CowPI: a rumen microbiome focussed version of the PICRUSt functional inference software
Open accessAbstract
Metataxonomic 16S rDNA based studies are a commonplace and useful tool in the research of the microbiome, but they do not provide the full investigative power of metagenomics and metatranscriptomics for revealing the functional potential of microbial communities. However, the use of metagenomic and metatranscriptomic technologies is hindered by high costs and skills barrier necessary to generate and interpret the data. To address this, a tool for Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was developed for inferring the functional potential of an observed microbiome profile, based on 16S data. This allows functional inferences to be made from metataxonomic 16S rDNA studies with little extra work or cost, but its accuracy relies on the availability of completely sequenced genomes of representative organisms from the community being investigated. The rumen microbiome is an example of a community traditionally underrepresented in genome and sequence databases, but recent efforts by projects such as the Global Rumen Census and Hungate 1000 have resulted in a wide sampling of 16S rDNA profiles and almost 500 fully sequenced microbial genomes from this environment. Using this information, we have developed "CowPI," a focused version of the PICRUSt tool provided for use by the wider scientific community in the study of the rumen microbiome. We evaluated the accuracy of CowPI and PICRUSt using two 16S datasets from the rumen microbiome: one generated from rDNA and the other from rRNA where corresponding metagenomic and metatranscriptomic data was also available. We show that the functional profiles predicted by CowPI better match estimates for both the meta-genomic and transcriptomic datasets than PICRUSt, and capture the higher degree of genetic variation and larger pangenomes of rumen organisms. Nonetheless, whilst being closer in terms of predictive power for the rumen microbiome, there were differences when compared to both the metagenomic and metatranscriptome data and so we recommend, where possible, functional inferences from 16S data should not replace metagenomic and metatranscriptomic approaches. The tool can be accessed at http://www.cowpi.org and is provided to the wider scientific community for use in the study of the rumen microbiome.
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CowPI is an improved 16S rDNA inference platform for the rumen based on PICRUSt but using Hungate1000 genomes as searchable genomes.
“Wilkinson et al. (2018), developed CowPI an improved 16S rDNA inference platform for the rumen which is based on PICRUSt but uses the Hungate1000 genomes as the searchable genomes (http://www.cowpi.org/).”
From Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future (2018) · cites this paper as Wilkinson et al., 2018
PICRUSt predictions show poor correlation with actual function seen in metagenomics/metatranscriptomic datasets for the rumen microbiome.
“The data shows poor correlation of predicted function with the actual function seen within the metagenomics/metatranscriptomic datasets (Wilkinson et al., 2018).”
From Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future (2018) · cites this paper as Wilkinson et al., 2018
PICRUSt has limitations in accurately representing microbiome function.
“Although this approach saves on the cost associated with more thorough and accurate shotgun metagenomic analysis, it has limitations in accurately represent microbiome function (Wilkinson et al., 2018).”
From Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future (2018) · cites this paper as Wilkinson et al., 2018
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- Authors
- Wilkinson, T. J., Huws, S. A., Edwards, J. E., Kingston-Smith, A. H., Siu-Ting, K., Hughes, M., Rubino, F., Friedersdorff, M., & Creevey, C. J.
- Affiliations
- Roslin Institute (GB)
- Queen's University Belfast (GB)
- Wageningen University & Research (NL)
- Aberystwyth University (GB)
- Teagasc - The Irish Agriculture and Food Development Authority (IE)
- Corresponding author
- Wilkinson, Toby J.
- ORCID
- Venue
- Frontiers in Microbiology
- Publisher
- Frontiers Media SA
- Published
- 2018-05-25
- Volume
- 9
- Pages
- 1095-1095
- ISSN
- 1664-302X (ISSN-L)
- PMC
- PMC5981159
- OpenAlex
- W2800638250
- Cited by
- 160
- References
- 44
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 37 · 2023: 24 · 2022: 26 · 2021: 19 · 2020: 22 · 2019: 13 · 2018: 6 · 2017: 1
- FWCI
- 5.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Frontiers in Microbiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Arrow@dit (Dublin Institute of Technology) (acceptedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion, cc-by)
- T-Stór (Teagasc) (acceptedVersion, cc-by-nc-sa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Research Portal (Queen's University Belfast) (submittedVersion, cc-by)
- Arrow@dit (Dublin Institute of Technology) (submittedVersion, cc-by-nc-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- T-Stór (Teagasc) (submittedVersion, cc-by-nc-sa)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- Funders
- Biotechnology and Biological Sciences Research Council (GCRF, BB/E/W/10964A01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Ruminant Nutrition and Digestive Physiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Keywords
- PICRUSt, rumen microbiome, functional metagenomics, 16S rRNA gene, metatranscriptomics, metagenomics, functional prediction, pan-genome, ribosomal RNA, ribosomal DNA
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