar: lookup
  8. Chua PYS, Crampton-Platt A, Lammers Y, Alsos IG, Boessenkool S, Bohmann K. Metagenomics: A viable tool for reconstructing herbivore diet. Molecular Ecology Resources 21(7),2249-2263.
    doi: 10.1111/1755-0998.13425google scholar: lookup
  9. Contasti AL, Tissier EJ, Johnstone JF, McLoughlin PD. Explaining spatial heterogeneity in population dynamics and genetics from spatial variation in resources for a large herbivore. PLoS One 7(10),e47858.
    doi: 10.1371/journal.pone.0047858google scholar: lookup
  10. Edwards JE, Shetty SA, van den Berg P, Burden F, van Doorn DA, Pellikaan WF, Dijkstra J, Smidt H. Multi-kingdom characterization of the core equine faecal microbiota based on multiple equine (sub)species. Animal Microbiome 2(1),6.
    doi: 10.1186/s42523-020-0023-1google scholar: lookup
  11. Gavriliuc S, Stothart MR, Henry A, Poissant J. Long-term storage of feces at −80 °C versus −20°C is negligible for 16S rRNA amplicon profiling of the equine bacterial microbiome. PeerJ 9,e10837.
    doi: 10.7717/peerj.10837google scholar: lookup
  12. Gilroy R, Leng J, Ravi A, Adriaenssens EM, Oren A, Baker D, La Ragione RM, Proudman C, Pallen MJ. Metagenomic investigation of the equine faecal microbiome reveals extensive taxonomic diversity. PeerJ 10,e13084.
    doi: 10.7717/peerj.13084google scholar: lookup
  13. Glendinning L, Genç B, Wallace RJ, Watson M. Metagenomic analysis of the cow, sheep, reindeer and red deer rumen. Scientific Reports 11(1990),1-10.
    doi: 10.1038/s41598-021-81668-9google scholar: lookup
  14. Glendinning L, Stewart RD, Pallen MJ, Watson KA, Watson M. Assembly of hundreds of novel bacterial genomes from the chicken caecum. Genome Biology 21(34),1-16.
    doi: 10.1101/699843google scholar: lookup
  15. Groisillier A, Shao Z, Michel G, Goulitquer S, Bonin P, Krahulec S, Nidetzky B, Duan D, Boyen C, Tonon T. Mannitol metabolism in brown algae involves a new phosphatase family. Journal of Experimental Botany 65(2),559-570.
    doi: 10.1093/jxb/ert405google scholar: lookup
  16. Hadfield JD. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. Journal of Statistical Software 33(2),1-22.
  17. Hillmann B, Al-Ghalith GA, Shields-Cutler RR, Zhu Q, Gohl DM, Beckman KB, Knight R, Knights D. Evaluating the information content of shallow shotgun metagenomics. MSystems 3(6),e00069-18.
    doi: 10.1128/msystems.00069-18google scholar: lookup
  18. Hillmann B, Al-Ghalith GA, Shields-Cutler RR, Zhu Q, Knight R, Knights D. SHOGUN: A modular, accurate and scalable framework for microbiome quantification. Bioinformatics 36(May),4088-4090.
    doi: 10.1093/bioinformatics/btaa277google scholar: lookup
  19. Jovel J, Patterson J, Wang W, Hotte N, O'Keefe S, Mitchel T, Perry T, Kao D, Mason AL, Madsen KL, Wong GK-S. Characterization of the gut microbiome using 16S or shotgun metagenomics. Frontiers in Microbiology 7,1-17.
    doi: 10.3389/fmicb.2016.00459google scholar: lookup
  20. Julliand V, Grimm P. The impact of diet on the hindgut microbiome. Journal of Equine Veterinary Science 52,23-28.
    doi: 10.1016/j.jevs.2017.03.002google scholar: lookup
  21. Kalbfleisch TS, Rice ES, DePriest MS, Walenz BP, Hestand MS, Vermeesch JR, O Connell BL, Fiddes IT, Vershinina AO, Saremi NF, Petersen JL, Finno CJ, Bellone RR, McCue ME, Brooks SA, Bailey E, Orlando L, Green RE, Miller DC, MacLeod JN. Improved reference genome for the domestic horse increases assembly contiguity and composition. Communications Biology 1(1),197.
    doi: 10.1038/s42003-018-0199-zgoogle scholar: lookup
  22. Kauter A, Epping L, Semmler T, Antao E-M, Kannapin D, Stoeckle SD, Gehlen H, Lübke-Becker A, Günther S, Wieler LH, Walther B. The gut microbiome of horses: Current research on equine enteral microbiota and future perspectives. Animal Microbiome 1(1),1-15.
    doi: 10.1186/s42523-019-0013-3google scholar: lookup
  23. Kohl KD. An introductory “How-to” guide for incorporating microbiome research into integrative and comparative biology. Integrative and Comparative Biology 57(4),674-681.
    doi: 10.1093/icb/icx013google scholar: lookup
  24. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nature Methods 9(4),357-359.
    doi: 10.1038/nmeth.1923google scholar: lookup
  25. Li JH, Mazur CA, Berisa T, Pickrell JK. Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays. Genome Research 31,529-537.
    doi: 10.1101/gr.266486.120google scholar: lookup
  26. Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nature Communications 11,1-11.
    doi: 10.1038/s41467-020-17041-7google scholar: lookup
  27. Mabeau S, Fleurence J. Seaweed in food products: Biochemical and nutritional aspects. Trends in Food Science & Technology 4(4),103-107.
    doi: 10.1016/0924-2244(93)90091-ngoogle scholar: lookup
  28. McMurdie PJ, Holmes S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8,1-11.
    doi: 10.1371/journal.pone.0061217google scholar: lookup
  29. Meehan CJ, Beiko RG. A phylogenomic view of ecological specialization in the Lachnospiraceae, a Family of digestive tract-associated bacteria. Genome Biology and Evolution 6(3),703-713.
    doi: 10.1093/gbe/evu050google scholar: lookup
  30. Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nature Communications 7(1),11257.
    doi: 10.1038/ncomms11257google scholar: lookup
  31. Miller AW, Dale C, Dearing MD. The induction of oxalate metabolism In vivo is more effective with functional microbial communities than with functional microbial species. MSystems 2(5),1-12.
    doi: 10.1128/msystems.00088-17google scholar: lookup
  32. Muir P, Li S, Lou S, Wang D, Spakowicz DJ, Salichos L, Zhang J, Weinstock GM, Isaacs F, Rozowsky J, Gerstein M. The real cost of sequencing: Scaling computation to keep pace with data generation. Genome Biology 17(1),1-9.
    doi: 10.1186/s13059-016-0917-0google scholar: lookup
  33. Nearing JT, Douglas GM, Hayes MG, Macdonald J, Desai DK, Allward N, Jones CMA, Wright RJ, Dhanani AS, Comeau AM, Langille MGI. Microbiome differential abundance methods produce different results across 38 datasets. Nature Communications 13(342),1-16.
    doi: 10.1038/s41467-022-28034-zgoogle scholar: lookup
  34. Neumann AP, Suen G. The phylogenomic diversity of herbivore-associated fibrobacter spp. is correlated to lignocellulose-degrading potential. MSphere 3(6),e00593-18.
    doi: 10.1128/msphere.00593-18google scholar: lookup
  35. Nielsen HB, Almeida M, Sierakowska Juncker A, Rasmussen S, Li J, Sunagawa S, Plichta DR, Gautier L, Pedersen AG, Le Chatelier E, Pelletier E, Bonde I, Nielsen T, Manichanh C, Arumugam M, Batto JM, Quintanilha Dos Santos MB, Blom N, Borruel N, Dusko Ehrlich S. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nature Biotechnology 32(8),822-828.
    doi: 10.1038/nbt.2939google scholar: lookup
  36. Oksanen J, Kindt R, Legendre P, O'Hara B, Simpson GL, Solymos PM, Simpson GL, Solymos P, Stevens MHH, Wagner H. vegan: Community Ecology Package. R Package .
  37. Ormerod KL, Wood DLA, Lachner N, Gellatly SL, Daly JN, Parsons JD, Dal'Molin CG, Palfreyman RW, Nielsen LK, Cooper MA, Morrison M, Hansbro PM, Hugenholtz P. Genomic characterization of the uncultured Bacteroidales family S24-7 inhabiting the guts of homeothermic animals. Microbiome 4(36),1-17.
    doi: 10.1186/s40168-016-0181-2google scholar: lookup
  38. Quince C, Walker AW, Simpson JT, Loman NJ, Segata N. Shotgun metagenomics, from sampling to analysis. Nature Biotechnology 35(9),833-844.
    doi: 10.1038/nbt.3935google scholar: lookup
  39. Ranjan R, Rani A, Metwally A, McGee HS, Perkins DL. Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochemical and Biophysical Research Communications 469,967-977.
    doi: 10.1016/j.bbrc.2015.12.083google scholar: lookup
  40. Raulo A, Allen B, Troitsky T, Husby A, Firth JA, Coulson T, Knowles SCL. Social networks strongly predict the gut microbiota of wild mice. ISME Journal 15,2601-2613.
  41. Raut MP, Couto N, Karunakaran E, Biggs CA, Wright PC. Deciphering the unique cellulose degradation mechanism of the ruminal bacterium Fibrobacter succinogenes S85. Scientific Reports 9,1-15.
    doi: 10.1038/s41598-019-52675-8google scholar: lookup
  42. Sanders JG, Nurk S, Salido RA, Minich J, Xu ZZ, Zhu Q, Martino C, Fedarko M, Arthur TD, Chen F, Boland BS, Humphrey GC, Brennan C, Sanders K, Gaffney J, Jepsen K, Khosroheidari M, Green C, Liyanage M, Knight R. Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads. Genome Biology 20(1),226.
    doi: 10.1186/s13059-019-1834-9google scholar: lookup
  43. Schloss PD. Amplicon sequence variants artificially split bacterial genomes into separate clusters. MSphere 6(4),1-6.
    doi: 10.1128/msphere.00191-21google scholar: lookup
  44. Srivathsan A, Ang A, Vogler AP, Meier R. Fecal metagenomics for the simultaneous assessment of diet, parasites, and population genetics of an understudied primate. Frontiers in Zoology 13(1),17.
    doi: 10.1186/s12983-016-0150-4google scholar: lookup
  45. Stanton TB, Canale-Parola E. Treponema bryantii sp. nov., a rumen spirochete that interacts with cellulolytic bacteria. Archives of Microbiology 127,145-156.
    doi: 10.1007/bf00428018google scholar: lookup
  46. Starke R, Pylro VS, Morais DK. 16S rRNA gene copy number normalization does not provide more reliable conclusions in metataxonomic surveys. Microbial Ecology 81(2),535-539.
    doi: 10.1007/s00248-020-01586-7google scholar: lookup
  47. Stewart RD, Auffret MD, Warr A, Walker AW, Roehe R, Watson M. Compendium of 4,941 rumen metagenome-assembled genomes for rumen microbiome biology and enzyme discovery. Nature Biotechnology 37(8),953-961.
    doi: 10.1038/s41587-019-0202-3google scholar: lookup
  48. Stothart MR, McLoughlin PD, Poissant J. Shallow shotgun metagenomic validation, amplicon and shotgun metagenomic sequences. NCBI SRA BioProject: PRJNA880353.
  49. Stothart MR, McLoughlin PD, Poissant J. Shallow shotgun metagenomic validation. Sable Island horses. .
    doi: 10.5061/dryad.jdfn2z3dvgoogle scholar: lookup
  50. Stothart MR, Greuel RJ, Gavriliuc S, Henry A, Wilson AJ, McLoughlin PD, Poissant J. Bacterial dispersal and drift drive microbiome diversity patterns within a population of feral hindgut fermenters. Molecular Ecology 30(2),555-571.
    doi: 10.1111/mec.15747google scholar: lookup
  51. Suzek BE, Wang Y, Huang H, McGarvey PB, Wu CH. UniRef clusters: A comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31(6),926-932.
    doi: 10.1093/bioinformatics/btu739google scholar: lookup
  52. Timonin ME, Poissant J, McLoughlin PD, Hedlin CE, Rubin JE. A survey of the antimicrobial susceptibility of Escherichia coli isolated from Sable Island horses. Canadian Journal of Microbiology 63,246-251.
    doi: 10.1139/cjm-2016-0504google scholar: lookup
  53. Tovo A, Menzel P, Krogh A, Lagomarsino MC, Suweis S. Taxonomic classification method for metagenomics based on core protein families with Core-Kaiju. Nucleic Acids Research 48(16),93.
    doi: 10.1093/nar/gkaa568google scholar: lookup
  54. Tvedte ES, Michalski J, Cheng S, Patkus RS, Tallon LJ, Sadzewicz L, Chung M, Mattick J, Sparklin BC, Hotopp JCD. Evaluation of a high-throughput, cost-effective Illumina library preparation kit. Scientific Reports 11(1),1-12.
    doi: 10.1038/s41598-021-94911-0google scholar: lookup
  55. Větrovsky T, Baldrian P. The variability of the 16S rRNA gene in bacterial genomes and Its consequences for bacterial community analyses. PLoS One 8(2),e57923.
    doi: 10.1371/journal.pone.0057923google scholar: lookup
  56. Walker AW, Martin JC, Scott P, Parkhill J, Flint HJ, Scott KP. 16S rRNA gene-based profiling of the human infant gut microbiota is strongly influenced by sample processing and PCR primer choice. Microbiome 3(1),1-11.
    doi: 10.1186/s40168-015-0087-4google scholar: lookup
  57. Waters JL, Ley RE. The human gut bacteria Christensenellaceae are widespread, heritable, and associated with health. BMC Biology 17(83),1-11.
    doi: 10.1186/s12915-019-0699-4google scholar: lookup
  58. Wilkinson T, Korir D, Ogugo M, Stewart RD, Watson M, Paxton E, Robert C. 1200 high-quality metagenome-assembled genomes from the rumen of African cattle and their relevance in the context of sub-optimal feeding. Genome Biology 21(1),1-25.
    doi: 10.1186/s13059-020-02144-7google scholar: lookup
  59. Xu W, Chen T, Pei Y, Guo H, Li Z, Yang Y, Zhang F, Yu J, Li X, Yang Y, Zhao B, Wu C. Characterization of shallow whole-metagenome shotgun sequencing as a high-accuracy and low-cost method by complicated mock microbiomes. Frontiers in Microbiology 12,678319.
    doi: 10.3389/fmicb.2021.678319/fullgoogle scholar: lookup
  60. Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, Schweer T, Peplies J, Ludwig W, Glöckner FO. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Research 42,643-648.
    doi: 10.1093/nar/gkt1209google scholar: lookup
  61. Youngblut ND, de la Cuesta-Zuluaga J, Reischer GH, Dauser S, Schuster N, Walzer C, Stalder G, Farnleitner AH, Ley R. Large-scale metagenome assembly reveals novel animal-associated microbial genomes, biosynthetic gene clusters, and other genetic diversity. MSystems 5(6),1-15.
    doi: 10.1128/msystems.01045-20google scholar: lookup

Citations

This article has been cited 14 times.
  1. Bautista J, Bedón-Galarza R, Martínez-Hidalgo F, Masache-Cruz M, Benítez-Núñez M, Valencia-Arroyo C, López-Cortés A. Decoding the microbial blueprint of pancreatic cancer. Front Med (Lausanne) 2026;13:1737582.
    doi: 10.3389/fmed.2026.1737582pubmed: 41658610google scholar: lookup
  2. Do TH, Dao TK, Pham TTN, Nguyen MH, Nguyen TQ, To LA, Nguyen TVH, Phung TBT. Understanding the bacteriome, phageome and phage-associated bacteriome in healthy Vietnamese children under two years of age. Arch Microbiol 2026 Feb 2;208(4):167.
    doi: 10.1007/s00203-026-04730-ypubmed: 41627460google scholar: lookup
  3. Lemonnier C, Alric B, Domaizon I, Rimet F. Comparison of Metabarcoding and Shotgun Sequencing Confirms the Relevance of Chloroplastic rRNA Genes to Assess Community Structure of Lake Phytoplankton. Mol Ecol Resour 2026 Jan;26(1):e70077.
    doi: 10.1111/1755-0998.70077pubmed: 41204870google scholar: lookup
  4. Liu S, Feng B, Zhang Z, Miao J, Lai X, Zhao W, Xie Q, Ye X, Cao C, Yu P, Sun J, Guo J, Wang Z, Wang Q, Zhang Z, Pan Y. UPGG: expanding the taxonomic and functional diversity of the pig gut microbiome with an enhanced genome catalog. NPJ Biofilms Microbiomes 2025 Oct 9;11(1):196.
    doi: 10.1038/s41522-025-00828-1pubmed: 41068119google scholar: lookup
  5. Sheridan PO, Meng Y, Bodington D, Coutts D, Williams TA, Gubry-Rangin C. Genomic recovery from rare terrestrial microbes enabled by DNA-based GC-fractionation. ISME Commun 2025 Jan;5(1):ycaf152.
    doi: 10.1093/ismeco/ycaf152pubmed: 41030375google scholar: lookup
  6. Graeber E, Tysha A, Nisar A, Wind D, Mendling W, Finzer P, Dilthey A. Shallow shotgun metagenomic sequencing of vaginal microbiomes with the Oxford Nanopore technology enables the reliable determination of vaginal community state types and broad community structures. BMC Microbiol 2025 Aug 25;25(1):544.
    doi: 10.1186/s12866-025-04236-5pubmed: 40855409google scholar: lookup
  7. Markey L, Qu EB, Mendall C, Finzel A, Materna A, Lieberman TD. Microbiome diversity of low biomass skin sites is captured by metagenomics but not 16S amplicon sequencing. bioRxiv 2025 Jun 24;.
    doi: 10.1101/2025.06.24.661265pubmed: 40666980google scholar: lookup
  8. Decadt H, Díaz-Muñoz C, Vermote L, Pradal I, De Vuyst L, Weckx S. Long-read metagenomics gives a more accurate insight into the microbiota of long-ripened gouda cheeses. Front Microbiol 2025;16:1543079.
    doi: 10.3389/fmicb.2025.1543079pubmed: 40196035google scholar: lookup
  9. Banerjee G, Papri SR, Huang H, Satapathy SK, Banerjee P. Deep sequencing-derived Metagenome Assembled Genomes from the gut microbiome of liver transplant patients. Sci Data 2025 Jan 9;12(1):39.
    doi: 10.1038/s41597-024-04153-8pubmed: 39788961google scholar: lookup
  10. Stothart MR, Lavergne S, McCaw L, Singh H, de Vega W, Amato K, Poissant J, Boonstra R. Population Dynamics and the Microbiome in a Wild Boreal Mammal: The Snowshoe Hare Cycle and Impacts of Diet, Season and Predation Risk. Mol Ecol 2025 Feb;34(3):e17629.
    doi: 10.1111/mec.17629pubmed: 39698753google scholar: lookup
  11. Stothart MR, McLoughlin PD, Medill SA, Greuel RJ, Wilson AJ, Poissant J. Methanogenic patterns in the gut microbiome are associated with survival in a population of feral horses. Nat Commun 2024 Jul 22;15(1):6012.
    doi: 10.1038/s41467-024-49963-xpubmed: 39039075google scholar: lookup
  12. Peng S, Ye L, Li Y, Wang F, Sun T, Wang L, Zhao J, Dong Z. Metagenomic insights into jellyfish-associated microbiome dynamics during strobilation. ISME Commun 2024 Jan;4(1):ycae036.
    doi: 10.1093/ismeco/ycae036pubmed: 38571744google scholar: lookup
  13. Chetty A, Blekhman R. Multi-omic approaches for host-microbiome data integration. Gut Microbes 2024 Jan-Dec;16(1):2297860.
    doi: 10.1080/19490976.2023.2297860pubmed: 38166610google scholar: lookup
  14. Khairunisa BH, Heryakusuma C, Ike K, Mukhopadhyay B, Susanti D. Evolving understanding of rumen methanogen ecophysiology. Front Microbiol 2023;14:1296008.
    doi: 10.3389/fmicb.2023.1296008pubmed: 38029083google scholar: lookup
Subscribe to our newsletter
Join 99,308 horse owners like you who receive our email updates on horse health and nutrition.