Evidence map›Paper›PMID 26317871›Full record

ArticlePLoS computational biology2015

Functional Basis of Microorganism Classification.

Chengsheng Zhu, Tom O Delmont, Timothy M Vogel, Yana Bromberg

Abstract read
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Discovery of novel treponemes associated with pododermatitis in elk (Applied and environmental microbiology · 2024
    Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Genomic Analysis ofInternational journal of genomics · 2020
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. COMPUTATIONAL APPROACHES TO STUDY MICROBES AND MICROBIOMES.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016
    Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Chengsheng ZhuDepartment of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey, United States of America.
Tom O DelmontEnvironmental Microbial Genomics, Laboratoire Ampere, École Centrale de Lyon, Université de Lyon, Ecully, France.
Timothy M VogelEnvironmental Microbial Genomics, Laboratoire Ampere, École Centrale de Lyon, Université de Lyon, Ecully, France.
Yana BrombergDepartment of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey, United States of America; Institute for Advanced Study, Technische Universität München, Garching, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with phylogenetic descent.

Indexed as

SoftwareBacteriaClassificationComputational BiologyGenome, Bacterial

Identifiers

PMID26317871
PMCPMC4552647

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.