↓ Skip to main content

The evolutionary signal in metagenome phyletic profiles predicts many gene functions

Overview of attention for article published in Microbiome, July 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
8 news outlets
blogs
2 blogs
twitter
57 X users
facebook
2 Facebook pages
wikipedia
1 Wikipedia page

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
70 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
The evolutionary signal in metagenome phyletic profiles predicts many gene functions
Published in
Microbiome, July 2018
DOI 10.1186/s40168-018-0506-4
Pubmed ID
Authors

Vedrana Vidulin, Tomislav Šmuc, Sašo Džeroski, Fran Supek

Abstract

The function of many genes is still not known even in model organisms. An increasing availability of microbiome DNA sequencing data provides an opportunity to infer gene function in a systematic manner. We evaluated if the evolutionary signal contained in metagenome phyletic profiles (MPP) is predictive of a broad array of gene functions. The MPPs are an encoding of environmental DNA sequencing data that consists of relative abundances of gene families across metagenomes. We find that such MPPs can accurately predict 826 Gene Ontology functional categories, while drawing on human gut microbiomes, ocean metagenomes, and DNA sequences from various other engineered and natural environments. Overall, in this task, the MPPs are highly accurate, and moreover they provide coverage for a set of Gene Ontology terms largely complementary to standard phylogenetic profiles, derived from fully sequenced genomes. We also find that metagenomes approximated from taxon relative abundance obtained via 16S rRNA gene sequencing may provide surprisingly useful predictive models. Crucially, the MPPs derived from different types of environments can infer distinct, non-overlapping sets of gene functions and therefore complement each other. Consistently, simulations on > 5000 metagenomes indicate that the amount of data is not in itself critical for maximizing predictive accuracy, while the diversity of sampled environments appears to be the critical factor for obtaining robust models. In past work, metagenomics has provided invaluable insight into ecology of various habitats, into diversity of microbial life and also into human health and disease mechanisms. We propose that environmental DNA sequencing additionally constitutes a useful tool to predict biological roles of genes, yielding inferences out of reach for existing comparative genomics approaches.

X Demographics

X Demographics

The data shown below were collected from the profiles of 57 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 70 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 30%
Student > Ph. D. Student 11 16%
Student > Master 9 13%
Student > Bachelor 7 10%
Professor 4 6%
Other 10 14%
Unknown 8 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 24%
Agricultural and Biological Sciences 16 23%
Computer Science 7 10%
Immunology and Microbiology 5 7%
Medicine and Dentistry 4 6%
Other 7 10%
Unknown 14 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 104. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 December 2023.
All research outputs
#412,525
of 25,732,188 outputs
Outputs from Microbiome
#99
of 1,792 outputs
Outputs of similar age
#8,753
of 340,355 outputs
Outputs of similar age from Microbiome
#3
of 50 outputs
Altmetric has tracked 25,732,188 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,792 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 36.4. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 340,355 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.